MCA syllabus2 1 Syllabus Mumbai University

Page 1

Page 2

Page 3

Copy to : -
1. The Deputy Registrar, Academic Authorities Meetings and Services
(AAMS),
2. The Deputy Registrar, College Affiliations & Development
Department (CAD),
3. The Deputy Registrar, (Admissions, Enrolment, Eligibility and
Migration Department (AEM),
4. The Deputy Registrar, Research Administration & Promotion Cell
(RAPC),
5. The Deputy Registrar, Executive Authorities Section (EA),
6. The Deputy Registrar, PRO, Fort, (Publication Section),
7. The Deputy Registrar, (Special Cell),
8. The Deputy Registrar, Fort/ Vidyanagari Administration Department
(FAD) (VAD), Record Section,
9. The Director, Institute of Distance an d Open Learning (IDOL Admin),
Vidyanagari,
They are requested to treat this as action taken report on the concerned
resolution adopted by the Academic Council referred to in the above circular
and that on separate Action Taken Report will be sent in this connection.

1. P.A to Hon’ble Vice -Chancellor ,
2. P.A Pro -Vice-Chancellor,
3. P.A to Registrar,
4. All Deans of all Faculties,
5. P.A to Finance & Account Officers, (F.& A.O),
6. P.A to Director, Board of Examinations and Evaluation,
7. P.A to Director, Innovation, Incubation and Linkages,
8. P.A to Director, Board of L ifelong Learning and Extension (BLLE),
9. The Director, Dept. of Information and Communication Technology
(DICT) (CCF & UCC), Vidyanagari,
10. The Director of Board of Student Development,
11. The Director, Department of Students Walfare (DSD),
12. All Deputy Registrar, Examination House,
13. The Deputy Registrars, Finance & Accounts Section,
14. The Assistant Registrar, Administrative sub -Campus Thane,
15. The Assistant Registrar, School of Engg. & Applied Sciences, Kalyan,
16. The Assistant Registrar, Ratnagi ri sub -centre, Ratnagiri,
17. The Assistant Registrar, Constituent Colleges Unit,
18. BUCTU,
19. The Receptionist,
20. The Telephone Operator,
21. The Secretary MUASA

for information.

Page 4

AC 23/7/2020
Item No. 4.134

UNIVERSITY OF MUMBAI



Master of Computer Applications
MCA

First Year with Effect from AY 2020 -21
Second Year with Effect from AY 2021 -22


(New Scheme - 2020) from Academic Year 2020 – 21
Under
FACULTY OF SCIENCE & TECHNOLOGY

(As per AICTE guidelines with ef fect from the
Academic Year 2020 –2021 )

Page 5

Page 6

Preamble
To meet the challenge of ensuring excellence in Master Program in Computer Applications
(MCA : referred as Master of Computer Applications) education, the issue of quality needs to be
addressed, debated and taken forward in a systematic manner. Accreditation is the principal means
of quality assurance in higher educatio n. The major emphasis of accreditation process is to measure
the outcomes of the program that is being accredited. In line with this Faculty of Science and
Technology of University of Mumbai has taken a lead in incorporating philosophy of outcome
based edu cation in the process of curriculum development.
Faculty of Science Technology, University of Mumbai, in one of its meeting unanimously resolved
that, each Board of Studies shall prepare some Program Educational Objectives (PEOs) and give
freedom to affil iated Institutes to add few (PEO s), course objectives and course outcomes to be
clearly defined for each course, so that all faculty members in affiliated institutes understand the
depth and approach of course to be taught, which will enhance learner’ s lea rning process. It was
also resolved that, maximum senior faculty from colleges and experts from industry to be involved
while revising the curriculum. We are happy to state that, each Board of studies has adhered to the
resolutions passed by Faculty of Technology, and developed curriculum accordingly. In addition to
outcome based education, semester based credit and grading system is also introduced to ensure
quality of Master of Comput er Applications (MCA) education.
Semester based Credit and Grading syste m enables a much required shift in focus from teacher
centric to learner centric education since the workload estimated is based on the investment of time
in learning and not in teaching. It also focuses on continuous evaluation which will enhance the
quality of education. University of Mumbai has taken a lead in implementing the system through its
affiliated Institutes and Faculty of Technology has devised a transparent credit assignment policy
and adopted ten points scale to grade learner’s performance. The overall credits and approach of
curriculum proposed in the present revision is in line with AICTE guidelines .
The present curriculum will be implemented for First Year of Master of Computer Applications
(MCA) from the academic year 2020 -2021 . Subsequently this system will be carried forward for
Second Year of MC A in the academic year 2021 -2022 .

Dr. S. K. Ukarande Dr. Anuradha Muj umdar
Associate Dean Dean
Faculty of Scien ce and Technology Faculty of Science and Technology
University of Mumbai University of Mumbai

Page 7

Incorporation of Massive Open Online Courses (MOOC) &
Institutional Social Responsibility ( ISR) Activities

The new curriculum is mainly focused on knowledge component, skill based activities and project
based activities. Self-learning opportunities are provided to the learners. In the earlier revision of
curriculum, efforts were made to use online contents mor e appropriately as additional learning
materials to enhance learning of the students.
In this ne w syllabus based on AICTE guidelines overall credits are reduced to provide opportunity
of self-learning to the learner. The l earners are now getting sufficien t time for self-learning either
through online courses or additional projects for enhancing their knowledge and skill sets.
Massive Open Online Courses (MOOC) are free online courses available for anyone to enroll.
MOOC provide an affordable and fl exible way to learn new skills and deliver quality educational
experiences at scale. The Principals /Directors / HoD’s/ Faculties of all the institute are required to
motivate and encourage learners to use additional online resources available on platforms such as
NPTEL/ Swayam. Learners can be advised to take up online courses, on successful completion they
are required to submit certification for the same. This will definitely help learners to facilitate their
enhanced learning based on their interest.
Institutio nal Social Responsibility (ISR) may be slightly impractical, especially in the modern
competitive world, where everyone works for self-interest , but it will succeed if we take decisions
based on what will benefit a large number of people and respect everyone‘s fundamental rights. As
individuals we can make our small contributions to society by doing social activities, individual or
in association with Institute/Social organizations/NGOs/Clubs etc. To create awareness among
students towards institution al & individual social responsibility for societal development ISR
activities are incorporated in new MCA syllabus.

Dr. S. K. Ukarande Dr. Anuradha Muj umdar
Associate Dean Dean
Faculty of Scien ce and Technology Faculty of Science and Technology
University of Mumbai University of Mumbai

Page 8

Preface By BoS
As AICTE declared Master of Computer Application a two-year program from academic year 2020 -
2021, it was a challenge to design the curriculum for two years program keeping the employability
intact. I t is a privilege to present the new syllabus of Mas ter of Computer Applications (MCA )
which will be in effect from academic y ear 2020 -2021 .

The basic objective of the syllabus is to equip the students with the strong foundation, necessary
skills and latest tools and technology required for making the impression globally . The syllabus is
designed keeping in view the requirements of the IT Industry so the i nclusion of outcome based
approach and project based learning. Since the M.C. A. progra m is inclined more towards
Application Development and thus has more emphasis on latest technology and tools to develop
better and faster applications using integrated approach.
The earlier revised curriculum was more focused on providing information and knowledge across
various domains, which led to heavily loading of students in terms of direct contact hours. In this
regard, MCA Ad -hoc Board of Studies under the faculty of science and technology re solved to
minimize the burden of contact hours, resulting total cred its of entire program to 88, wherein focus
is not only on providing knowledge but also on building skills, attitude and self-learning on higher
cognitive levels . Therefore in the present curriculum self-learning topics, skill bas ed laboratories
and mini projects are incorporated every semester , Semester IV offers MOOC course to enable self -
learning. Bridge course is designed additionally as a prerequisite for graduates coming from non
IT/CS background. Inclusion of Institute Social Responsibility is another flavor of the curriculum,
aimed to inculcate social awareness, values and environmentally responsible behavior among the
learners . Curriculum offers the courses from Emerging Areas like Artificial Intelligence, Machine
Learning, Data Science, Quantum Computing, IoT, Block chain. Soft skill lab will enable the
learners to get proficiency in soft skills, the f lexibility in course allows to deliver the content as per
the need of each batch of learners . Flexible continuous assessment helps academicians for academic
experiments to improve learning experience. These skills further enable the students to take a full,
active and responsible ro le in the IT enabled industries. The course structures are carefully
designed so that students get superiority in dealing with diverse situations when they step into the
corporate world.
We would like to extend our thanks to Dean Dr. Anuradha Muzumdar , Associate Dean Dr. Suresh
Ukarande , Board of Studies members, Directors /Principals, HODs for valuable inputs to strength en
the scope and contents of the syllabus. We would also like to extend our thanks to all M.C.A.
Faculty members , Industry experts and alumni for their contribution in designing an outcome based
curriculum for 2 years MCA program .


Ad-hoc Board of Studies, MCA, University of Mumbai
Dr. Pooja Raundale : Chairman
Dr. Kavi Arya : Member
Dr. Murlidhar Dhanawade : Member
Dr. Suhasini Vij aykumar : Member
Dr. Jyoti Kharade : Member

Page 9

Structure for Student Induction Program
New students enter an institution with diverse thoughts, back grounds and preparations. It is
important to help them adjust to the new environment and inculcate in them the ethos of the
institution with a sense of larger purpose.
The Induction Program is designed to make the newly joined students feel comfortable, se nsitize
them towards exploring their academic interests and activities, reducing competition and making
them work for excellence, promote bonding within them, build relations between teachers and
students, give a broader view of life, and building of chara cter.
Its purpose is to make the students feel comfortable in their new environment, open them up, set a
healthy daily routine, create bonding in the batch as well as between faculty and students, develop
awareness, sensitivity and understanding of the sel f, people around them, society at large, and
nature.
New students be informed that the Induction is mandatory non -credit course for which a certificate
will be issued by the institution. At the start of the induction, the incumbents learn about the
institu tional policies, processes, practices, culture and values, and their mentor groups are formed.
The different activities are:
1. Orientation : In the first session of Induction program learners and parents to be oriented about
institute policies, processes, practices, culture and values. In addition to this, learners will be
educated for 1st year academic program information in terms of academic calendar, Assessment
plan, grading information, university ordinances, rules and regulations related to academics.
2. Mentoring : Mentoring and connecting the students with faculty members is the most important
part of student induction. Mentoring process shall be carried out in small groups . For each groups
one faculty mentor to be allocated, who will remain the mentor till those students post graduates
from the institute. In the second session of Induction program, groups for mentoring to be formed
and student mentors and faculty mentors to be introduced to newly inducted students. Introduction
of mentoring system to b e given to new students. Minimum one meeting to be conducted every
month during semesters with students group by faculty mentors. For record keeping appropriate
formats to be developed and information to be updated regularly by faculty mentors.
3. Universa l Human Values : Universal Human Values gets the student to explore oneself and
experience the joy of learning, prepares one to stand up to peer pressure and take decisions with
courage, be aware of relationships and be sensitive to others, understand the r ole of money in life
and experience the feeling of prosperity. Need for character building has been underlined by many

Page 10

thinkers, universal human values provide the base. Methodology of teaching this content is
extremely important. It must not be through do„s and dont„s, but by getting the students to explore
and think by engaging them in a dialogue. It is best taught through group discussions and real life
activities rather than lecturing. The role of group discussions, however, with clarity of thought of
the teachers cannot be over emphasized. It is essential for giving exposure, guiding thoughts, and
realizing values.
4. Proficiency Modules : The induction program period can be used to overcome some critical
lacunas that students might have, for example, En glish, Mathematics, computer familiarity etc.
These should run like crash courses, so that when normal courses start after the induction program,
the student has overcome the lacunas substantially. A diagnostic test should be conducted on Day 2
itself. Bef ore the test, the students should be informed that the test would not affect their grades or
any aspect of their admission, placement, study, etc. Purpose of the test is to provide help to those
students who need help in English, Mathematics, Computer prof iciency etc. Students having more
than 80% marks in their qualifying examination in respective subjects need not take the diagnostic
test. For those below this cut -off, writing the test is mandatory. Students with weak performance in
the test, must attend a non -credit course in Basic English, Basic Mathematics, and Basic Computer
Operation etc. Their attending the course is mandatory. There would be no separate fee payable for
the course. The classes of Basic courses must start from Day 4 at the latest. Stu dents those who are
excluded from basic courses, for them some activity in the domain of creative arts, cultural and
literature to be organised.
5. Physical Activity : Fitness session, yoga classes, lecture(s) on facing world with sportsman spirit,
making young students aware that there is nothing like being failure in the world. The world gives
opportunities to all. The incoming students must be divided into batches of 50 students maximum,
and a qualified coach in physical education/ faculty member should b e attached to each batch. The
list of available games, sport, or physical activities should be announced in orientation program on
Day 1. They should be asked to fill their choice with three preferences, and the game or sport be
allotted to them as per the ir preference. The physical activity should start from Day 3 onwards,
wherein the student learns and plays his assigned game during the induction program. It is also
important that along with his assigned game the student also practices yoga.
6. Creative A rts, Cultural and Literary Activity : Qualified instructors for arts may be hired on
contract basis and be paid honorarium as per norms of the institute. Daily 90 to 120 minute sessions
may be arranged. The list of available art forms, such as vocal music, instrumental music, folk
music, painting, sketching, dance, group dance, clay modeling, pottery, dramatics, etc. should be
announced. They should be asked to fill their choice with three preferences, and the art form be

Page 11

allotted to them as per their prefer ence. There should be sufficient number of teachers for each art
form. The ratio may be kept as 1 teacher for every 25 students. A faculty member interested in
literary activity should be assigned for organizing the activity. A list of books which are inte resting
and educational should be prepared beforehand. Books in Indian languages must be included and
even given priority. Students are losing connection with languages in general and their own
language, in particular. Students should be assigned a book or other smaller reading material. They
should be asked to read and write a critical summary. They should present their summary in front of
their group. A literary group may consist of around 30 -40 students. Similarly, debating and public
speaking activity c ould also be undertaken. If the college can arrange for a drama workshop where a
group of students learn and enact a play it would be very good. Not all the incoming students would
do this, but those who wish may be provided the opportunity. Help may be ta ken from senior
students engaged in such extra -curricular activities in the college.
7. Familiarisation with Institute and Department : The students admitted visit their department .
The Head of the department and other associated faculty should address the new student„s right on
Day 2 or so. Arrangements should be made about the meeting/gathering. The parents of the students
should also be welcomed if they accompany their ward. It would be helpful if an alumnus of the
Dept. relates his professional experienc e related to the field of the study to the incoming students.
8. Lectures /Workshops by Eminent People : Eminent people from all walks of life may be invited
to deliver lectures, namely, from industry, academia, social science (authors, historians), social
work, civil society, alumni etc. be identified and invited to come and address the new students.
Motivational lectures about life, meditation, etc. by Ramakrishna Mission, Art of Living, S -VYASA
university, Vivekanand Kendras, etc. may be organized. Worksh ops which rejuvenate or bring relief
to students
would also be welcome, such as, Art of Living workshops.
9. Extra -Curricular Activity : Every college has extra -curricular activities. Most of them are
student
driven. They are organized by student councils a nd clubs. The extra -curricular activities going on in
the college should be presented to the new students under the guidance of faculty advisors for such
activity. The new students should be informed about how they can join the activities. Related
faciliti es should be described to them. Presentation on the activities by the student council should be
made.
10. Feedback and Report on the Program : A formal feedback at the end of the program should be

Page 12

collected from students by their filling a form in writing o r online. Besides the above, each group
(of 20 students) should write a report on the Induction Program towards the end of the semester.
They would also have to make a presentation of their report. They should be encouraged to use
slides while making a presentation. Presentation of the report should be made in the language they
are comfortable with, without any insistence that it should be in English. It is more important that
they feel comfortable and confident. Each group may make the presentation through 4-5 of its group
members or more. In each session, their faculty mentors and student guides, if any, should also be in
the audience. These sessions would tell you how well the program ran, and what the students are
feeling at the end of the program. This would also serve as a grand closure to the program. A
certificate shall be awarded to all the students, upon successful completion of the induction program
based on their report and presentation.
Tentative schedule of 1st Week Induction Program:
Day 1
Session 1 Orientation program
Session 2 Mentoring (group formation and introduction)
Day 2
Session 3 Diagnostic test (basic English, Mathematics and Computer O peration)
Session 4 Familiarisation of Department and Institute (Visits to department, Laboratory,
Library, Examination cell, O ffice etc .)
Day 3
Session 5 Physi cal Activity ( Yoga, S ports etc .)
Session 6 Universal human values session
Day 4
Session 7 Proficiency Modules (Short courses on basic Mathematics, English and C omputer
Operation etc. for identified students)
Sessi on 8 Physical Activity ( Yoga, S ports etc)
Day 5
Session 9 Profici ency Modules (Short courses on B asic Mathematic s, English and Computer
Operation etc. for identified students)
Session 10 Cre ative Arts, Cultural and Literary Activity

Page 13

Program Structure for
First Year Master of Computer Applications
UNIVERSITY OF MUMBAI (With Effect from 2020 -2021)
Semester I
Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Pract. Tut. Theor
y Pract. Tut. Total
MCA11 Mathematical
Foundation for
Computer Science 1 3 -- 1 3 -- 1 4
MCA12 Advanced Java 3 -- -- 3 -- 3
MCA13 Advanced Database
Management System 3 -- -- 3 -- -- 3
MCA14 Software Project
Management 3 -- 1 3 -- 1 4
MCAL11 Data Structure Lab
using C and / C++ -- 4 -- -- 2 -- 2
MCAL12 Advanced Java LAB -- 2 -- -- 1 -- 1
MCAL13 Advanced Database
Management System
LAB -- 2 -- -- 1 -- 1
MCAL14 Web Technologies -- 4 -- -- 2 -- 2
MCAP11 Mini Project – 1 A -- 2 -- -- 1 -- 1
Total 12 14 2 12 07 2 21
Course
Code Course Name Examination Scheme
Theory Term
Work Pract
& oral Total
Internal Assessment End
Sem.
Exam Exam.
Duration
(in Hrs)
CA Test Avg
MCA11 Mathematical
Foundation for
Computer Science 1 20 20 20 80 3 25 -- 125
MCA12 Advanced Java 20 20 20 80 3 -- -- 100
MCA13 Advanced Database
Management System 20 20 20 80 3 -- -- 100
MCA14 Software Project
Management 20 20 20 80 3 25 -- 125
MCAL11 Data Structure Lab
using C and / C++ -- -- -- -- -- 50 50 100
MCAL12 Advanced Java LAB -- -- -- -- -- 25 50 75
MCAL13 Advanced Database
Management System
LAB -- -- -- -- -- 25 50 75
MCAL14 Web Technologies -- -- -- -- -- 50 50 100
MCAP11 Mini Project – 1 A -- -- -- -- -- 50 -- 50
Total -- -- 80 320 -- 250 200 850

Page 14

Program Structure for
First Year Master of Computer Applications
UNIVERSITY OF MUMBAI (With Effect from 2020 -2021)
Semester II
Teaching Scheme

Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assig ned
Theory Pract. Tut. Theory Pract. Tut. Total
MCA21 Mathematical
Foundation for
Computer Science 2 3 -- 1 3 -- 1 4
MCA22 Artificial Intelligence
and Machine Learning 3 -- -- 3 -- -- 3
MCA23 Information Security 3 -- -- 3 -- -- 3
MCAE24 Elective - 1 3 -- -- 3 -- -- 3
MCAE25 Elective - 2 3 -- 1 3 -- 1 4
MCAL21 Artificial Intelligence
and Machine Learning
Lab -- 2 -- -- 1 -- 1
MCAL22 Soft Skill
Development Lab -- 2 -- -- 1 -- 1
MCALE23 Elective 1 Lab -- 2 -- -- 1 -- 1
MCAL24 Skill based Lab
Course
AWT Lab -- 4 -- -- 2 -- 2
MCAL25 Skill based Lab
Course
User Interface Lab -- 2 -- -- 1 -- 1
MCAL26 Skill based Lab
Course
Networking with
Linux Lab -- 2 -- -- 1 -- 1
MCAP21 Mini Project 1 -B -- 2 -- -- 1 -- 1
Total 15 16 2 15 8 2 25

Page 15

Program Structure for
First Year Master of Computer Applications
UNIVERSITY OF MUMBAI (With Effect from 2020 -2021)
Semester II
Examination Scheme

Course
Code Course Name Examination Scheme
Theory Term
Work Pract
& oral Total
Internal Assessment End
Sem.
Exam. Exam.
Duration
(in Hrs)
CA Test Avg.
MCA21 Mathematical
Foundation for
Computer Science 2 20 20 20 80 3 25 -- 125
MCA22 Artificial Intelligence
and Machine Learning 20 20 20 80 3 -- -- 100
MCA23 Information Security 20 20 20 80 3 -- -- 100
MCAE24 Elective - 1 20 20 20 80 3 -- -- 100
MCAE25 Elective – 2 20 20 20 80 3 25 -- 125
MCAL21 Artificial Intelligence
and Machine Learning
Lab -- -- -- -- -- 25 50 75
MCAL22 Soft Skill
Development Lab -- -- -- -- -- 50 -- 50
MCALE23 Elective 1 Lab -- -- -- -- -- 25 50 75
MCAL24 Skill based Lab
Course
AWT Lab -- -- -- -- -- 50 50 100
MCAL25 Skill based Lab
Course
User Interface Lab -- -- -- -- -- 25 50 75
MCAL26 Skill based Lab
Course
Networkin g with
Linux Lab -- -- -- -- -- 25 50 75
MCAP21 Mini Project 1 -B -- -- -- -- -- 50 -- 50
Total -- -- 100 400 -- 300 250 1050

Page 16

Elective 1

Sr. No. Course Code Course Name Lab Course Code
1 MCAE241 Image Processing MCALE231
2 MCAE242 Internet Of Things MCALE232
3 MCAE243 Robotic Process Automation MCALE233
4 MCAE244 Computer Vision MCALE234
5 MCAE245 Embedded Systems MCALE235


Elective 2

Sr. No. Course Code Course Name
1 MCAE251 Natural Language Processing
2 MCAE252 Geogra phic Information System
3 MCAE253 Design and Analysis of Algorithm
4 MCAE254 Digital Marketing and Business Analytics
5 MCAE255 Research Methodology

Page 17

Program Structure for
Second Year Master of Computer Applicatio ns
UNIVERSITY OF MUMBAI (With Effect from 2021 -2022)
Semester III
Teaching Scheme

Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Pract. Tutoria
l Theory Pract. Tutorial Total
MCA31 Big Data Analytics
and Visualization 3 -- -- 3 -- -- 3
MCA32 Distributed System
and Cloud
Computing 3 -- -- 3 -- -- 3
MCAE33 Elective - 3 3 -- -- 3 -- -- 3
MCAE34 Elective - 4 3 -- 1 3 -- 1 4
MCAL31 Big Data Analytics
and Visualization
Lab -- 2 -- -- 1 -- 1
MCAL32 Distributed System
and Cloud
Computing Lab -- 2 -- -- 1 -- 1
MCALE33 Elective 3 Lab -- 2 -- -- 1 -- 1
MCAL34 Skill based Lab
Mobile Computing
Lab -- 4 -- -- 2 -- 2
MCAL35 Software Testing
Quality Assurance
Lab -- 2 -- -- 1 -- 1
MCAP31 Mini Project: 2 A -- 2 -- -- 1 -- 1
Total 12 14 1 12 07 1 20

Page 18

Program Structure for
Second Year Master of Computer Applications
UNIVERSITY OF MUMBAI (With Effect from 2021 -2022)
Semester III
Examination Scheme


Course
Code Course Name
Examination Scheme

Theory Term
Work Pract &
oral
Total
Internal
Assessment End Sem
Exam
Exam.
Duration
(in Hrs)
CA Test Avg
MCA31 Big Data
Analytics and
Visualization 20 20 20 80 3 -- -- 100
MCA32 Distributed
System and Cloud
Computing 20 20 20 80 3 -- -- 100
MCAE33 Elective - 3 20 20 20 80 3 -- -- 100
MCAE34 Elective - 4 20 20 20 80 3 25 -- 125
MCAL31 Big Data
Analytics and
Visualization Lab -- -- -- -- -- 25 50 75
MCAL32 Distributed
System and Cloud
Computing Lab -- -- -- -- -- 25 50 75
MCALE33 Elective 3 Lab -- -- -- -- -- 25 50 75
MCAL34 Skill based Lab
Mobile
Computing Lab -- -- -- -- -- 50 50 100
MCAL35 Software Testing
Quality Assurance
Lab -- -- -- -- -- 25 50 75
MCAP31 Mini Project: 2 A -- -- -- --
-- 50 - 50
Total -- -- 80 320
-- 225 250 875

Page 19

Elective 3


Sr. No. Course Code Course Name Lab Course Code
1 MCAE331 Blockchain MCALE331
2 MCAE332 Deep Learning MCALE332
3 MCAE333 Game Development MCALE333
4 MCAE334 Ethical Hacking MCALE334
5 MCAE33 5 Quantum Computing MCALE335


Elective 4

Sr. No. Course Code Course Name
1 MCAE341 Intellectual Property Rights
2 MCAE342 Green Computing
3 MCAE343 Management Information System
4 MCAE344 Cyber Security and Digital Forensics
5 MCAE345 Entrepr eneurship Management

Page 20

Program Structure for
Second Year Master of Computer Applications
UNIVERSITY OF MUMBAI (With Effect from 2021 -2022)
Semester IV
Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Pract. Theory Pract. Total
MCAI41 Internship -- 40 -- 15 15
MCAR42 Research Paper 1 -- 1 -- 1
MCAM43 Online Course - (MOOC) 4# -- 4 -- 4!
MCAS44 Institute Social Responsibility* -- -- -- -- 2*
Total 5 40 5 15 20+2*
Course
Code Course Name Examin ation Scheme
Internal Assessment University
Assessment

Total Mid term
Presentat
ion I Mid term
Presentation
II Final
Presentation
MCAI41 Internship 25 25 200 250
MCAR42 Research Paper 25 25 -- 50
Total 50 50 200 300

# Work load only for stude nts
! Credits transferred from MOOC courses
* Credits allotted in sem ester IV based on the (ISR) work done in sem esters I II III

Note: Internal assessment of Tutorials to be done separately and term work marks to be given out of
25 for those courses where tutorial is mentioned.

Page 21

Program Structure for Bridge Course
(With Effect from 2020 -2021)
For t he graduates, not having graduation in Computer Science/Information Technology/
Computer Application , need to complete the bridge course in first year of MCA along with
the semester I and II of MCA
Course
Code Course Name Group Teaching Scheme
Credits Assigned
(Contact Hours)
Theory Pract. Tut. Theory Pract. Tut Total
MCABR1 Programming
with C++ ICT 3 -- -- -- -- -- --
MCABR2 Data Structures ICT 3 -- -- -- -- -- --
MCABR3 Operating
Systems ICT 3 -- -- -- -- -- --
MCABR4 Computer
Networks ICT 3 -- -- -- -- -- --
MCABR5 Discrete
Mathematics M 3 -- -- -- -- -- --
Total 15
-- -- -- -- --

Course
Code Course Name Group Examin ation Scheme
Theory Pract
Internal Assessment End
Sem. Exam.
Duratio
n Pract Oral Tot
al
CA Test Avg.
In Hrs

MCABR1 Programming
with C++ ICT 20 20 20 80 3 -- -- 100
MCABR2 Data Structures ICT 20 20 20 80 3 -- -- 100
MCABR3 Operating
Systems ICT 20 20 20 80 3 -- -- 100
MCABR4 Computer
Networks ICT 20 20 20 80 3 -- -- 100
MCABR5 Discrete
Mathematics M 20 20 20 80 3 -- -- 100
Total 500

Page 22

Semester I
Course Course
Name Teaching Scheme Credits Assigned
MCA11
Mathemat ic
al
Foundation
for
Computer
Science 1 Contact Hours
Theory Tutorial Theory Tutorial Total
3 1 3 1 4
Examination Scheme
Theory Term
Work End Sem
Exam Total CA Test AVG
20 20 20 25 80 125
Pre-requisite: Student must know
 Measures o f central tendency and dispersion
 Set theory
 Basic principles of counting
Course Objectives: Learner /Student will learn and perform
Sr.No. Course Objective
1  Statistical measures on various types of data
2 Correlation and regression techniques for esti mation
3 Probability aspects to take proper decision
4 Application of discrete and continuous probability distributions
5 Various methods of hypothesis testing
Course Outcomes: On successful completion of course learner/student will be able to
Sr.No. Outcome Bloom
Level
CO 1 Apply different statistical measures on various types of data Applying
CO 2 Evaluate using regression analysis. Evaluat ing
CO 3 Analyze different types of Probability and their fundamental
applications and random variable. Anal yzing
CO 4 Apply probability distribution to real world problems Applying
CO 5 Formulate and test the hypothesis for business problem using
various methods Creat ing

Page 23

Module Detailed Contents Hrs
01 Module: Skewness
Karl Pearson‟s coefficient of skew ness, Bowley's coefficient of skewness.
Self Learning Topics: Determining skewness of data related to real system
and its graphical representation 04
02 Module: Regression and correlation
Correlation: Karl Pearson‟s coefficient of correlation, Spearman‟ s rank
correlation coefficient.
Regression: Linear and Non -linear regression (quadratic and cubic),
Estimation using linear regression.
Self Learning Topics: Apply correlation and regression on real world data
and its graphical representation 08
03 Module : Introduction to probability & conditional probability
Introduction to probability, Random experiment, Sample space, Events,
Axiomatic Probability, Algebra of events. Conditional Probability,
Multiplication theorem of Probability, Independent events, Baye s‟ Theorem
Self Learning Topics: Applications based on Bayes‟ theorem 08
04 Module: Random variable
Discrete random variable, Continuous random variable, Two -dimensional
random variable, Joint probability distribution, Stochastic independence,
Properties of Expectation and Variance, Covariance.
Self Learning Topics: Study of various random variables and its
independence. 08
05 Module: Theoretical probability distributions
Binomial, Poisson, Normal.
Self Learning Topics: Study of properties of standard no rmal variate. 07
06 Module: Testing of hypothesis
Hypothesis testing, Type I and Type II errors.
Tests of significance – single sample, Student's t -test, large sample test (z -
test), Chi -Square test - test for independence of attributes.
Self Learning Top ics: Study of elementary sampling methods. 05

Page 24

Reference Books :
Reference
No Reference Name
1 S C Gupta, Fundamentals of Statistics, Himalaya Publishing house, Seventh
edition.
2 S.C.Gupta, V.K.Kapoor , S Chand , Fundamentals of Mathematical Statistic s,
Sultam and Chand sons publication, First Edition
3 Kishore Trivedi, Probability and Statistics with Reliability, Queuing, And
Computer Science Applications, PHI ,First Edition
4 Hwei P. Hsu, Schaum‟s Outlines Probability, Random Variables & Random
Process, Tata McGraw Hill, Third Edition
5 J.Susan Milton, Jesse C. Arnold, Introduction to Probability & Statistics, Tata
McGraw Hill, Fourth Edition
6 Dr J Ravichandran ,Probability & Statistics for Engineers, Wiley
7 Dr Seema Sharma, Statistics for Busi ness and Economics, Wiley
8 Ken Black, Applied Business Statistics, Wiley, Seventh Edition
Web References:
Reference
No Reference Name
1 IIT Kharagpur – Probability and Statistics by Dr. Somesh Kumar
https://nptel.ac.in/courses/111105041/
2 IIT Madras – Introduction to Probability and Statistics by Dr. G. Srinivasan
https://nptel.ac.in/courses/111/106/111106112/
3 IIT Kanpur – Descri ptive Statitics with R Software by Prof. Shalabh
https://nptel.ac.in/courses/111/104/111104120/
4 IIT Roorkee – Business Statistics by Prof. Mukesh Kumar Barua
https://nptel.ac.in/courses/110/107/110107114/
5 MIT – Introduction to Probability and statistics by Jeremy Orloff and Jonathan
Bloom https://ocw.mit.edu/courses/mathematics/18 -05-introduction -to-probability -
and-statistics -spring -2014/index.htm
6 An Introduction to Statistical Learning with Applications in R by Gareth James,
Daniela Witten, Tre vor Hastie and Robert Tibshirani
http://faculty.marshall.usc.edu/gareth -james/ISL/data.html

Tutorials :
SrNo Topic Hrs
1 Find Bowley‟s coefficient of skewness 1
2 Find Karl Pe arson‟s coefficient of skewness 1
3 Calculate Karl Pearson‟s coefficient of correlation 1
4 To fit linear regression and estimate 1
5 Examples on addition and multiplication theorem of probability 1
6 Examples based on Bayes‟ theorem 1
7 Examples base d on independence of discrete random variables. 1

Page 25

8 Examples based on independence of continuous random variables. 1
9 Example on Poisson distribution 1
10 Example on normal distribution 1
11 Example on t -test 1
12 Example on Chi -square test 1
Assess ment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation ha s to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
Term Work : 25 marks
The term work will be based on t he tutorial performance of the student .
End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on the entire syllabu s wherein 4 sub -questions of
5 marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to the number of respective lecture hours as
mentioned in the syllabus.

Page 26

Course Course
Name Teaching Scheme Credits Assigned
MCA12
Advanced
Java Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term
Work End Sem
Exam Total CA Test AVG
20 20 20 -- 80 100
Pre-requisite :
1.Basic understanding of any Object Oriented Programming Language
2. Successfully completed Programming Concepts of Core Java course
Course Objectives: Learner /Student will learn and perform
Sr.No. Course Objective
1  Learn the basic data structure ope ration using Java Collection Framework and
understand Lambda expressions.
2 Build web applications using JSP and JSTL.
3 Understand Spring Framework and build Java EE applications and services.
4 Apply Data Access using Spring Framework
5 Understand how to simplify Spring applications using Spring Boot and spring
Boot RESTful WebServices.
Course Outcomes: On successful completion of course learner/student will be able to
Sr.No. Outcome Bloom Level
CO1 Demonstrate use of data structure and data manipul ation concept
using Java Collection Framework and Lambda expressions. Creating
CO 2 Create JSP using standard actions, custom tags, Introduction to
JSP Standard Tag Library (JSTL) and JSTL Tags. Creating
CO 3 Understand and develop applications using Spr ing Framework,
Lightweight Container and Dependency Injection with Spring. Creating
CO 4 Develop applications using Aspect Oriented Programming with
Spring. Creating
CO 5 Apply JDBC Data Access with Spring and demonstrateData access
operations with Jdbc Template and Spring. Creating
CO 6 Create Spring Boot Web Application and Spring Boot RESTful
WebServices. Creating

Page 27

Modul
e Detailed Contents Hrs
01 Collection and Generic :
Introduction to Generics , Generics Types and Parameterized Types, WildCards
, Java Collection Framework, Collections (Basic Operations, Bulk Operations,
Iteration) List, Set, Maps
Lambda Expressions - Lambda Type Inference, Lambda Parameters,
Lambda Function Body, Returning a Value,From a Lambda Expression,
Lambdas as Objects.
Self learning topics Collection Queues and Arrays 9
02 Introduction Java EE Programming
JSP Architecture, JSP building blocks, Scripting Tags,implicit
object,Introduction to Bean,standardactions,session tracking types and
methods. Custom Tags, Introduction t o JSP Standard Tag Library (JSTL) and
JSTL Tags.
Self learning topics Simple Application using Servlet 8
03 Spring Frameworks:
Introduction to Spring Framework,POJO Programming Model, Lightweight
Containers(Spring IOC container, Configuration MetaData, C onfiguring and
using the Container) Dependency Injection with Spring - Setter Injection,
Constructor Injection, Circular Dependency, Overriding Bean, Auto Wiring
Bean Looksup, Spring Manage Beans)
Self learning topics Bean Definition Profiles 6
04 Spring a nd AOP
AspectOriented Programming with Spring, Types of advices, Defining Point
Cut Designator,Annotations.
Self learning topics AspectJ 5
05 JDBC Data Access with Spring
Managing JDBC Connection, Configuring Data Source to obtain JDBC
Connection, Data A ccess operations with JdbcTemplate and Spring, RDBMS
operation classes , Modelling JDBC Operations as Java Objects
Self learning topics JDBC Architecture and basic JDBC Program using DML
operation 6
06 Getting Started with Spring Boot
Spring Boot and Data base, Spring Boot Web Application Development, Spring
Boot RESTful WebServices.
Self learning topics Understanding Transaction Management in Spring 6

Page 28

Reference Books :

Reference
No Reference Name
1 Java 6 Programming Black Book, Wiley –Dreamtech
ISBN 10: 817722736X ISBN 13: 9788177227369
2 Web Enabled Commercial Application Development using java 2.0, Ivan
Byaross ISBN -10: 8176563560 / ISBN -13: 978-8176563567
3  Java Server Programming java EE6, Black book, Dreamtechpress. ISBN -
10: 8177229362 / ISBN -13: 978-8177229363
4 Core Servlets and Java Server Pages :Vol I: Core Technologies 2/e ,
Marty Hall and Larry Brown, Pearson ,
ISBN: 9788131701638, 8131701638
5 Java Enterprise in a Nutshell, 3rd Edition A Practical Guide, Jim
Farley, William Cr awford,O'Reilly
ISBN -13: 978-0596101428 / ISBN -10: 0596101422
6 Java EE 6 Server Programming For Professionals, Sharanam Shah and Vaishali
Shah, SPD, ISBN -10: 9788184049411 / ISBN -13: 978-8184049411
7 Spring in Action, Craig Walls, 3rd Edition,Manning, I SBN 9781935182351
8 Professional Java Development with the Spring Framework by Rod
Johnsonet al.John Wiley & Sons 2005 (672 pages) ISBN:0764574833
9 Beginning Spring , Mert Calıs kan and KenanSevindik Published by John Wiley
& Sons, Inc. 10475 Crosspoin t Boulevard Indianapolis, IN 46256
www.wiley.com
Web References:
Reference
No Reference Name
1 https://docs.oracle.com
2 Spring.io
Assessment :
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requi rement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Intern al Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.

Page 29

End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Quest ion No: 01 will be compulsory and based on the entire syllabus wherein 4 sub -questions of
5 marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to the number of re spective lecture hours as
mentioned in the syllabus.

Page 30

Course Course Name Teaching Scheme Credits Assigned
MCA13
Advanced
Database
Management
System Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 -- 80 100
Pre-requisite: Database Management System
Course Objectives Course is ai m to
Sr. No. Course Objective
1 Explain the concept of parallel, distr ibuted & ORDBMS and understand their
applications
2 Explain the architecture of Data Warehouse and perform ETL and data
preprocessing tasks.
3 Understand Dimensional Modeling and OLAP architecture.
4 Analyze data, identify the problems ,choose relevant data mining models and
algorithms for respective applications
5 Understand the optimization Algorithms used for training Models.
6 Understand concepts of web and text mining methods.

Course Outcomes:
Sr. No. Outcome Bloom Level
CO1 Demonstrate complex database systems like parallel, distributed &
object oriented databases Understanding
CO 2 Model data warehouse with ETL process and dimensional
modeling and data analysis using OLAP operations. Applying
CO 3 Discover association among it ems using Association rule mining. Analyzing
CO 4 Evaluate different data mining techniques like classification,
prediction, clustering, web and text mining to solve real world
problems. Evaluating

Page 31

Module Detailed Contents Hrs
01 Module: Parallel Database Distributed Database and ORDBMS:
Architecture for Parallel Databases, Types of Distributed Databases,
Distributed DBMS Architecture, Storing Data in a Distributed DBMS.
ORDBMS: Structured Data Types, Operations on Structured Data, Objects,
OIDs and Reference Types, Object oriented versus Object relational
database.
Self Learning Topics: Mapping OODBMS to ORDBMS 05
02 Module: Data warehousing and OLAP:
Data warehouse: Introduction to DW, DW architecture, ETL Process, Top -
down and bottom -up approaches, characteristics and benefits of data mart.
Dimensional Modeling: Star, snowflake and fact constellation schema.
OLAP in the data warehouse: Major features and functions, OLAP models -
ROLAP and MOLAP, Difference between OLAP and OLTP
Self Learning Topics: Study any one DW implementation 06
03 Module: Data Mining and Preprocessing:
Introduction to da ta mining, Knowledge discovery - KDD process.
Data Preprocessing: Types of attributes, Data Cleaning - Missing values,
Noisy data, data integration and transformations.
Data Reduction - Data cube aggregation, dimensionality reduction, data
compression, Nu merosity reduction, discretization and concept hierarchy.
Self Learning Topics: Application of data mining in Business Intelligence 05
04 Module: Data Mining Algorithm - Association rules:
Association rule mining: support and confidence and frequent item sets,
market basket analysis, Apriori algorithm,, Associative classification - Rule
Mining.
Self Learning Topics: Association Rule Mining applications 06
05 Module: Data Mining Algorithm -Classification:
Classification methods: Statistical -based algorithm s- Regression, Naïve
Bayesian classification, Distance -based algorithm - K Nearest Neighbor,
Decision Tree -based algorithms -ID3, C4.5,CART.
Self Learning Topics: Comparative study of classification algorithms 08
06 Module: Data Mining Algorithm -Clustering:
Clustering Methods: Partitioning methods - K-Means, Hierarchical -
Agglomerative and divisive methods
Self Learning Topics: Clustering algorithm applications
06
07 Module: Web Mining and Text Mining:
Web Mining: web content, web structure, w eb usage.
Text Mining: Text data analysis and Information retrieval, text retrieval
methods.
Self Learning Topics: Web Mining Applications 04

Page 32

Reference Books :
Reference
No Reference Name
1 Ponniah, Paulraj, Data warehousing fundamentals: a comprehensi ve guide for IT
professionals, John Wiley & Sons, 2004.
2 Dunham, Margaret H, Data mining: Introductory and advanced topics, Pearson
Education India, 2006.
3 Gupta, Gopal K, Introduction to data mining with case studies, PHI Learning
Pvt. Ltd., 2014.
4 Han, Jiawei, Jian Pei, and Micheline Kamber, Data mining: concepts and
techniques, Second Edition, Elsevier, Morgan Kaufmann, 2011.
5 Ramakrishnan, Raghu, Johannes Gehrke, and Johannes Gehrke, Database
management systems, Vol. 3, McGraw -Hill, 2003.
6 Elmasri, Ramez, and Shamkant B. Navathe, Fundamentals of Database Systems,
Pearson Education, 2008, (2015).
7 Silberschatz, Abraham, Henry F. Korth, and Shashank Sudarshan,Database
system concepts, Vol. 5,McGraw -Hill, 1997.
Web References:
Reference No Reference Name
1 https://www.guru99.com/data -mining -vs-datawarehouse.html
2 https://www.tutorialspoint.com/dwh/dwh_overview
3 https://www.geeksforgeeks.org/
4 https://blog.eduonix.com/internet -of-things/web -mining -text-mining -depth -mining -guide/
Assessme nt:
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.

End Semester Theory Examination:
1. Question paper will c omprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on the entire syllabus wherein 4 sub -questions of
5 marks each will be asked.
4. Remaining questions will be random ly selected from all the modules.
5. Weightage of each module will be proportional to the number of respective lecture hours as
mentioned in the syllabus.

Page 33

Course Course Name Teaching Scheme Credits Assigned
MCA14 Software
Project
Management
Contact H ours
Theory Tutorial Theory Tutorial Total
3 1 3 1 4
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 25 80 125
Pre-requisite: Knowledge of Basic Programming Courses
Course Objectives: course aim to
Sr.N o. Course Objective
01 Understand the concepts of Software Engineering and Project Management.
02 Familiarize Project Management framework and Tools.
03 Apply knowledge of Project Life Cycle to implement the projects.
04 Apply the requirement specifica tion and designing tools along with UML.
05 Understand the techniques of project scheduling & p roject implementation.
06 Learn software cost estimation and software quality assurance techniques.
Course Outcomes: On successful completion of course learne r/student will be able to
Sr.No. Course Outcome Bloom Level
CO1 Define the key concepts of Software Project Management. Remembering
CO2 Demonstrate understanding of the requirements Analysis and
Application of UML Models. Understanding
CO3 Make use of e stimation logic for estimation of software size as
well as cost of software. Applying
CO4 Examine the need of ch ange management during software
development as well as application of quality tools. Analyzing
CO5 Assess various factors influencing project management, quality
assurance and risk assessment. Evaluating
CO6 Develop process for successful quality project delivery. Creating

Module Detailed Contents Hrs.
01 Module: An Overview of Software Project Management:
Introduction to Project, Project Ma nagement, Difference between Software
Engineering & Software Project Management.
An Overview of IT Project Management: Define project, project
management framework, The role of project Manager, Systems View of
Project Management, Stakeholder management,Le adership in Projects:
Modern Approaches to Leadership & Leadership Styles. 04

Page 34

Self Learning Topics: Evolving role of software.
02 Module: Software Process Models:
Project phases and the project life cycle, Waterfall Model, Evolutionary
Process Model: Prot otype and Spiral Model, Incremental Process model:
Iterative approach, RAD model, Agile Development Model: Extreme
programming, Scrum.
Self Learning Topics: JAD &DevOps Model, Comparison among models. 05
03 Module: Software Requirement Analysis and Design :
Types of Requirement, Feasibility Study , Requirement Elicitation
Techniques: Interviews, Questionnaire, Brainstorming, Facilitated
Application Specification Technique (FAST), Requirement Analysis and
Design: Data Flow Diagram (DFD), Data Dictionary , Software Requirement
Specification (SRS).
Object Oriented Analysis and Design: UML Overview, The Nature and
purpose of Models, UML diagrams(Use Case diagram, Activity Diagram,
Class & Object Diagram, Sequence Diagram, State Transition Diagram,
Deployment Diagr am).
Self Learning Topics: Comparison of Requirements Elicitation Techniques. 09
04 Module: Software Project Planning & Software Cost Estimation:
Business Case, Project selection and Approval, Project charter, Project Scope
management, Creating the Work B reakdown Structures (WBS).
Software Estimation: Size Estimation: Function Point (Numericals). Cost
Estimation: COCOMO (Numericals), COCOMO -II (Numericals)till Early
design model.
Self Learning Topics: COCOMO II Post - Architecture model. 07
05 Module: Proje ct Scheduling and Procurement Management :Relationship
between people and Effort: Staffing Level Estimation, Effect of schedule
Change on Cost, Project Schedule, Schedule Control, Critical Path Method
(CPM) (Numericals), Basics of Procurement Management, Ch ange
Management.
Self Learning Topics: Degree of Rigor. 06
06 Module: Software Quality Assurance:
Software and System Quality Management: Overview of ISO 9001, SEI
Capability Maturity Model, McCalls Quality Model, Six Sigma, Formal
Technical Reviews, Tool s and Techniques for Quality Control, Pareto
Analysis, Statistical Sampling, Quality Control Charts and the seven Run
Rule.
Software Risk Management:
Identify IT Project Risk, Risk Analysis and Assessment, Risk Strategies,
Risk Monitoring and Control, Ri sk Response and Evaluation.

Self Learning Topics: Software ReliabilityMetrics, Reliability Growth
Modeling. 08

Page 35

07 Module: The Project Implementation Plan and Closure:
The Project Implementation Plan and Closure : Project Implementation
Administrative Closure.
Self Learning Topics: Ethics in Projects, Multicultural Projects. 01

Reference Books :
Reference
No. Reference Name
1 Software Engineering, 5th and 7th edition , by Roger S Pressman, McGraw Hill
publication.
2 Managing Information Technology Pro ject, 6edition, by Kathy Schwalbe, Cengage
Learning publication.
3 Information Technology Project Management by Jack T Marchewka Wiley India
publication.
4 Software Engineering 3rd edition by KK Agrawal, Yogesh Singh, New Age
International publication.
5 The Unified Modelling Language Reference manual, Second Edition, James
Rambaugh, Iver Jacobson, Grady Booch, Addition - Wesley.
6 Object -Oriented Modeling and Design with UML, Michael Blaha, James
Rumbaugh, PHI (2005).
Web References:
Reference No. Refer ence Name
1 https://www.projectmanager.com
2 https://www.tutorialspoint.com
3 https://technologyadvice.com
4 https://www.javatpoint.com
5 https://www.geeksforgeeks.org

SPM : Tutorials
Sr. No. Detailed Contents Hrs.
01 Business Case for Project (Cas e study). 01
02 Software Requirement Specification (SRS Case Study). 01
03 Project Scheduling tools (any open source tools like Microsoft Projects):
Creating a Project Plan or WBS,
Establishing the Project Start or Finish Date, Entering Tasks. 01
04 Gantt chart, Critical Path Analysis. 01
05 Software Cost Estimation using COCOMO -I / COCOMO -II for Project. 01
06 UML Diagrams: Use Case Diagram. 01
07 UML Diagrams: Activity Diagram. 01
08 UML Diagrams: Class Diagram. 01

Page 36

09 UML Diagrams: Sequence Diagra m 01
10 UML Diagrams: State Chart Diagram. 01
11 UML Diagrams:Component Diagram. 01
12 UML Diagrams:Deployment Diagram. 01

Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz / Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subjec t.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
Term Work : 25 marks
The term work will be based on the tutorial performance of the student .
End Semester Theory Examination:
1. Question paper will comprise of total 06 questio ns, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on the entire syllabus wherein 4 sub -
questions of 5 marks each will be asked.
4. Remaining questions will be randomly selected from all the modul es.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 37

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term
Work Practical Oral Total
MCAL11 Data Stru ctures
Lab using
C/C++ 04 02 50 30 20 100

Pre-requisite: Basic understanding of fundamentals of any programming language
Lab Course Objectives:
Sr. No. Course Objective
1 Understand concepts of searching and sorting algorithms.
2  Impart a thorough und erstanding of linear and non -linear data structures
3  Choose the appropriate data structure for solving real world problems
4  Learn hashing techniques and collision resolution
Lab Course Outcomes: On successful completion of course learner/student will be able to
Sr. No. Outcome Bloom Level
CO1  Implement searching and sorting algorithms Applying
CO 2  Implement linear and non -linear data structures Applying
CO 3  Choose the appropriate data structures to solve complex real life
problems Creat ing
CO 4  Analyze hashing techniques for data storage and retrieval Analyzing
Description:
Module
No Detailed Contents Hrs
01 Module: Sorting Techniques:
Bubble Sort, Insertion Sort, Selection Sort, Shell Sort, Radix Sort
Self Learning Topics: Quick sort 04
02 Module: Searching and Hashing Techniques:
Linear search, Binary search, Methods for Hashing: Modulo Division,
Digit Extraction, Fold shift, Fold Boundary, Linear Probe for Collision
Resolution.

Self Learning Topics : Direct and Subtraction hashing 08
03 Module: Stacks :
Array implementation, Linked List implementation, Evaluation of postfix
expression and balancing of parenthesis
Self Learning Topics: Conversion of infix notation to postfix notation 06
04 Module: Queue: 08

Page 38

Linked List implementation of or dinary queue, Array implementation of
circular queue, Linked List implementation of priority queue, Double
ended queue
Self Learning Topics : Other queue applications
05 Module: Linked List:
Singly Linked Lists, Circular Linked List, Doubly Linked List s : Insert,
Display, Delete, Search, Count, Reverse(SLL), Polynomial Addition
Self Learning Topics : Comparative study of arrays and linked list 10
06 Module: Trees :
Binary search tree : Create, Recursive traversal: preorder, postorder,
inorder, Search La rgest Node, Smallest Node, Count number of nodes,
Heap: MinHeap, MaxHeap: reheapUp, reheapDown, Delete
Self Learning Topics: Expression Tree, Heapsort 08
07 Module: Graphs:
Represent a graph using the Adjacency Matrix, BFS, Find the
minimum spanning tree (using any method Kruskal‟s Algorithm or
Prim‟s Algorithm)
Self Learning Topics : Shortest Path Algorithm 08
Reference Books :

Reference
No Reference Name
1 Y. Langsam, M. Augenstin and A. Tannenbaum, Data Structures using C and
C++, Pearson Education Asia, Second Edition, ISBN No. 978 -81-203-1177 -0
2 Richard F Gilberg Behrouz A Forouzan , Data Structure A Pseudocode Approach
with C, Cengage India, Second Edition, ISBN No. 978 -81-315-0314 -0
3 S. Lipchitz, Data Structures, Mc -Graw Hill Education, ISB N No. 978 -12-590-
2996 -7
4 Ellis Horowitz, S. Sahni, D. Mehta, Fundamentals of Data Structures in C++,
Galgothia Publication, ISBN No. 978 -81-751-5278 -6
5 Michael Berman, Data structures via C++, Oxford University Press, First Edition,
ISBN No. 978-01-980-8952 -0
Web References:
Reference
No Reference Name
1 https://www.digimat.in/nptel/courses/video/106106133/L25.html
2 https://www.youtube.com/watch?v=zWg7U0OEAoE
3 https://www.digimat.in/nptel/courses/video/106106145/L01.html
4 https://www.cs.aucklan d.ac.nz/software/AlgAnim/ds_ToC.html
5 https://nptel.ac.in/courses/106/101/106101208/
Suggested list of experiments
Practical No Problem Statement
1 Implementation of different sorting techniques.
2 Implementation of searching algorithms.
3 Implementa tion of stacks(Using arrays and Linked List)
4 Implementation of Stack Applications like:
a. Postfix evaluation

Page 39

b. Balancing of Parenthesis
5 Implement all different types of queues.
6 Demonstrate application of queue (eg. Priority Queue, Breath First Search )
7 Implementation of all types of linked lists.
8 Demonstrate application of linked list (eg. Polynomial addition, Sparse matrix)
9 Create and perform various operations on BST.
10 Implementing Heap with different operations performed.
11 Create a G raph storage structure (eg. Adjacency matrix)
12 Perform various hashing techniques with Linear Probe as collision resolution
scheme.
13 Create a minimum spanning tree using any method Kruskal‟s Algorithm or
Prim‟s Algorithm
14 Implementation of Graph t raversal. (DFS and BFS)
15 Group project (3 to 4 members) to be given to work on one application to a
real world problem like:
a) Bus routes of school buses for XYZ school
b) Voucher system for a canteen
c) Game like Sudoku solver
d) Car pooling application etc.
Assessment :
Term Work: Will be based on Continuous Assessment
1. Laboratory work will be based on the syllabus with minimum 10 experiments and group
project (refer Practical No. 15 in suggested list of experiments).
Experiments 25 marks
Group Project 15 marks
Attendance 10 marks
2. Practical will be evaluated by the subject teacher and documented according to a rubrics.
End Semester Practical Examination: Practical and oral examination will be based on
suggested practical list and entire syllabus.

Page 40

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term
Work Practical Oral Total
MCAL12 Advanced Java
Lab 02 01 25 30 20 75

Pre-requisite: 1. Basic understanding of Core Java Programming.
2. Basics of web technology.
Lab Course Objectives: Students will be able to
Sr.No. Course Objective
1 Write programs based on Java Generics, Collection framework and Lambda
expressions.
2 Develop web applications using JSP and JSTL.
3 Demonstrate Data Access wi th Spring.
4 Build an application using Spring Framework.
5 Develop Spring applications using Spring Boot and spring Boot RESTful Web
Services.
Lab Course Outcomes: On successful completion of course learner/student will be able to
Sr. No. Outcome Bloo m
Level
CO1 Demonstrate use of data structure and data manipulation concept
using Java Collection Framework and Lambda expressions. Applying
CO 2 Build JSP web application using standard actions, custom tags and
JSTL Tags. Creat ing
CO 3 Develop applicat ion using Spring Framework, Lightweight
Containers and Dependency Injection with Spring. Applying
CO 4 Develop applications using Aspect Oriented Programming with
Spring. Applying
CO 5 Build JDBC application with Spring using JdbcTemplate. Creat ing
CO 6 Develop Spring Boot Web Application and Spring Boot RESTful
web services. Creat ing

Description:
Module Detailed Contents Hrs
01 Module: Java Collections and Generics :
Programs based on Generic classes and Java Collection Framework List, Set and
Map, Wildcards and Lambda expressions. 4

Page 41

Self Learning Topics: Collection Queues and Arrays
02 Module: Introduction Java EE Programming:
Programs based on JSP elements, Standard Actions, JSP Directives, Implicit objects,
Error handling in JSP, Session trackin g – Cookies and Session, Custom tags, JSTL
tags.
Self Learning Topics: Simple Application using Servlet 4
03 Module: Spring Framework :
Programs based on using Spring Framework, dependency injection.
Self Learning Topics: Bean Definition Profiles 4
04 Module: Aspect Oriented Programming : Programs based on Spring AOP –
Before, After, Around, After Returning and After Throwing advice, PointCuts.
Self Learning Topics: AspectJ 4
05 Module: JDBC Data Access with Spring using Oracle/ MySQL database :
Programs b ased of Spring JDBC, JdbcTemplate, PreparedStatementCallback,
ResultSetExtractor and RowMapper interface.
Self Learning Topics: Basic JDBC Program using DML operation 6
06 Module: Getting Started with Spring Boot: Programs based on Spring Boot,
RESTful We b Services with Spring Boot.
Self Learning Topics: Understanding Transaction Management in Spring 4
Reference Books :

Referenc
e No. Reference Name
1 Java 6 Programming Black Book, Wiley –Dreamtech
2 Web Enabled Commercial Application Development using ja va 2.0, Ivan Byaross
3 Java Server Programming java EE6, Black book, Dreamtech Press.
4 Core Servlets and Java Server Pages: Vol. I: Core Technologies 2/e , Marty
Hall and Larry Brown, Pearson
5 Java Enterprise in a Nutshell, 3rd Edition A Practical Gui de, Jim Farley,
William Crawford, O‟Reilly
6 Java EE 6 Server Programming For Professionals, Sharanam Shah and
Vaishali Shah, SPD
7 Spring in Action, Craig Walls, 3rd Edition, Manning
8 Professional Java Development with the Spring Framework, Rod Johnso n et al., John
Wiley & Sons
9 Beginning Spring, Mert Caliskan and Kenan Sevindik, John Wiley & Sons
10 Spring Recipes A Problem Solution Approach, Gary Mak, Josh Long and Daniel Rubio,
Apress

Web References:
Reference No Reference Name
1 https://docs.oracle.com
2 https://spring.io/
Suggested list of experiments
Practical No Problem Statement
1. Assignments on Java Generics
1. Write a Java Program to demonstrate a Generic Cla ss.
2. Write a Java Program to demonstrate Generic Methods.

Page 42

3. Write a Java Program to demonstrate Wildcards in Java Generics.
2. Assignments on List Interface
1. Write a Java program to create List containing list of items of type String and use for -
--each loop t o print the items of the list.
2. Write a Java program to create List containing list of items and use ListIterator
interface to print items present in the list. Also print the list in reverse/ backword
direction.
3. Assignments on Set Interface
1. Write a Java program to create a Set containing list of items of type String and print
the items in the list using Iterator interface. Also print the list in reverse/ backword
direction.
2. Write a Java program using Set interface containing list of items and perform the
following operations:
a. Add items in the set.
b. Insert items of one set in to other set.
c. Remove items from the set
d. Search the specified item in the set
4. Assignments on Map Interface
Write a Java program using Map interface containing list of items having k eys and
associated values and perform the following operations:
a. Add items in the map.
b. Remove items from the map
c. Search specific key from the map
d. Get value of the specified key
e. Insert map elements of one map in to other map.
f. Print all keys and values of th e map.
5. Assignments on Lambda Expression
1. Write a Java program using Lambda Expression to print ”Hello World”.
2. Write a Java program using Lambda Expression with single parameters.
3. Write a Java program using Lambda Expression with multiple parameters to a dd two
numbers.
4. Write a Java program using Lambda Expression to calculate the following:
a. Convert Fahrenheit to Celcius
b. Convert Kilometers to Miles.
5. Write a Java program using Lambda Expression with or without return keyword.
6. Write a Java program using Lamb da Expression to concatenate two strings.
6. Assignments based on web application development using JSP
1. Create a Telephone directory using JSP and store all the information within a
database, so that later could be retrieved as per the requirement. Make y our own
assumptions.
2. Write a JSP page to display the Registration form (Make your own assumptions)
3. Write a JSP program to add, delete and display the records from StudentMaster
(RollNo, Name, Semester, Course) table.
4. Design loan calculator using JSP which accepts Period of Time (in years) and
Principal Loan Amount. Display the payment amount for each loan and then list the
loan balance and interest paid for each payment over the term of the loan for the
following time period and interest rate:
a. 1 to 7 year at 5.35%
b. 8 to 15 year at 5.5%
c. 16 to 30 year at 5.75%
5. Write a program using JSP that displays a webpage consisting Application form for
change of Study Center which can be filled by any student who wants to change his/
her study center. Make neces sary assumptions
6. Write a JSP program to add, delete and display the records from StudentMaster
(RollNo, Name, Semester, Course) table.
7. Write a JSP program that demonstrates the use of JSP declaration, scriptlet,
directives, expression, header and footer.
7. Assignment based Spring Framework
1. Write a program to print “Hello World” using spring framework.
2. Write a program to demonstrate dependency injection via setter method.

Page 43

3. Write a program to demonstrate dependency injection via Constructor.
8. Assignment based Aspect Oriented Programming
1. Write a program to demonstrate Spring AOP – before advice.
2. Write a program to demonstrate Spring AOP – after advice.
3. Write a program to demonstrate Spring AOP – around advice.
4. Write a program to demonstrate Spring AOP – after returning advice.
5. Write a program to demonstrate Spring AOP – after throwing advice.
6. Write a program to demonstrate Spring AOP – pointcuts.
9. Assignment based Spring JDBC
1. Write a program to insert, update and delete records from the given ta ble.
2. Write a program to demonstrate PreparedStatement in Spring JdbcTemplate
3. Write a program in Spring JDBC to demonstrate ResultSetExtractor Interface
4. Write a program to demonstrate RowMapper interface to fetch the records from the
database.
10. Assignme nt based Spring Boot and RESTful Web Services
1. Write a program to create a simple Spring Boot application that prints a message.
2. Write a program to demonstrate RESTful Web Services with spring boot.
Assessment :
Term Work: Will be based on Continuous Assess ment
1. Laboratory work will be based on the syllabus with minimum 10 experiments.
Experiments 20 marks
Attendance 5 marks
2. Practical will be evaluated by the subject teacher and documented according to a rubric

End Semester Practical Examination:
Prac tical and oral examination will be based on the suggested practical list and entire
syllabus.

Page 44

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term
Work Practical Oral Total
MCAL13 Advanced
Database
Management
System Lab 02 01 25 30 20 75

Pre-requisite: Database Management System, SQL.

Lab Course Objectives

Sr.No. Course Objective

1 Understanding functioning of advanced databases like distributed and ORDBMS.

2 Understand ETL process performed to create da ta warehouse.

3 Understand basic OLAP Operations.

4 Understand the data mining process and important issues around data cleaning.

5 Identify the problems, choose relevant data mining algorithms and analyze the
results for respective applications.

Lab Course Outcomes: On successful completion of course learner/student will be able to

Sr.No. Outcome Bloom Level
CO1 Demonstrate distributed and ORDBMS concepts Applying
CO 2 Perform ETL operations used in the building data warehouse. Applying
CO 3 Demonstrate and analysis various OLAP operations . Analyzing
CO 4 Implement and evaluate different data mining techniques like
classification, prediction, clustering a nd association rule mining in R Evaluatin

Page 45

Description:
Module
No Detai led Contents Hrs
1 Distributed Database :
Implementation of Partitions: Range, List.
Self-Learning Topics : Hash Partition, Composite partition 2
2 OLAP with Oracle :
Analytical Queries
Self-Learning Topics: Cume_list, Percent_rank 4
3 ORDBMS :
Implemen tation of,
• Abstract Data Type
• Reference
Self-Learning Topics: Nested ADT, Inheritance 2
4 ETL through Pentaho :
ETL Transformation with Pentaho
Self-Learning Topics: Any two more transformation operation in Pentaho beyond
the syllabus 4
5 Basics Of R and Data Acquisition :
Introduction to R, Data Types and Objects, Reading and writing data,
Reading data from the console
Packages, Loading packages, Attach, and detaching data. Loading Data
from different Data Source
Self-Learning Topics: Operators, C onditional Statements and Loops, Functions,
Loading data from Relational Databases, XML 2
6 Preprocessing in R :
Data preprocessing techniques in R
Self-Learning Topics:Sorting, Date Conversion 2
7 Data Mining - Classification using R -Programming :
Imple mentation and Analysis of -Regression, Classification Models
Self-Learning Topics: Implement One classification algorithm in weka 6
8 Data Mining - Clustering and Association using R -Programming :
Implementation of Market Basket Analysis and Clustering .
Self-Learning Topics: Implementation clustering, association in Weka 4
Reference Books:
Reference
No Reference Name
1 John M. Quick, “Statistical Analysis with R”", PACKT Publishing, 2015 ISBN
NO: 9781849512084, 9781849512084
2 G.K. Gupta, “Introduct ion to data mining with case studies ”, PHI Learning
Publishing, ISBN: 9788120350021, 8120350022

Web References:
Reference No Reference Name
1 http://cookbook -r.com
2 https://www.r -project.org/about.html
3 “Statistical Analysis with R - a quick start” , Oleg Nenadic, Walter Zucchini,
September 2004, http://www.statoek.wiso.uni -goettingen.de /mitarbeiter /ogi
/pub /r_workshop.pdf
4 https://www.rstudio.com/
5 http://www.r -project.org/doc/bib/R -books.html

Page 46

Suggested List of Experiments
Practical No. Problem Statement
1 Implementation of Data partitioning through Rang and List partitioning
2 Implementation of Analytical queries like
Roll_UP , CUBE , First, Last , Lead ,Lag,Rank AND Dense Rank
3 Implementation of ORDBMS concepts like
ADT(Abstract Data Types) , Reference
4 Implementation of ETL transformation with Pentaho like
Copy data from Source (Table/Excel/ Oracle) and store it to Target
(Table/Excel/ Oracle) , Adding sequence ,Adding Cal culator
Concatenation of two fields , Splitting of two fields , Number Range ,
String Operations , Sorting data , Implement the merge join
transformation on tables , Implement data validations on the table data.
5 Introduction to R programming and Data acquisit ion
Install packages , Loading packages
Data types, checking type of variable, printing variable and objects
(Vector, Matrix, List, Factor, Data frame, Table)
cbind -ing and rbind -ing, Reading and Writing data.
setwd(), getwd(), data(), rm() , Attaching and Detaching data.
Reading data from the consol.
Loading data from different data sources.(CSV, Excel).
6 Implementation of Data preprocessing techniques like,
Naming and Renaming variables, adding a new variable.
Dealing with missing data.
Dealing with cat egorical data.
Data reduction using subsetting
7 Implementation and analysis of Linear regression through graphical methods.
8 Implementation and analysis of Classification algorithms like
Naive Bayesian, K-Nearest Neighbor , ID3 , C4.5
9 Implementat ion and analysis of Apriori Algorithm using Market Basket
Analysis.
10 Implementation and analysis of clustering algorithms like
K-Means , Agglomerative

Assessment :

Term Work: Will be based on Continuous Assessment

1. Laboratory work will b e based on the syllabus with minimum 10 experiments.
Experiments 20 marks
Attendance 5 marks

2. Practical will be evaluated by the subject teacher and documented according to a rubric

End Semester Practical Examination:

Practical and oral examina tion will be based on the suggested practical list and entire syllabus.

Page 47

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term
Work Practical Oral Total
MCAL14 Web
Technologies 04 02 50 30 20 100

Pre-requisite: Basic u nderstanding of fundamentals of Web Technologies and JavaScript
Lab Course Objectives Course aim to
Sr.No. Course Objective
1 Create simple websites based on Node.js features
2 Demonstrate database connectivity and operations
3 Make applications making use of Angular.js concepts
4 Construct Angular.js Forms and Single Page Applications
Lab Course Outcomes: On successful completion of course learner/student will be able to
Sr.No. Outcome Bloom Level
CO1  Build simple websites making use of various Node. js features Applying
CO 2  Design a dynamic web application enabled with database
connectivity Creating
CO 3  Use the fundamentals of Angular.js Filters, Directives and
Controllers to build applications Applying
CO 4  Develop Forms and Single page applicat ions (SPA) Creating
Description:
Module
No Detailed Contents Hrs
1 Introduction to Node.js :
What is Node.js, Advantages of Node.js, Node.js Process Model, Traditional
Web Server Model, Setup Development Environment: Installation of Node.js
on Windows, Working in REPL, Node JS Console

Self-Learning Topics : W3C Architecture 2
2 Node.js Modules, Events & Functions :
Standard Callback Pattern, Event Emitter Pattern, Event Types, Event
Emitter API, Creating an Event Emitter, Defer Execution of a Function ,
Cancel Execution of a Function, Schedule/Cancel repetitive execution of a
Function, Block/Escape Event Loop
Self-Learning Topics : Additional Events 6
3 File Handling& HTTP Web Server :
File Paths, fs Module, Opening a file, Reading from a file, Writing to a file,
Closing a file.
HTTP request/response object, Headers, Piping, Shutting down the server
Self-Learning Topics: TCP server 12
4 Databases :
Connect and Communicate with a MySQL Database, Adding data to the
database, Reading data 4

Page 48

Self-Learning To pics: Working with any other database
5 Angular JS Basics :
Introduction to AngularJS, MVC Architecture, Conceptual Overview:Setting
up the Environment,First Application, Understanding ng attributes,
Expressions: Number and String Expressions, Object Bi nding and
Expressions,Working with Arrays
Self-Learning Topics: Comparison of Angular and React js 6
6 Filters, Directives :
Built -In Filters, Uppercase and Lowercase Filters, Currency and Number
Formatting Filters,OrderBy Filter ,
Introduction to Directiv es, Directive Lifecycle, Conditional Directives,Styles
Directives, Mouse and Keyboard Events Directives, Using Angular JS built -
in directives: app, init, model

Self-Learning Topics: Creating a custom filter and a custom directive 8


7 Controllers :
Unde rstanding Controllers, Programming Controllers & $scope object,
Adding Behavior to a Scope Object, Passing Parameters to the Methods,
Having Array as members in Controller Scope.
Self-Learning Topics: Nested Controllers 6
8 Forms and SPA (Single Page Appl ication):
Working with Simple Angular Forms, Working with Select and Options,
Input Validations, Using CSS classes, Form Events, Custom Model update
triggers, Custom Validation.
Introduction to SPA, Creating HTML Template, Configuring Route
Provider, Creat ing Single Page Application
Self-Learning Topics: Animations 8

Reference Books :
Reference
No Reference Name
1 Powell TA, Powell TA. HTML & CSS: the complete reference. New York:
McGraw -Hill; 2010. ISBN No. 9780071496292
2 Haverbeke M. Eloquent Javasc ript: A modern introduction to programming. No
Starch Press; 2018. ISBN No. 9781593279509
3 Teixeira P. Professional Node.js: Building Javascript based scalable software.
John Wiley & Sons; 2012. ISBN No. 9781118185469
4 Brown E. Web development with nod e and express: leveraging the JavaScript
stack. O'Reilly Media; 2014. ISBN No. 9781491949306
5 Karpov V, Netto D. Professional AngularJS. John Wiley & Sons; 2015. ISBN
No. 9781118832073
6 Dayley B. Learning AngularJS. Pearson Education; 2014. ISBN No.
9780134034546
7 Seshadri S, Green B. AngularJS: Up and Running: Enhanced Productivity with
Structured Web Apps. O'Reilly Media; 2014. ISBN No. 9781548785710

Page 49

Web References:
Reference
No Reference Name
1 https://nptel.ac.in/courses/106106222/
2 https://learn.shayhowe.com/html -css/
3 https://www.w3schools.com/nodejs/
4 https://www.coursera.org/learn/server -side-nodejs
5 https://www.coursera.org/learn/single -page -web-apps-with-angularjs
6 https://docs.angularjs.org/tutorial
7 https://www.freecodecamp.org/news/want -to-learn -angular -heres -our-free-33-
part-course -by-dan-wahlin -fc2ff27ab451/
Suggested list of experiments
Practical No Problem Statement
1 Create an application to demonstrate Node.js Modules
2 Create an application to demonstrate various Node.js Events
3 Create an application to demonstrate Node.js Functions
4 Using File Handling demonstrate all basic file operations (Create, write, read,
delete)
5 Create an HTTP Server and perform operations on it
6 Create an application to establish a connection with the MySQL database and
perform basic database operations on it
7 Create an application using Filters
8 Create an application to demonstrate directives
9 Demonstrate controllers in Angular.js through an application
10 Demonstrate features of Angular.j s forms with a program
11 Create a SPA (Single Page Application)
Assessment :
Term Work: Will be based on Continuous Assessment
1. Laboratory work will be based on the syllabus with minimum 10 experiments.
Experiments 40 marks
Attendance 10 marks
2. Practical will be evaluated by the subject teacher and documented according to a rubric
End Semester Practical Examination:
Practical and oral examination will be based on suggested practical list and entire syllabus.

Page 50

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term
Work Pract. Oral Total
MCAP1
1 Mini Project – 1 A 02 01 50 - -- 50

Pre-requisite: NIL
Lab Course Objectives: The course is aimed to
Sr. No. Course Objective
1 Conceptualize knowledge w ith emphasis on team work, effective communication,
critical thinking and problem solving skills.
2 Adapt to a rapidly changing environment by having learned and applied new skills
and new technologies.
3 Acquaint with the process of applying basic compu ter applications and provide
solutions to the problems in various application domains.

Lab Course Outcomes: On successful completion of course learner/student will be able to
Sr. No. Course Outcome Bloom Level
CO1 Demonstrate the ability to produce a t echnical document. Understanding
CO2 Apply software project management skills during project work. Applying
CO3 Build small groups to work effectively in team on medium scale
computing projects. Creating
CO4 Design and evaluate solutions for complex pro blems. Creating

Guidelines for Mini Project:
1. Students shall form a group of 2 to 3 students .
2. Students should do survey and identify needs, which shall be converted into problems in
consultation with the faculty Supervisor/Guide/HOD/Internal Committee of facultie s.
The project contact hours shall be allotted in the time table and 2 hours work load shall be
considered for the guide/ supervisor .
3. Students shall submit an implementation plan in the form of Gantt/PERT/CPM chart,
which will cover weekly activit y of mini project.

Page 51

4. A log book to be prepared by each group, wherein the group can record weekly work
progress, Guide/Supervisor can verify and record notes/comments.
5. Faculty may give inputs during mini project activity; however, focus shall be on self -
learning.
6. Students in a group shall understand the problem effectively, propose multiple solutions
and select the best possible solution in consultation with G uide/ Supervisor.
7. Students shall convert the best solution into a working model using various com ponents
of their domain areas and demonstrate.
8. The solution to be validated with proper justification and project report to be compiled in
standard format of University of Mumbai.

Assessment of Mini Project:
I) Term work (25 Marks):
 The progress of the m ini project to be evaluated on a continuous basis.
 In continuous assessment focus shall also be on each individual student, assessment based
on individual‟s contribution in group activity, their understanding and response to questions.
 Distribution of Term work marks shall be as below;
o Marks awarded by guide/supervisor based on log book : 10
o Self contribution and use of skill set in project : 10
o Quality of Project report : 05

II) Mini Project Internal Examination (25 Marks):
 Report should be prepare d as per the guidelines issued by the University of Mumbai.
 The students shall present a seminar on Mini project and demonstrate their understanding of
need/problem.
 Mini Project shall be evaluated through a presentation and demonstration of working model
by the student project group to a pane l of examiner at Institute level.
 Mini Project shall be assessed based on following points:
 Quality of survey/ need identification.
 Clarity of Problem definition based on need.
 Innovativeness in solutions.
 Feasibilit y of proposed problem solutions and selection of best solution.
 Cost effectiveness.
 Societal impact.
 Full functioning of working model as per stated requirements.
 Effective use of skill sets.
 Contribution of an individual as a member or leader.
 Clarity in written and oral communication.

Page 52

Semester II

Page 53

Course
Code Course Name Teaching Scheme Credits Assigned
MCA21 Mathematical
Foundation
for Computer
Science 2 Contact Hours
Theory Tutorial Theory Tutorial Total
3 1 3 1 4
Examinati on Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 25 80 125
Pre-requisite: Basic knowledge of Mathematics and Statistics
Course Objectives: The course aim to
Sr.No Course Objective
1  Study the formulation of Linear programm ing problems and obtain the optimum
solution using various methods.
2 Solve the transportation, assignment problems and obtain their optimal solution
3 Use competitive strategy for analysis and learn to take decisions in various business
environments
4 Understand queuing and simulation models and analyze their performance in real
world systems

Course Outcomes: On successful completion of course learner/student will be able to
Sr.No
. Outcome Bloom
Level
CO1 Formulate mathematical model for a broa d range of problems in business
and industry. Creat ing
CO 2 Apply mathematics and mathematical modeling to forecast implications of
various choices in real world problems Applying
CO 3 Think strategically and decide the optimum alternative from various
available options Evaluat ing
CO 4 Evaluate performance parameters of a real system using various methods Evaluat ing

Page 54

Modul
e No Detailed Contents Hrs
01 Linear Programming Problem : Introduction, Formulation of linear
programming problem and basic fe asible solution: graphical method, Simplex
method, artificial variables, Big M method, Two Phase method.
Self Learning Topics: special cases of LPP 10
02 Transportation Problem : Definition of Transportation Problem, Initial basic
feasible solution: North -West Corner method, Least Cost method, Vogel‟s
Approximation method, optimum solution: MODI method.
Self Learning Topics: optimization using stepping stone method 6
03 Assignment Problem & Travelling Salesman Problem : Definition of
assignment Problem : Hungarian method (minimization and maximization),
Travelling Salesman Problem : Hungarian method.
Self Learning Topics: Simple applications in daily life 6
04 Game Theory & Decision Making : Rules of Game Theory, Two person
zero sum game, solving simple g ames (2x2 games), solving simple games
(3x3 games) Decision making under certainty, under uncertainty, Maximax
Criterion ,Maximin Criterion , Savage Minimax Regret criterion,
Laplace criterion of equal Likelihoods, Hurwicz criterion of Realism
Self Learni ng Topics: Decision tree for decision -making problem. 7
05 Queuing Models : Essential features of queuing systems, operating
characteristics of queuing system, probability distribution in queuing systems,
classification of queuing models, solution of queu ing M/M/1 :  /FCFS,
M/M/1 : N/FCFS.
Self Learning Topics: Understanding Kendle‟s notation in queuing theory 5
06 Simulation : Introduction to simulation, steps in simulation, advantages of
simulation, limitations of simulation, applications of s imulation, Monte -Carlo
method: simple examples, single server queue model.
Self Learning Topics: Generation of pseudo random numbers and their
properties. 6
Reference Books :

Reference
No Reference Name
1 Hamdy A. Taha, University of Arkansas, “Operat ions Research: An
Introduction”, Pearson, 9th Edition, ©2011, ISBN -13: 9780132555937
2 Sharma, S.D. and Sharma, H. , “Operations Research: Theory, methods and
Applications”,KedarNath Ram Nath, 2010, 15, reprint

Page 55

3 J. K. Sharma, “Operations Research : The ory And Applications” , Macmillan
India Limited, 2006 (3 Edition),ISBN 1403931518, 9781403931511
4 S. C. Gupta, “Fundamentals of Statistics” – Himalaya Publishing House, 2017,
7th edition, ISBN 9350515040, 9789350515044
5 Prem Kumar Gupta & D S Hira, S. Chand publications , “Operations
Research”, 7/e,
ISBN -13: 978-8121902816, ISBN -10: 9788121902816
6 A. Ravindran, Don T. Phillips, James J. Solberg, “Operations Research:
Principles and Practice”, 2nd Edition, January 1987, ISBN: 978 -0-471-08608 -6
7. Frederick S. Hillier, Gerald J. Lieberman, Introduction to Operations Research
, McGraw -Hill, 2001,Edition7, illustrated,ISBN 0071181636,
9780071181631
8. Jerry Banks, John S. Carson, Barry L. Nelson, Contributor Barry L. Nelson
"Discrete -event System Simul ation",Prentice Hall, 1996, Edition 2,
illustrated, ISBN 0132174499, 9780132174497
Web References:
Reference
No Reference Name
1 Operations Research, Prof.Kusum Deep, IIT -MADRAS,
https://nptel.ac.in/courses/111/107/111107128/
2 Introduction to Operatio ns Research, Prof. G. Srinivasan, IIT -ROORKEE,
https://nptel.ac.in/courses/110/106/110106062/
3 Fundamentals of Operations Research, Prof. G. Srinivasan, IIT -MADRAS,
https://nptel.ac.in/courses/112/106/112106134/
4 Modeling and simulation of discrete eve nt systems,Prof.P. Kumar Jha, IIT -
ROORKEE, https://nptel.ac.in/courses/112107220/
5 Game Theory, Prof. K. S. MallikarjunaRao, IIT -BOMBAY,
https://nptel.ac.in/courses/110/101/110101133/
6 Decision Modelling, Prof. BiswajetMahanty, IIT -KHARGPUR,
https://nptel.ac.in/courses/110105082/
7 Karmarkar's Method: https://www.youtube.com/watch?v=LWXXhBIlj0o
8 Karmarkar's Method : https://en.wikipedia.org/wiki/Karmarkar%27s_algorithm
Tutorials :
Sr.
No. Topic Hr
1 Linear programming problem using graphical method 1
2 Linear programming problem using simplex method 1
3 Linear prog ramming problem using Big M method 1
4 Finding the basic feasible solution using Vogel‟s Approximation
Method 1

Page 56

5 Finding the optimal solution using Modi Method 1
6 Assignment Problem using Hungarian method 1
7 Travelling salesman Problem using Hungar ian method 1
8 Solving Two person zero sum game 1
9 Decision Making Under Uncertainty 1
10 Queuing system - (M/M/1):(FCFS /infinity) 1
11 Queuing system - (M/M/1 ): (FCFS/N) 1
12 Monte -Carlo Method 1
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. T he faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
Term Work : 25 marks
The term work will be based on the tutorial performance of the student .
End Semester Theory Examination:

1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -questions of 5
marks ea ch will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus

Page 57

Course
Code Course Name Teaching Scheme Credits Assig ned
MCA22
Artificial
Intelligence
And Machine
Learning Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 -- 80 100
Pre-requisite: Basics o f data mining and Mathematical foundations of computerscience -MCA11
Course Objectives The c ourse aim to
Sr.No. Course Objective
1  Understand different AI concepts
2 Elucidate knowledge of Artificial Intelligence techniques for problem solving
3 Understand Artificial intelligence search strategies and neural networks
4 Provide an insight into the fundamentals of Machine Learning Techniques
5 Become familiar with regression methods, classification methods, clustering
methods
6 Become familiar with methods to improve the learnin

Course Outcomes: On the successful completion of the course, students will be able to
Sr.No. Outcome Bloom Level
CO1 Interpret Artificial Intelligence concepts intelligence concepts Understanding
CO 2 Apply Artifici al intelligence techniques for problem solving Applying
CO 3 Analyze the fundamentals of machine learning, the learning
algorithms and the paradigms of supervised and un -supervised
learning Analyz ing
CO 4 Identify methods to improve machine learnin g results for better
predictive performance Applying

Page 58

Modul
e Detailed Contents Hrs
1 Module1:Introduction: Artificial Intelligence, Application of AI, AI
Problems, Problem Formulation, Intelligent Agents, Types of Agents, Agent
Environments, PEAS re presentation for an Agent, Architecture of Intelligent
agents. Reasoning and Logic, Prepositional logic, First order logic, Using
First-order logic, Inference in First -order logic, forward and Backward
Chaining
Self-Learning topics: Expert systems 6
2 Module2: Search Strategies: Solving problems by searching, Search - Issues
in The Design of Search Programs, Un -Informed Search - BFS, DFS; Heuristic
Search Techniques: Generate -And- Test, Hill Climbing, Best -First Search, A*
Algorithm, Alpha beta search alg orithm, Problem Reduction, AO*Algorithm,
Constraint Satisfaction, Means -Ends Analysis
Self-Learning topics: Tabu search 8
3 Module3:Artificial Neural Networks : Introduction, Activation Function,
Optimization algorithm - Gradient decent, Networks - Percept rons, Adaline,
Multilayer Perceptrons , Backpropogation Algorithms Training Procedures,
Tuning the Network Size
Self-Learning topics: Maxnet algorithm 6
4 Module4: Introduction to ML : Machine Learning basics, Applications of
ML,Data Mining Vs Machine L earning vs Big Data Analytics.
Supervised Learning - Naïve Base Classifier, , Classifying with k -Nearest
Neighbour classifier, Decision Tree classifier, Naive Bayes classifier.
Unsupervised Learning - Grouping unlabeled items using k -means clustering,
Association analysis with the Apriori algorithm Introduction to reinforcement
learning
Self-Learning topics: Density Based Clustering,K -medoid 4
5 Module5: Forecasting and Learning Theory : Non-linear regression,
Logistic regression, Random forest, Baysian Be lief networks, Bias/variance
tradeoff, Tuning Model Complexity, Model Selection Dilemma
Clustering : Expectation -Maximization Algorithm, Hierarchical Clustering,
Supervised Learning after Clustering, Choosing the number of clusters,
Learning using ANN
Self-Learning topics: Maximum Likelihood Estimation 6

Page 59

6 Module6:Kernel Machines & Ensemble Methods
Introduction, Optimal Separating Hyperplane, Separating data with maximum
margin, Support Vector Machine (SVM), Finding the maximum margin, The
Non-Separable C ase: Soft Margin Hyperplane, Kernel Trick, Defining
Kernels
Ensemble Methods : Mixture Models, Classifier using multiple samples of the
data set, Improving classifier by focusing on error, weak learner with a
decision stump, Bagging , Stacking, Boosting , Implementing the AdaBoost
algorithm, Classifying with AdaBoost Bootstrapping and cross validation
Self-Learning topics: SMO Algorithm 8
7 Module7:Dimensionality Reduction : Introduction, Subset Selection,
Principal Components Analysis, Multidimensional Scal ing, Linear
Discriminant Analysis.
Self-Learning topics; Feature selection – feature ranking and subset
selection 2
Reference Books :

Reference
No Reference Name
1 George F Luger, Artificial Intelligence, Fifth Edition -2009, Pearson Education
Publicatio ns ,ISBN -978-81-317-2327 -2
2 Stuart Russell, Peter Norvig ,Artificial Intelligence – A Modern Approach, ,
Pearson Education / Prentice Hall of India, 3rd Edition, 2009 . ISBN - 13: 978-
0136042594
3 Elaine Rich, Kevin Knight, S.B. Nair, Artificial Intellig ence, 3rd Edition, Tata
McGraw Hill -2008., ISBN 10: 007008770 9 / ISBN 13: 9780070087705
4 Anandita Das ,Artificial Intelligence and Soft Computing for Beginners -,2nd
Edition, ShroffPublication, ISBN - 9789351106159
5 Nils J. N ilsson, ―Artificial Intelligence: A new Synthesis, Morgan Kaufmann
Publishers, Harcourt Asia Pvt. Ltd., 2000, ISBN -1-55860 -535-5
6 Kumar Satish ,Neural Networks, Second edition Tata McGraw Hill -,2013,
ISBN1259006166, 9781259006166
7 EthemAlpaydın, Intro duction to Machine Learning, PHI, Third Edition, ISBN
No. 978 -81-203- 5078 -6. ( this can be made the text book)
8 Peter Harrington, Machine Learning in Action . Manning Publications , April
2012 ,ISBN 9781617290183
9 Tom Mitchell, Machine Learning, Mcg raw-Hill, First Edition, ISBN No. 0 -07-
115467 -1.
10 Christopher M. Bishop, Pattern Recognition and Machine Learning, Mcgraw -
Hill, ISBN No. 978 -81-322-0906 -5

Page 60

11 ShaiShalev -Shwartz and Shai Ben David ,Understanding Machine Learning
From Theory to Algorith ms, Cambridge University Press, First Edition, ISBN
No. 978 -1-107-05713 -5
Web References:
Reference
No Reference Name
1 nptel.ac.in -A first course in Artificial Intelligence -Deepak Khemani,
2 nptel.ac.in -Introduction to machine learning – BalaramanR avindran, IIT
Madras
3 Tutorial point.com/machine_learning_with_python/index.htm
Assessment :
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any oth er measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment con sists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire sy llabus wherein 4 sub -
questions of 5 marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 61

Course
Code Course Name Teaching Scheme Credits Assigned
MCA23 Information
Security Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 -- 80 100
Pre-requisite: Computer Networks
Course Objectives: The course aim to
Sr. No. Course Objective
1 Understand the concepts of Information Security, cryptography and its applications
2 Familiarize various authentication and integrity techniques available
3 Understand firewalls and intrusion detection systems .
4 ● Familiarize relevant security parameters in the web, internet, database and operating
systems

Course Outcomes: On successful completion of course learner/student will be able to
Sr. No. Outcome Bloo m Level
CO1 Discuss the requirement of information security , private and public
key algorithms and to examine the mathematics of cryptography Understanding
CO 2 Analyze authentication and integrity techniques available Analyz ing
CO 3 Interpret the i mportance of firewalls and intrusion detection systems
and signatures. Understanding
CO 4 Relate to the security issues and technologies used in the web, internet,
database and operating system Understanding

Module
No. Detailed Contents Hrs
1 Introduction :
Introduction to Information Security, principles, services and attacks,
functional requirements of security, current trends in security 03

Page 62

Self learning topics:
Need for security, Security approaches
2 Cryptography and Authentication:
Cryptography :Concept: Symmetric and Asymmetric Cryptography.
Mathematics of cryptography: Modular Arithmetic Additive Inverse,
Multiplicative Inverse, Euclidean Algorithm and Extended Euclidean
Algorithm.
Stream Cipher and Block Cipher, Concept of Confusion and Diffusion.
Modes of Operation of Block Cipher: ECB, CBC, OFB, CFB , DES, RSA,
Numerical on RSA
Authentication :Types of authentication, Biometric Authentication and
Third Party Authentication using KDC and Kerberos Version 5, Mutual
authentication ,re flection attack
Self learning topics:
Variations of DES – 2DES and 3DES , Symmetric and Asymmetric Key
Cryptography together 10
3 Digital certificates and integrity
Digital Signature :Concept, Compare Digital Signature with Public Key
Cryptography, Digit al Signature Schema.
Public Key Infrastructure (PKI): Private key management, Public Key
Cryptography Standards (PKCS). Digital Certificate Creation Steps,X.509
Certificate, Certificate Revocation
Integrity : Message Integrity, Hash functions Properties
Algorithm:MDC,MAC,HMAC,MD5, SHA -512
Self learning topics:
PKIX model, Data integrity threats 8
4 Internet and web security
SSL, IPSec, Email Security - PGP, Email attacks
Web services Security: web app versus web service concept, WS -Security,
SOAP web ser vice, SAML assertion, Browser attacks, web attacks targeting
users, obtaining user or website data.
Self learning topics:
SET , SSL Vs SET, S/MIME 8

Page 63

5 Firewall and IDS
Firewall : Introduction, Characteristic ,Types :Packet Filter, Stateful and
Stateles s Packet Filter, Attacks of Packet Filter, Circuit Level and
Application Level Firewall, Bastion Host, Firewall Configurations.
Intrusion: What is Intrusion, Intruders, Intrusion Detection, Behavior of
Authorized user and Intruder, Approaches for Intrusion Detection: Statistical
Anomaly Detection and Rule based Detection. Audit Record and Audit
Record Analysis.
Self learning topics:
Virtual Private Network (VPN) 6
6 Database and OS Security
Introduction to database, Security requirements of database, se nsitive data,
Database access control, inference, Security in operating systems: Operating
System Structure, Security Features of Ordinary Operating Systems,
Operating System Tools to Implement Security Functions, Rootkit: Phone
Rootkit, Sony XCP Rootkit, TDSS Rootkits .
Self learning topics:
Cryptographic Toolkits, Denial of Service attack 5
Reference Books :
Reference
No Reference Name
1 AtulKahate, “Cryptography and Network Security”, McGraw Hill
2 Kaufman C., Perlman R., and Speciner, “Network Secu rity”, Private
Communication in a public world, 2nd ed., Prentice Hall PTR.,2002
3 Cryptography and Network Security, Behrouz A Forouzan
4 Cryptography and Network Security: Principles and Practice, William Stallings
5 Computer Security :William Stallin gs , Edition 6
6 Security in Computing fifth edition Charles P. Pfleeger Shari Lawrence Pfleeger
Jonathan Margulies
7 Network Security sand Cryptography: Bernard Menezes, CENGAGE Learning
8 Network Security bible, Eric Cole
9 Cryptography And Informat ion Security, V. K. Pachghare

Page 64

10 Information Systems Security: Security Management, Metrics, Frameworks and Best
practices: Nina Gobole
11 The complete refere nce Information Security by Mark Rhodes -ousley
Web References:
Reference No Reference Name
1 https://link.springer.com/content/pdf/10.1007%2F978 -1-4302 -6383 -8_16.pd f
2 docs.oracle.com/cd/B19306_01/server.102/b14220/security.htm
3 https://www.w3.org/Security/security -resource
4 https://www.sophos.com/en -us/labs/security -threat -report.aspx
5 https://www.tutorialspoint.com/cryptography/data_integrity_in_cryptography.htm
6 https://www.unf.edu/public/cop4610/ree/Notes/PPT/PPT8E/CH15 -OS8e.pdf
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requir ement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Interna l Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
End Semester Theory Examination:

1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Questi on No: 01 will be compulsory and based on entire syllabus wherein 4 sub -questions of
5 marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weight age of each module will be proportional to number of respective lec ture hours as
mentioned in the syllabus.

Page 65

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE241 Image
Processing Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Tota l CA Test AVG
20 20 20 -- 80 100
Pre-requisite:
 Probability and Statistics
 Linear algebra, Differential equation and Calculus
 Basic Programming Skills
Course Objectives : Course aim to
Sr.No. Course Objectives
1 Study the fundamental conc epts of Digital Image processing and to
discuss mathematical transforms .
2 Study image enhancement techniques and explore DCT and DFT techniques
3 Expose students to various image enhancement, restoration methods and
morphological operations.
4 Analyze Image Data Compression and morphological Operation

5 Explain various Applications of Image Processing
Course Outcomes:
Sr.No. Course Outcomes Bloom
Level
CO1 Explain the fundamental concepts of a digital image processing
System
Understanding
CO 2 Apply techniques for enhancing digital images Applying
CO3 Examine the use of Fourier transforms for image processing in
the frequency domain Analyzing
CO4 Compare various Image compression standards and
morphological Operation Analyzing
CO5 Identify various Applications of Image Processing Understanding

Module Detailed Contents Hrs
1 Introduction to Image Processing Systems:
Image representation, basic relationship between pixels, elements of DIP system,
elements of visual perception -simple image formation model Vidicon and Digital
Camera working principles Brightness, contrast, hue, saturation, mach band effect,
Colour image fundamentals -RGB, CMY, HSImodels 2D sampling,quantization.
Self Learning Topic: Image acquisition techniques used in a digital camera, Structure
of a 24 -bit bmp colour image. 6

Page 66

2 Image Enhancement in the Spatial domain:
Spatial domain methods: point processing - intensity transformations, histogram
processing, image subtraction, image averaging Spatial filtering - smooth ing filters,
sharpening filters Frequency domain methods: low pass filtering, high pass filtering,
homomorphic filter.
Self Learning Topic: Interpretation of various image attributes by plotting their
histograms , Applications of filters in various domains .
7
3 Discrete Fourier Transform:
Discrete Fourier Transform: Introduction , DFT and its properties, FFT algorithms ñ
direct, divide and conquer approach, 2 -D DFT &FFT Image Transforms : Introduction
to Unitary Transform, DFT, Properties of 2-D DFT, FFT, IFFT, Walsh transform,
Hadamard Transform, Discrete Cosine Transform, Discrete Wavelet Transform: Haar
Transforms, KL Transform
Self Learning Topics: Signals, Fourier Transform, Color space and Transformation.
8
4 Image Restoration and Image Segmentation :
Image degradation, Classification of Image restoration Techniques, Image
restoration Model, Image Blur, Noise Model : Exponential, Uniform, Salt and
Pepper, Image Restoration Techniques : Inverse Filtering, Average Filtering,
Median Filtering. The detect ion of discontinuities - Point, Line and Edge
detections: Prewit Filter, Sobel Filter, Fri -Chen Filter Hough Transform,
Thresholding Region based segmentation Chain codes, Polygon approximation,
Shape numbers.
Self Learning Topics: Difference between ima ge enhancement and restoration/
The use of motion in Segmentation.
8
5 Image Data Compression and morphological Operation:
Need for compression, redundancy, classification of image compression schemes,
Huffman coding, arithmetic coding, dictionary base d compression, transform Based
compression, Image compression standards - JPEG &MPEG,vector quantization,
wavelet based image compression. Morphological Operation: Introduction, Dilation,
Erosion, Opening, Closing
Self-Learning Topics: Image File format, Mo rphological filters for gray -level
images.
7
6 Applications of Image Processing:
Case Study on Digital Watermarking, Biometric Authentication (Face, Finger Print,
Signature Recognition), Vehicle Number Plate Detection and Recognition, Object
Detection u sing Correlation Principle, Person Tracking using DWT, Handwritten and
Printed Character Recognition, Contend Based Image Retrieval, Text Compression.
Self-Learning Topics: Industrial applications. 4
Reference Books :

Reference
No Reference Name
1 R.C.Gonzalez&R.E.Woods, Digital Image Processing, Pearson Education,
3rd edition, ISBN. 13:978 -0131687288

Page 67

2 S. Jayaraman Digital Image Processing TMH (McGraw Hill) publication,
ISBN - 13:978 -0-07- 0144798
3 Gonzalez, Woods & Steven, Digital Image Pr ocessing using MATLAB, Pearson
Education, ISBN -13:978 -0130085191
4 William K. Pratt, “Digital Image Processing”, John Wiley, NJ,
4th Edition,200
5 Sid Ahmed M.A., “Image Processing Theory, Algorithm andArchitectures”,
McGraw -Hill, 1995.Um baugh, “Computer Vision”.
6 Anil K.Jain,Fundamentals of Digital Image Processing,Prentice Hall of India,2nd
Edition,2004.
Web References:
Reference no Reference name
1 https://
www.ict.gnu.ac .in › sites › files › subject syllabus pdf

2 https:// www.gtu.ac.in › syllabus › sem7
3 https://
www.nptel.ac.in › content › syllabus_pdf

4 https://
www.ktuweb.com › page_showdoc › dopage=syllabus

5 https://www.cse.iitkgp.ac.in › syllabus

6 https://
www.vit.edu › images › btech_syllabus_entc_16 -17

7 https://
www.iitkgp.ac.in › fac-profiles › showprofile
8 https://
www.cse.iitb.ac.in › ~ajitvr › cs663_fall2018

Assessment :
Continuous Assessment: 20 marks
Follow ing measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The fa culty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test sh all be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carryin g 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -questions of
5 marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of eac h module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 68

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE24
2 Internet of
Things Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 -- 80 100

Pre-requisite: 1. Knowledge of Computer Networks.
2. Basics of Cloud.
Course Objectives: The Course aim to
Sr.No. Course Objective
1  Explain the b asics of IoT, M2M, IoT enabling technologies, characteristics of IoT
systems and IoT levels.
2 Explain different state of art IoT reference models and architectures as well as
Architecture Reference Model (ARM) for IoT.
3 Explain the IoT protocols, IoT s ecurity aspects and generic design methodology.
4 Discuss IoT applicability in various domains along with the concept of Web of
Thing (WoT) and Cloud of Thing (CoT).
Course Outcomes: On successful completion of course learner/student will be able to
Sr.N o. Outcome Bloom Level
CO1 Compare M2M and IoT; discuss applicability of IoT enabling
technologies, characteristics of IoT systems and IoT levels. Understanding
CO 2 Explain different state of art IoT reference models and
architectures as well as Archite cture Reference Model (ARM) for
IoT Understanding
CO 3 Analyze various protocols for IoT, IoT security aspects and
generic design methodology Analyz ing
CO 4 Develop cloud based and web based IoT Model for specific
domains. Applying

Page 69

Module Detailed Contents Hr
s
1 Module: Introduction to IoT and M2M :
 Definition & Characteristics of IoT
 Physical Design of IoT - Things in IoT
 Logical Design of IoT - IoT Functional Blocks , IoT Communication
Models , IoT Communication APIs
 IoT Enabling Technologies - Wireless Sensor Networks , Cloud
Computing, Big Data Analytics, Communication Protocols, Embedded
Systems
 IoT Levels & Deployment Templates - IoT Level -1 ,IoT Level -2, IoT
Level -3
 M2M to IoT – The Vision :
 Introduction
 From M2M to IoT
o A brief background ,
o M2M communication,
o Differing characteristics
Self Learning Topics:
 IoT Level -4, IoT Level -5, IoT Level -6
 M2M to IoT – A Market Perspective
 M2M to IoT – An Architectural Overview 6
2 Module: IoT Architecture :
 Introduction
 State of the Art
o European Teleco mmunications Standards Institute M2M/oneM2M
o International Telecommunication Union Telecommunication sector
view
o Internet Engineering Task Force architecture fragments
o Open Geospatial Consortium architecture
 Architecture Reference Model
o Introduction,
o Refe rence model and architecture,
o IoT reference model
 IoT domain model, Information model, Functional model,
Communication model, Safety, privacy, trust, security model
o IoTReference Architecture
 Introduction, Functional view, Information view,
Deployment and operational view
Self Learning Topics: Other relevant architectural views 12
3 Module: IoT Protocols and Security:
 IoT Protocols :
Protocol Standardization for IoT Efforts, M2M and WSN Protocols,
SCADA and RFID Protocols, Issues with IoT Standardization, Unified
Data Standards, Protocols – IEEE 802.15.4, BACnet Protocol, Modbus, 6

Page 70

KNX, Zigbee Architecture, Network layer, APS layer.
 IoT Security:
Need for IoT Security,IoTVulnerabilities, Elements of IoT Security, IoT
Security best practices, Threat Modelin g an IoT system
Self Learning Topics: Basics of Internet Protocols, Basic understanding of
cryptography
4 Module: IoT Platform Design Methodology:
 Purpose and requirement specification
 Process specification
 Domain model specification
 Information model specification
 Service specifications
 IoT level specification
 Functional view specification
 Operational view specification
 Device and component integration
 Application development
Self Learning Topics: Basics of DFD, UML Modeling 4
5 Module: Domain Specifi cIoTs :
 HomeAutomation
o Smart Lighting
o Smart Appliances
o Intrusion Detection
o Smoke/Gas Detectors
 Cities
o Smart Parking
o Smart Lighting
o Smart Roads
o Structural Health Monitoring
o Surveillance
o Emergency Response
 Environment
o Weather Monitoring
o Air Pollution Monitoring
o Noise Pollution Monitoring
o Forest Fire Detection
o River Floods Detection
 Energy
o Smart Grids
o Renewable Energy Systems
o Prognostics
 Retail
o Inventory Management
o Smart Payments
o Smart Vending Machines
 Agriculture
o Smart I rrigation
o Green House Control
 Industry 6

Page 71

o Machine Diagnosis & Prognosis
o Indoor Air Quality Monitoring
Self Learning Topics: Case Study on Logistics and Health & Lifestyle
6 Module: Web of Things and Cloud of Things:
 Web of Things: Web of Things v ersus Internet of Things, Two Pillars of
the Web, Architecture Standardization for WoT, Platform Middleware for
WoT, Unified Multitier WoT Architecture, WoT Portals and Business
Intelligence.
 Cloud of Things: Grid/SOA and Cloud Computing, Cloud Middlewar e,
Cloud Standards – Cloud Providers and Systems, Mobile Cloud
Computing, The Cloud of Things Architecture.
Self Learning Topics: Basics of Web and Cloud. 6
Reference Books :

Reference
No Reference Name
1 From Machine -to-Machine to the Internet of Things : Introduction to a New Age of Intelligence,
Jan Holler,VlasiosTsiatsis, Catherine Mulligan, Stefan Aves and StamatisKarnouskos, David
Boyle, ELSEVIER
2 Vijay Madisetti and ArshdeepBahga, “Internet of Things (A Hands -on-Approach)”, 1 st Edition,
VPT, 2014
3 IoT Security for Dummies, Lawrence Miller, John Wiley & Sons Ltd.
4 Practical Internet of Things Security, Brian Russell, Drew Van Duren, PACKT
publishing
5 The Internet of Things in the Cloud:A Middleware Perspectiv,By Honbo Zhou
6 Rethinking the Internet of Things A Scalable Approach to Connecting Everything, Francis
daCosta, Apress
7 Getting Started with the Internet of Things, CunoPfister
8 The Internet of Things: Connecting Objects,HakimaChaouchi
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
End Semester Theory Exa mination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -questions of
5 marks each will be asked.
4. Remaining que stions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 72

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE24
3 Robotic
Process
Auto mation Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 -- 80 100

Pre-requisite: 1. Software Engineering
2. Basics of Computer Science
Course Objectives: The course aim to
Sr. No. Course Objective
1 Explain the concepts of Robotic Process Automation
2 Explain the process methodologies for BOT development
3 Apply knowledge BOT development for intelligent automation
4 Explore various RPA tools with their specifications
Course Outcomes: On successful completion of course learner/student will be able to
Sr. No. Course Outcome Bloom Level
CO1 Define the key concepts of Robotic Process Automation and
evolution. Remembering
CO2 Demonstrate development of BOT with specific tools Understanding
CO3 Apply RPA implementation cycle considering security and scaling Applying
CO4 Examine specifications of RPA tools and justify applications of
appropriate tool for problem. Analyzing
CO5 Assess perf ormance of BOTs in context of intelligent automation Evaluating

Page 73

Module
No. Detailed Contents Hr
s
1 Module: Introduction to RPA:
 What is RPA,
 Flavors of RPA,
 History of RPA,
 Benefits of RPA,
 Current Status of RPA Utilisation and Value,
 Levels of RPA,
 Skills Required for RPA,
 RPA Lifecycle,
 RPA Use Cases
Self Learning Topics:
 Evolution of RPA,
 RPA compared to BPA, BPM and BPO, 5
2 Module: Process Methodologies and Planning:
 Lean,
 Six Sigma,
 Applying Lean and Six Sigma to RPA,
 Planning:ROI for RPA,
 Agile technology for RPA
 Relationship between RPA and Workload Automation
Self Learning Topics: Other relevant architectural views 5
3 Module: BOT Development
 Analysis of Business Process and development of BOT,
 Activities, Flowcharts and seque nces, Log Message, loops and conditions,
 Common UIPath Functions,
 Best practices for BOT Development,
 Evaluating BOT Performance
 Error Handling
Self Learning Topics: Learning UiPath Studio 8
4 Module: Deployment, Monitoring and Data Preparation for RPA
 Testing, Monitoring
 Type of Data for RPA,
 Data Process and Types of Algorithms,
 Managing RPA Implementation Cycle
Self Learning Topics: Security, Scaling for RPA 6

Page 74

5 Module: Intelligent Automation & BOT Management
 Cognitive Automation,
 Intelligent Pro cess Automation or IPA,
 Examples of cognitive RPA
 Web Scrapping
 Types of BOTs,
 Examples of BOTs,
Self Learning Topics: Difference between RPA BOT and ChatBOTs 6
6 Module: Security of BOT:
 Security Challenges for RPA
 Secured BOT Development and Secured BOT Deployment,
 secured BOT architecture design,
 security requirements through threat modeling
Self Learning Topics: Risks for RPA. 2
7 Module: RPA Technologies & Case Studies
 RPA Tools: UIPath, BluePrism, WorkFusion, Nice,
 Open Source RPA ,
 Resilient Automation
 Case studies of RPA implementation
Self Learning Topic: RPA Best Practice
8

Reference Books :

Reference
No Reference Name
1 Tom Taulli , “The Robotic Process Automation Handbook: A Guide to
Implementing RPA Systems”, 1st Edition, 2019
2 Mathias Kirchmer, Peter Franz and Danny Bathmaker, “Value -Driven Robotic
Process Automation Enabling Effective Digital Transformation” , October 2019
3 Richard Murdoch, “Robotic Process Automation: Guide to Building Software
Robots, Automate Repetitive Tas ks and Become an RPA Consultant”, May 2018.
4 GerardusBlokdyk , “Robotic Process Automation Rpa A Complete Guide - 2020
Edition”, 1st Edition, 5STARCooks, 2019 .
5 Alok Mani Tripathi, “ Learning Robotic Process Automation”, 2018, Packt
Publishing
6 Lim Mei Ying, “Robotic Process Automation with Blue Prism Quick Start Guide”,
November 2018, Packt Publishing
Web References:
Reference
No. Reference Name
1 https://resources.automationanywhere.com/articles

Page 75

2 https://www.automationanywhere.com/in/solutions
3 https://www.infobeans.com/robotic -process -automation -lifecycle
4 https://university.automationanywhere.com/rpa -courses/
5 https://www.uipath.com/blog/the -evolution -of-rpa-past-present -and-future
6 https://university.automationanywhere.com/rpa -learning -trails/business -analyst/
7 https://www.chatbot.com/blog/6 -types -of-bots-that-can-serve -your-clients/
8 https://university.automationanywhere.com/?ReturnUrl=%2fcourse%2f1324366%2fmodule%2f371981
8%2fScorm%3fLPId%3d0&LPId=0
9 https://university.automationanywhere.com/?ReturnUrl=%2fcourse%2f1324366%2fmodule%2f371981
8%2fScorm%3fLPId%3d0&LPId=0
10 https://university.automationanywhere.com/rpa -learning -trails/automation -anywhere -university -
essential -level -prep-courses -mba-students/
11 https://university.automationanywhere.com/rpa -learning -trails/automation -anywhere -university -
essential -level -prep-courses -mba-students/
12 https://university.automationanywhere.com/rpa -learning -trails/technical -support -specialist/
13 https://university.automationanywhere.com/rpa -learning -trails/automation -anywhere -secure -bot-
developer/
14 https://w ww.onesourcevirtual.com/resources/blogs/technology -and-
innovation/prepare -for-robotic -process -automation -with-lean-six-sigma.html
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Qui z /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the sub ject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be c ompulsory and based on entire syllabus wherein 4 sub -questions
of 5 marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
menti oned in the syllabus.

Page 76

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE24
4 Computer
Vision Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 -- 80 100
Pre-requisite: Basic Understanding of Computer Graphics and Image Processing
Course Objectives: The course aim to
Sr. No. Course Objective
01 Learn basic concepts and applications of computer vision.
02 Learn image processi ng techniques
03 Use and implement feature detection mechanism
04 Understand advanced concepts leading to object and scene categorization from
images.
Course Outcomes: On successful completion of course learner/student will be able to
Sr.No. Course Outc ome Bloom Level
CO1 Explain Concepts and Applications of Computer Vision Understanding
CO2 Apply image processing techniques to design Computer Vision
applications Applying
CO3 Implement algorithms of face recognition and motion detection Analyz ing
CO4 Provide solutions to real world computer vision problems Creat ing

Module
No. Detailed Contents Hrs.
1 Module: Introduction to Computer Vision
Definition of Computer Vision, Easy Vs Hard Problems, Computer Vision
System, Components of a vision system, Applications of Computer vision,
Image Sources for computer Vision, Image structure and Pixels, Frameworks
for Computer Vision 06

Page 77

Self Learning Topics: Computer vision in Finance
2 Module: Basic Image Handling and Processing
Geometric primitives and tra nsformations, Plotting images, points and lines,
Image contours and histograms, Histogram equalization, Interactive
annotation, Gray level transforms, Image Transformations, Image
Derivatives
Self Learning Topics: Image Denoising 07
3 Module: Local Image D escriptors and Image Mappings
Line Detection -Hough Transforms, Harris corner detector, Edge Detection, SIFT -
Scale -Invariant Feature Transform, Matching Geotagged Images, Homographies,
Warping images, Creating Panoramas :Camera Models and Augmented reali ty,
Light effects
Self Learning Topics: Drawing on Images 07
4 Module: Exploring Structure from Motion
Structure from Motion concepts, Estimating the camera motion from a pair of
images, Reconstructing the scene , Reconstruction from many views , Refinemen t
of the reconstruction, Visualizing 3D point clouds, Object Recognition and Bag -of-
Words Models
Self Learning Topics: Object Classification 07
5 Module: Face Detection and Tracking
Face detection, Pedestrian detection, Face recognition, Eigenfaces, Viola -Jones
Algorithm, Haar -like Features, Integral Image, Training Classifiers, Adaptive
Boosting (Adaboost)
Self Learning Topics: Measuring features 07
6 Module: Convolutional Nerual Networks for CV
CNN Advantages,Architecture,Layers,TrainingCNNs,Build your o wn CNN,CNN
applications
Self Learning Topics: Dogs and cats case study 06

Reference Books :
Reference
No. Reference Name
1 Szeliski, Richard. Computer vision: algorithms and applications. Springer Science &
Business Media, 2010. ISBN: 1848829345
2 Solem, Jan Erik. Programming Computer Vision with Python: Tools and algorithms
for analyzing images. " O'Reilly Media, Inc.", 2012.ISBN: 144934193
3 Demaagd, Kurt. Practical Computer Vision with SimpleCV: Making Computers See
in Python. 2012.ISBN: 9781449337865

Page 78

4 Jähne, Bernd, Horst Haussecker, and Peter Geissler, eds. Handbook of computer
vision and applications. Vol. 2. San Diego: Academic press, 1999.ISBN:
0123797713
5 Jähne, Bernd, and Horst Haußecker. "Computer vision and applications." A Guide
for Studen ts and Practitioners (2000). ISBN: 7302269157
6 Baggio, Daniel Lélis. Mastering OpenCV with practical computer vision projects.
Packt Publishing Ltd, 2012.ISBN: 1849517827
7 Khan, Salman, et al. "A guide to convolutional neural networks for computer
visio n." Synthesis Lectures on Computer Vision 8.1 (2018).ISBN: 1681730219
Web References:
Reference No. Reference Name
1 http://groups.csail.mit.edu/vision/
2 https://medium.com/readers -writers -digest/beginners -guide -to-computer -vision -23606224b720
3 https ://vision.in.tum.de/research
4 Deeplearning.ai
5 http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html
Assessment :
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Ca se studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (o ut of 20) will be the average of the Class test and the
Continuous Assessment.
End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compul sory and based on entire syllabus wherein 4 sub -questions of
5 marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours a s
mentioned in the syllabus.

Page 79

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE24
5 Embedded
Systems Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 -- 80 100
Pre-requisite:
 Computer Organisation & Architecture
 Operating Systems
 C / C++ / Java Programming
Course Objectives : The course aim to
Sr.No. Course Objective
1 Introduce students to the features, applications of embedded systems
2 Develop an understanding of the design challenges of embedded systems
3 Understand the basic architecture of 8051 microcontroller
4 Introduce students to Embedded C programming
5 Enable students to develop basic programs for embedded systems using Em bedded
C.
Course Outcomes: On successful completion of course learner/student will be able to
Sr.No. Outcome Bloom Level
CO1 Explain hardware and software design requirements of Embedded
Systems Understanding
CO 2 Discuss the architecture of 8051 proce ssor Understanding
CO 3 Describe 8051 Processor Addressing modes and instruction sets Understanding
CO 4 Use Embedded C for writing basic programs for embedded
systems Applying
CO 5 Examine the use of various Embedded C programming constructs
for writi ng programs for embedded systems. Analys ing

Page 80

Module Detailed Contents Hrs
1 Fundamentals of Embedded Systems :
Introduction, Features, Applications of Embedded Systems, Subsystems in an
Embedded System.
Design Considerations of Embedded Systems : De sign Challenges, Common
Design Metrics, Design Trade offs and Performance
Classification of Computer Architecture : Basic operation of a computer
system, CPU Architecture, Microprocessor, Microcomputer.
Introduction to Real Time Operating Systems
Self lear ning topics; Embedded systems classifications and their usage. 7
2
Microcontrollers :
Evolution and Uses in Embedded Systems and its Advantages.
Architecture of 8051 Microcontroller : Introduction, Block Diagram, Registers,
Internal Memory, Counters, I/O Ports, Basic Concepts in Serial I/O
Self learning topics: Overview of ATOM / ARM Processor 6
3 8051 Processor Addressing modes and Instruction Set :
Assembly language programming in 8051, Data Types, Addressing Modes,
Arithmetic and Logical Operators
Interfacing 8051 with external devices : LED‟s and SSD.
Self learning topics: Operating systems used for designing embedded
systems 6
4 Embedded C Programming : Introduction :
Introduction to Embedded C, Difference between C & Embedded C,
Programming style , Basic structure of the program.
Keywords & Identifiers, Data type & its memory representation, Arrays and
strings , Input and Output.
Self learning topics; Practice programming Exercises 6
5 Embedded C Programming : Operators and control structures :
Types of Operators, Bitwise Operators
Decision making with if statement, If….else statement, Switch statement, and
GOTO statement, The While and Do – While statements, For statement
Self learning topics: Practice programming Exercises 6

Page 81

6 Embedded C Prog ramming : Functions :
Why Functions, Types of Functions, A Multi functional program, Return
values & their types.
Self learning topics : Practice programming Exercises 5
7 Case Study : Use of Embedded systems is designing various commercial
applications / appliances : Home Automation Systems, Washing machine etc. 4
Reference Books :

Reference
No Reference Name
1 K V K Prasad, “Embedded/Real Time Systmes : Concepts, Design and
Programming”, Dreamtech Press
2 Steve Furber, “ARM System -on-chip Architect ure”, 2e, Addison Wesley
3 Tammy Noergaard, “Embedded System Architecture : A comprehensive Guide
for Engineers and Programmers”, Newnes (Elsevier)
4 Shibu K V, “Introduction to Embedded Systems”, Tata McGraw Hill
5 Raj Kamal, “Embedded Systems : Archit ecture, Programming and Design”, 2e,
Tata McGraw Hill
6 K Uma Rao, AndhePallavi, “The 8051 and MSP430 Microcontrollers :
Architecture, Programming and Applications, Wiley
7 Bahadure, Chandrakar, “ Microcontrollers and Embedded System Design”,
Wiley
8 Raj Kamal, “Embedded Systems : Architecture, Programming and Design”,
Tata McGraw Hill.

Web References: ww.nptel.ac.in
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case st udies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsor y and based on entire syllabus and may have 4 sub -questions
of 5 marks each.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the sylla bus.

Page 82

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE25
1 Natural
Language
Processing Contact Hours
Theory Tutorial Theory Tutorial Total
3 1 3 1 4
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 25 80 125
Pre-requisite:
 Probability
 Python programming
 Data structure &
Algorithms

Course Objectives : The course aim to
Sr.No. Course Objective
1  Understand natural language processing and to learn how to apply basic
algorithms in this field .
2 Get acquainted with the basic concepts and algorithmic description of the main
language levels: morphology, syntax, semantics, and pragmatics.
3 Implement a rule based system to tackle morphology/syntax of aLanguage
4 Compare and contrast use of dif ferent statistical approaches for different types of
applications
5 Design a tag set to be used for statistical processing keeping an application in
mind, design a Statistical technique for a new application
6 Design an innovative application using NLP c omponents

Course Outcomes: On successful completion of course learner/student will be able to
CO No. Outcome Bloom Level
CO1 Understand the computational properties of natural languages and
the commonly used algorithms for processing linguistic
informa tion. Understanding
CO 2 Understand the information retrieval techniques using NLP Understanding
CO 3 Apply mathematical techniques that are required to develop NLP
applications. Applying

Page 83

CO 4 Analyze various NLP algorithms and text mining NLP applicati ons Analyz ing
CO 5 Design real world NLP applications such as machine translation,
text categorization, text summarization, information extraction by
applying NLP techniques. Creat ing

Module
No. Detailed Contents Hrs
1 Module: Introduction
History of N LP, Generic NLP system, levels of NLP , Knowledge in
language processing , Ambiguity in Natural language , stages in NLP,
challenges of NLP ,Applications of NLP
Self learning topics: Empirical laws 4
2 Module: Word Level Analysis
Morphology analysis –survey of English Morphology, Inflectional
morphology & Derivational morphology, Lemmatization, Regular
expression, finite automata, finite state transducers (FST) ,Morphological
parsing with FST , Lexicon free FST Porter stemmer. N –Grams - N-gram
language mod el,
Self learning topics: N-gram for spelling correction 8
3 Module: Syntax analysis
Part-Of-Speech tagging( POS) - Tag set for English ( Penn Treebank ) ,
Rule based POS tagging, Stochastic POS tagging, Issues –Multiple tags &
words, Unknown words. Introdu ction to CFG, Sequence labeling: Hidden
Markov Model (HMM), Maximum Entropy
Self learning topics: Conditional Random Field (CRF). 8
4 Module: Semantic Analysis
Lexical Semantics, Attachment for fragment of English - sentences, noun
phrases, Verb phrases , prepositional phrases, Relations among lexemes &
their senses –Homonymy, Polysemy, Synonymy, Hyponymy , Robust Word
Sense Disambiguation (WSD),Dictionary based approach
Self learning topics: WordNet 10
5 Module: Text Summarization, Text Classification
Text su mmarization - LEXRANK , Optimization based approaches for
summarization , Summarization evaluation , Text classification
Self learning topics: NLK T , Naïve Bayes Theorem 6

Page 84

6 Module: Sentiment Analysis and Opinion Mining
Sentiment Analysis introduction , Se ntiment Analysis - Affective lexicons,
Learning affective lexicons, Computing with affective lexicons, Aspect
based sentiment analysis
Self learning topics: Named Entity Recognition 4
Reference Books :

Reference
No Reference Name
1 Dan Jurafsky and Jam es Martin. “Speech and Language Processing: An
Introduction to Natural Language Processing, Computational Linguistics and
Speech Recognition”, Prentice Hall, Second Edition, 2009.
2 Steven Bird, Ewan Klein, Natural Language Processing with Python, O„Reill y
3 Christopher D.Manning and HinrichSchutze, ― Foundations of Statistical
Natural Language Processing ―, MIT Press, 1999.
4 Siddiqui and Tiwary U.S., Natural Language Processing and Information
Retrieval, Oxford University Press (2008).
5 Daniel M Bike l and ImedZitouni ― Multilingual natural language processing
applications‖ Pearson, 2013
6 Alexander Clark (Editor), Chris Fox (Editor), Shalom Lappin (Editor) ―
The Handbook of Computational Linguistics and Natural Language
Processing ― ISBN: 978 -1-118-
7 Brian Neil Levine, An Introduction to R Programming
8 Niel J le Roux, SugnetLubbe, A step by step tutorial : An introduction into R
application and programming
Web References:
Reference No Reference Name
1 https://youtu.be/xvqsFTUsOmc
2 https://www.coursera.org/learn/language -processing
3 https://nptel.ac.in/courses/106/105/106105158/
4 https://youtu.be/IIaYk2hIYKk
5 https://www.udemy.com/course/natural -language -
processing/ ?trk=profile_certification_title&utm_source=adwords&utm_medium=ud
emyads&utm_campaign=DSA

Page 85

Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other m easure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consist s of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of t he Class test and the
Continuous Assessment.
End Semester Theory Examination:

1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllab us wherein 4 sub -questions of 5
marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Tutorial
Sr.
No. Detailed Content Hours
1 NLP processing of any one Indian regional language 4
2 Web mining using NLP (fetching web pages and
extracting the human language data from them) for
sentiment analysis 4
3 Using NLP to complete analytical tasks such as
generating document abstracts 4

Page 86

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE25
2 Geographic
Information
System Contact Hours
Theory Tutorial Theory Tutorial Total
3 1 3 1 4
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 25 80 125
Pre-requisite: Nil
Course Objectives: The course aim to
Sr.No. Course Objective
1 Understand the concepts of Geographic Information System.
2 Examine the motive of Vector Data Model
3 Discuss the design of R aster Data Model
4 Demonstrate the Terrain Mapping, View shade and Watershed Analysis outline.
5 Apply knowledge of Geocoding and Dynamic Segmentation
6 Determine the available of Remote Sensing Techniques.
Course Outcomes: On Successful completion of course learner/student will be able to
Sr. No. Course Outcome Bloom Level
CO1 Define the key concept of Geographic Information System Remembering
CO 2 Examine the various aspects of vector data model by survey and
discover of concepts. Analyzing
CO 3 Elaborate and estimate raster data model by designing and
developing effective plan. Creating
CO 4 Demonstrate understanding of the Terrain Mapping, View shade
and Watershed Analysis in contrast by explaining main ideas. Understand
CO 5 Experiment of Geoc oding and Dynamic Segmentation by
applying facts and techniques. Applying
CO 6 Present and explain importance of remote sensing by evaluating
recommended set of criteria Evaluating

Module Detailed Contents Hrs.
1 Principles of GIS:

Introduction: Co mponents of GIS, History of GIS, Elements of GIS, Applications
of GIS, Integration of GIS with Web and Mobile Technology 6

Page 87

2 Vector Data Model:
Representation of Spatial Features,
Topology - TIGER, Importance of Topology,
Georelational Data Model -Covera ge, Coverage Data Structure, Sapefile, Object
Based Data Model - Classes and Class Relationships, Interface, Geodatabase,
Topology Rules,
Representation of Composite Features - TIN, Regions, Routes 6
3 Raster Data Model:
Elements of the Raster Data Model - Cell Value, Cell Size, Cell Depth, Raster
Bands, Spatial Reference,
Satellite Images - Landsat, SPOT, Digital Globe, Sentinel, Terra Satellite,
Digital Elevation Models - Optical Sensor, InSAR, LiDAR.
Raster Data Structure - Cell-by-Cell Encoding, Run -Leng th Encoding, Quadtree,
Header File 6
4 Terrain Mapping, Viewshade and Watershed Analysis:
Terrain Mapping - Contouring, Vertical Profiling, Hill Shading, Hypsometric
Tinting, Perspective View,
Viewshade Analysis - Line-of-Sight Operation, Raster -Based Opera tion, Raster -
Based, TIN -Based, Cumulative,
Watershed Analysis - Filled DEM, Flow Direction, Flow Accumulation, Stream
Network, Stream Links, Areawide Watershed, Point -Based Watersheds 6
5 Geocoding and Dynamic Segmentation:
Geocoding, Variations of Geocod ing,
Application of Geocoding - Location Based Services, Business Application,
Wireless Emergency Services, Crime Mapping and Analysis, Public Health,
Dynamic Segmentation - Routes, Events,
Applications of Dynamic Segmentation: Data Management, Data Displ ay, Data
Query, Data Analysis 6
6 Principles of Remote Sensing:
Introduction: Definitions, concepts and types of remote sensing, evolution, stages
and advantages of remote sensing, spatial data acquisition, Electromagnetic
spectrum, electromagnetic radiat ion, wavelength regions of electromagnetic
radiation, types
Remote Sensing Technologies: Thermal Remote Sensing – Thermal radiation
principles; Precision remote sensing – spatial, spectral and temporal precision;
Passive and Active Microwave Remote Sensing
Applications of Remote Sensing: Applications of remote sensing in agriculture.
Applications of remote sensing in forestry, Applications of remote sensing in
oceans and coastal monitoring. 10
Reference Books :

Reference
No. Reference Name
1 Remote Sensi ng and GIS - Bhatta B, Oxford
2 Integrated Coastal and Ocean Management – BilianaCicin -Sain Gunnar Kullenburg, Island Press
3 Remote Sensing and GIS – Anji Reddy, BS Publication
4 Introduction to Geographic Information Systems - Tsung Chang Kang , McGra w-Hill
5 An Introduction to Geographical Information Systems D. Ian Heywood , Sarah Cornelius , Steve Carver ,
Pearson Prentice Hall, 2006

Page 88

6 Concepts And Techniques Of Geographic Information Systems 2nd Edition by Chor Pang
Lo (Author), Albert K.W. Yeung (Author). PHI
Web References:
Reference No. Reference Name
1 https://www.nationalgeographic.org/encyclopedia/geographic -information -system -gis/
2 https://gisg eography.com/satellite -maps/
3 https://www.esri.com/en -us/what -is-gis/overview
4 https://gisgeography.com/gis -applications -uses/
5 http://sparcindia.com/gis -case-studies/
6 https://gisgeography.com/geoprocessing -tools/
7 https://bharatmaps.gov.in/
GIS: Tutorials
Sr. No. Detailed Contents Hrs.
1 Subject Instructor/Mentor can form the groups of the students and they can
discuss the case studies with the guidance of Instructor/Mentor to understand
and explore the GIS Tools and Techniques
12 2 Case stu dies can be chosen in the area like application of GIS in Industrial
Establishments, Mining, Forestry and Environment, Power Utilities, Pipeline
Utilities, Transportation and Logistics, Water Resources, Agriculture,
Governance, Risk Management, Education H ealthcare.
3 Presentations based on various GIS concepts can also be included in
discussion to understand the application of GIS in real world.
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requi rement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Intern al Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
Term Work : 25 marks
The term work will be based on the tutorial performance of the student .
End Semester Theory Examination:
1. Question paper will compri se of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -questions of 5
marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 89

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE253 Design and
Analysis of
Algorithm Contact Hours
Theory Tutorial Theory Tutorial Total
3 1 3 1 4
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 25 80 125
Pre-requisite: MCAL11 - Data Structures Lab using C/C++
Course Objectives: The course aim to
Sr.No. Course Objective
1  Analyze asymptotic notations
2 Analyze various problem solving techniques
3 Analyze different algorithmic design paradigms.
4 Interpret approximation algorithms.
Course Outcomes: On Successful Completion of course learner/students will be able to
Sr.No. Outcome Bloom Level
CO1 Analyze the time and space complexity of various algorithms. Analyz ing
CO2 Analyze divide and conquer, greedy and dynamic programming
strategies. Analyz ing
CO3 Analyze backtracking, branch and bound and s tring matching
algorithm. Analyz ing
CO4 Explain NP hard NP complete problem. Understanding

Module
No. Detailed Contents Hrs
1 Module: Introduction :
Notion of an Algorithm , Fundamentals of Algorithmic Problem Solving,
Fundamentals of the Analysis of A lgorithmic Efficiency, Asymptotic
Notations and their properties. Analysis Framework, Mathematical
analysis for Recursive and Non -recursive algorithms, Substitution
method and growth of function.
06
2 Module:Divide And Conquer :
Divide and Conquer Methodo logy, Binary Search, Merge sort, Quick
08

Page 90

sort, Heap Sort, Multiplication of Large Integers, Closest - Pair and
Convex - Hull Problems
Self Learning Topics: Optimal storage on tape
3 Module: Greedy Technique :
Introduction, Control Abstraction for Greedy Al gorithms, Fractional
knapsack, Minimum cost spanning tree(Kruskal, Prims) , Single source
shortest path (Dijkstra‟s algorithm)
Self Learning Topics: Huffman Coding
06
4 Module: Dynamic Programming:
Introduction, Control Abstraction for Dynamic Programmi ng, Knapsack
(0/1), Matrix chain multiplication, Longest common subsequence, All
pair shortest path (Floyd Warshall)
Self Learning Topics: DFS and BFS
06
5 Module: Backtracking And Brach And Bound:
Introduction to Backtracking, n -Queen problem, Graph c olouring
problem, Hamiltonian cycle.
Branch and Bound, LIFO Search and FIFO search, Least cost search, 15
puzzles, Travelling Salesman Problem.
Self Learning Topics: Subset Sum Problem
08
6 Module: Approximation Algorithms:
NP Hard and NP –complete probl em, set covers,
Natural algorithms
Self Learning Topics: Study of open ended problems
02
7 Module: String Matching:
Brute force string matching, Knutt -Morris -Pratt algorithm, Rabin -Karp
algorithm, Naïve string matching, Finite automata, Boyer Moore
algorithm
Self Learning Topics: Study of applications of string matching
04

Reference Books :

Reference
No. Reference Name
1 Thomas H.Cormen, Charles E.Leiserson, Ronald L. Rivest and Clifford Stein,
―Introduction to Algorithms, Third Edition, PHI Learning Private Limited,
2012 ISBN 978-0-262-03384 -8.
2 AnanyLevitin, ―Introduction to the Design and Analysis of Algorithms, Third
Edition, Pearson Education, 2012 ISBN 978 0 13 231681 1
3 Ellis Horowitz, SartajSahni and SanguthevarRajasekaran - Computer
Algo rithms/ C++, Second Edition, Universities Press, 2007 ISBN :
9788173716126
4 S. Sridhar ―Design of Algorithms and Analysis, Oxford university press, 2014.
5 Alfred V. Aho, John E. Hopcroft and Jeffrey D. Ullman, ―Data Structures and
Algorithms, Pearson E ducation, Reprint 2006
6 Harsh Bhasin, ―Algorithms Design and Analysis, Oxford university press,
2016.
7 Parag H. Dave, Himanshu B. Dave, “Design and Analysis of Algorithms”, 1st
Edition, 2008, ISBN: 8177585959, Pearson Education.

Page 91

Tutorials:
Sr.No. Detailed Contents Hrs
1 Find time complexity of given code (e.g. using substitution theorem, master
theorem etc) 01
2 Problems on recurrence relation 01
3 Comparative analysis of various sorting techniques 02
4 Problem solving on greedy techniques 01
5 Problem solving on dynamic techniques 02
6 Problem solving on backtracking and branch and bound 02
7 Problem solving on string matching 01
8 Case study of trending services (like but not limited to uber, zomato, olx,
nature basket, packers and movers et c) 02
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
Term Work : 25 marks
The term work will be based on the tutorial performance of the student .
End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -
questions of 5 marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 92

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE25
4 Digital
Marketing
and
Business
Analytics Contact Hours
Theory Tutorial Theory Tutorial Total
3 1 3 1 4
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 25 80 125
Pre-requisite: Nil
Course Objectives: The course aim to
Sr.
No. Course Objective
01 Examine and explore the role and importance Digital Marketing in the current business
scenario.
02 Familiarize with the various Digital Marketin g Tools.
03 Apply Digital Marketing tools for formulating a Digital Marketing Strategy.
04 Understand Digital Marketing Campaigns using various Tools and measure their
effectiveness.
Course Outcomes: On successful completion of course learner/student wi ll be able to

Sr.
No. Course Outcome Bloom Level
CO1 Understand the role of Digital Marketing Remembering
CO2 Demonstrate use of various Digital Marketing Tools. Understanding
CO3 Discuss key element of Digital Marketing Strategy. Applying
CO4 Underst and use of Digital Marketing Tools for Digital Marketing
Campaigns Analyzing
CO5 Assess / Measure the effectiveness of the Digital
Marketing Campaigns. Evaluating
CO6 Demonstrate practical skills using common digital marketing tools
like SEO, SEM, Conte nt Marketing… Creating

Module
No. Detailed Contents Hrs.
1 Module: Fundamentals of Digital Marketing:
Digital Marketing. Digital Marketing Strategy. Skills Required in Digital
Marketing, Digital Marketing Plan,
Digital Marketing:
Introduction to Displa y Marketing, Types of Display Ads, Buying
Models, Display Plan, Analytics Tools.
Dignified Digital Marketing – Ethics and Data Privacy
Self Learning Topics: What makes a Good Ad? Programmatic Digital
Advertising, YouTube Advertising 04
2 Module: Search En gine Advertising 05

Page 93

Introduction, Understanding Ad Placement, Understanding AdRanks,
Creating First Ad Campaign, Enhance Your Ad Campaign, Performance
Reports.
Social Media Marketing
Building a Successful Strategy
Facebook Marketing
Facebook Marketing for Bu siness, Anatomy of an Ad Campaign, Adverts,
Facebook Insights, Other Marketing Tools, Other Essentials
Self Learning Topics:
Campaign Management, Running Campaigns, Lead Generation, Qualified
Leads
3 Module: LinkedIn Marketing
Importance of LinkedIn P resence, LinkedIn Strategy, Sales Leads
Generation Using LinkedIn, Content Strategy, LinkedIn Analytics,
Targeting, Ad Campaign
Twitter Marketing
Getting Started with Twitter, Building a Content Strategy, Twitter Usage,
Twitter Ads, Twitter Analytics, Twi tter Tools and Tips for Marketers
Self Learning Topics: 09
4 Module: Instagram
Mobile Marketing
Mobile Usage, Mobile Advertising, Mobile Marketing Toolkit, Mobile
Marketing Features, Campaign Development Process, Mobile Analytics
Self Learning Topics:
Addressing the Diversity in India through Mobile 07
5 Module: SEO
Search Engine, Concept of Search Engine Optimization (SEO), SEO
Phases, On Page Optimization, Off Page Optimization, Social Media
Reach, Maintenance
Self Learning Topics:
SEM 06
6 Module: Web Analytics
Data Collection, Key Metrics, Making Web Analytics Actionable, Multi -
Channel Attribution, Types of Tracking Codes, Mobile Analytics,
Universal Analytics, Competitive Intelligence
Self Learning Topics:
Interpretation of various Charts availa ble in Google Analytics. How to
connect Offline with Online. 08
Refere nce Books :
Referenc
e No. Reference Name
1 Digital Marketing, Seema Gupta, McGraw Hill Education (India) Private Limited
2 Social Media& Mobile Marketing: Includes Online Worksheets Pu neet Singh Bhatia
,ISBN: 9788126578078
3 Digital Marketing for Dummies, Ryan Deiss& Russ Henneberry, John Wiley & Son,
Inc.
4 Social Media Marketing All -In-One, Jan Zimmerman, Deborah Ng, John Wiley & Sons
Inc.

Page 94

5 Epic Content Marketing, Joe Pulizzi, McG raw Hill Education
6 Youtility, Jay Baer, Gildan Media, LLC
7 Hit Makers : The Science Age of Dice of Popularity in an Age of Distraction, Derek
Thompson, Penguin Press
8 The Art of SEO, Eric Enge, Stephan Spencer, Jessie Stricchiola, O‟Reilly Media Inc ,
9 Digital Marketing 2020, Danny Star,
Tutorial
Sr.
No. Detailed Contents Hrs
01 Digital Marketing – Case Study : Ariel Fashion Shoot 01
02 Display Advertising – Case Study : Anything for Jetta 01
03 Search Engine Advertising – Case Study : Kotak Se rvices 01
04 Social Media Marketing – Case Study : The Fall and Rise of Maggie 01
05 Facebook Marketing – Case Study : Tata DoCoMo 01
06 Facebook Marketing – Case Study : ICICI Bank – Building India‟s Most Social
Bank on Facebook 01
07 LinkedIn Market ing – Case Study : Mercedes Benz, DELL 01
08 Twitter Marketing – Case Study : Mercedes -Benz (2011) 01
09 Instagram – Case Study : H & M 01
10 Mobile Marketing – Case Study : Philips Airfryer 01
11 SEO – Case Study : Barclays Business Banking SEO Campai gn 01
12 Web Analytics – Case Study : Conversion Tracking through URL Builder – A
Hotel Brand 01


Note: The Case Studies mentioned above are indicative and not limited to. The Teacher has the flexibility of
taking similar Case Studies taking into consid eration the current scenario and technological changes.

Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks

Page 95

Assessment consists of one class tests of 20 marks. T he Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous As sessment.
Term Work : 25 marks
The term work will be based on the tutorial performance of the student .
End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Quest ion No: 01 will be compulsory and based on entire syllabus wherein 4 sub -
questions of 5 marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lect ure
hours as mentioned in the syllabus.

Page 96

Course
Code Course Name Teaching Scheme Credits Assigned
MCAE255 Research
Methodology Contact Hours
Theory Tutorial Theory Tutorial Total
3 1 3 1 4
Examination Scheme
Theo ry Term
Work End Sem Exam Total CA Test AVG
20 20 20 25 80 125
Pre-requisites:
Basic knowledge of Mathematics for Data Analysis, Software, Internet
Course Objectives: The course aim to
Sr.No. Course Objective
1 Understand Research and Research Process and their types
2 Acquaint students with identifying problems for research
3 Explain the various research strategies and apply them to various research problems

Course Outcomes:
Sr.No. Outcome Bloom Level
CO1 Demonstrate knowledge of researc h concepts and processes understand ing
CO 2 Perform literature reviews, prepare the key elements of a research
proposal Applying
CO 3 Compare and contrast quantitative and qualitative research analyzing
CO 4 Define and develop a possible research inter est area using specific
research design applying
CO 5 Explain the rationale for research ethics, and its importance understand ing
CO 6 Demonstrate enhanced writing skills Applying

Module Detailed Contents Hrs

Page 97

01 Introduction and Basic Research Conce pts Research – Definition;
Concept of Construct, Postulate, Proposition, Thesis, Hypothesis, Law,
Principle. Research methods vs Methodology, Need of Research in Business
and Social Sciences, Objectives of Research , Issues and Problems in
Research ,
Self Learning Topics : Characteristics of Research: Systematic, Valid,
Verifiable, Empirical and Critical 8
02 Research types and Design : Basic Research , Applied Research ,
Descriptive Research, Analytical Research , Empirical Research ,Qualitative
and Quant itative Approaches
Research Design – Meaning, Types and Significance , Sample Design –
Meaning and Significance Essentials of a good sampling Stages in Sample
Design Sampling methods/techniques Sampling Errors
Self Learning Topics : types of Sampling 8
03 Research Methodology Meaning of Research Methodology ,Stages in
Scientific Research Process: Identification and Selection of Research
Problem , Formulation of Research Problem , Review of Literature ,
Formulation of Hypothesis , Formulation of research De sign , Sample
Design , Data Collection , Data Analysis , Hypothesis testing and
Interpretation of Data , Preparation of Research Report
Self Learning Topics : types of Hypothesis 8
04 Formulating Research Problem Considerations: Relevance, Interest, Data
Availability, Choice of data, Analysis of data, Generalization , Interpretation,
and analysis Validity Testing
Self Learning Topics : importance of interpretation 5
05 Ethics : Ethical Issues , Ethical Committees , Commercialization , copy right
, royalty , Intellectual Property rights and patent law , Track Related aspects
of intellectual property Rights, Reproduction of published material ,
Plagiarism, Citation and Acknowledgement ,Reproducibility and
accountability.
Self Learning Topics : Steps of p atent filing 5
06 Testing & Report writing Preparation of the report on conclusion reached ,
, Suggestions and Recommendation 5
Reference Books :
Reference No Reference Name
1 Garg.B.L., Karadia, R., Agarwal,F. and Agarwal, U.K., 2002. An introduction
to Research Methodology, RBSA Publishers.
2 Kothari, C.R.(2008). Research Methodology: Methods and Techniques.
Second Edition. New Age International Publishers, New Delhi.

Page 98

3 Pruzan, Peter , Research Methodology, The Aims, Practices and
Ethics of Science , ISBN 978 -3-319-27167 -5
4 Dawson, Catherine, 2002, Practical Research Methods, New Delhi, UBS
Publishers Distributors
5 Wadehra, B.L.2000. Law relating to patents, trade marks, copyright designs
and geographical indications. Universal Law Publishing.
6 Kumar Ranjit, 2005, Research Methodology -A Step -by-Step Guide for
Beginners, (2nded), Singapore, Pearson Education
Web References:
https://www.wisdomjobs.co m/e-university/research -methodology -tutorial -355.html
https://academicguides.waldenu.edu/library/srmo/tutorials
Research Methodology Tutorial
Sr. No. Detailed Content
Hrs
1 Defin ing a Research Problem in the area of interest 1
2 Literature Review of the Research Problem - Case Study 1
3 Research Design of the problem - Case Study 1
4 Sampling Design of the problem - Case Study 1
5 Measurement And Scaling Techniques to be used - Case Study 1
6 Formation of hypothesis Methods Of Data Collection for the research
problem - Case Study 1
7 Processing And Analysis Of Data for the research problem - Case Study 2
8 Hypothesis testing - Case Study 1
9 Interpretation & Report Writing - Case Study 1
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks

Page 99

The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
Term Work : 25 marks
The term work will be based on the tutorial performance of the student .
End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -
questions of 5 marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture
hours as mentioned in the syllabus.

Page 100

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term
Work Practical Oral Tota
l
MCAL21 Artificial
Intelligence &
Machine Learning 2 1 25 30 20 75

Pre-requisite: Basic understanding of mathematical, dat a mining concepts and any programming
Language.
Lab Course Objectives:
Sr.
No. Course Objective
1 ● Understand problem solving concepts of artificial intelligence.
2 Impart a thorough understanding of basic machine learning algorithms and its
applications.
3 Demonstrate dimensionality reduction techniques for feature extraction and selection.
4 Build model using appropriate machine learning algorithms for real world problems.

Lab Course Outcomes: On the successful completion of the course, students will be able to
Sr.
No. Outcome Bloom
Level
CO1 Apply the basic concepts of artificial intelligence and its applications. Apply ing
CO 2 Experiment with basic and ensemble the machine learning algorithms and
its applications. Analyzing
CO 3 Analyze dimension ality reduction techniques for feature extraction and
selection. Analyzing
CO 4 Develop models using appropriate machine learning algorithms for real
world problems. Creating

Description:
Module Detailed Contents Hrs
1 Logic programming with Prolog: To specify relationships among objects and
properties of objects , problem solving.
Self Learning Topic: - Define rules defining implicit relationships between
objects. 2
2 Introduction to Python Programming: Learn the different libraries - NumPy,
Pandas, SciPy, Matplotlib, Scikit Learn. 4

Page 101

Self Learning Topic: - Milk, Shogun.
3 Supervised Learning: Linear Regression predicts a real -valued output based on
an input value, Logistic regression - the notion of classification, the cost function
for logistic regre ssion, and the application of logistic regression , KNN -
classification.
Self Learning Topic: - Evaluation metrics like MSE, Accuracy, Confusion Matrix,
Precision, Recall, ROC curve. 4
4 Dimensionality Reduction: Features Extraction, Feature selection,
Normalization, Transformation, Principal Components Analysis -visualizations
of complex datasets.
Self Learning Topic: - LDA (Linear Discriminant Analysis). 4
5 Unsupervised Learning: K-Means clustering algorithm, K -medoid clustering
algorithm.
Self Learning Topic: Other Clustering Algorithms. 2
6 Classifying data using Support Vector Machines (SVMs): SVM -RBF kernels.
Self Learning Topic : -SVM -Kernels -Polynomial kernel. 2
7 Bagging Algorithm: Decision Tree, different ensemble techniques like bagging, boosting ,
stacking and voting, Random Forest - bagging, Attribute bagging and voting for
class selection .
Self Learning Topic: - Extra Trees. 4
8 Boosting Algorithms: AdaBoost, Stochastic Gradient Boosting, Voting
Ensemble.
Self Learning Topic : - AdaBoost as a For ward Stage wise Additive Model. 2
9 Deployment of Machine Learning Models: simple Web API.
Self Learning Topic : Python Flask library. 2

Reference Books :
Reference
No Reference Name
1 Aurelian Géron, Hands -On Machine Learning with Scikit -Learn, Keras, and
TensorFlow, 2nd Edition.
2 Paul J. Deitel, Python Fundamentals.
3 Stuart Russell, Peter Norvig ,Artificial Intelligence – A Modern Approach, ,
Pearson
Education / Prentice Hall of India, 3rd Edition, 2009.
4 EthemAlpaydın, Introduction to Machine Le arning, PHI, Third Edition, ISBN No.
978-81-203- 5078 -6.
5 Peter Harrington, Machine Learning in Action. Manning Publications, April
2012ISBN 9781617290183.
6 Introduction to Computer Programming using Python, John V Guttag
7 Core Python Programming, R . NageswaraRao

Page 102

Web References:
Reference
No Reference Name
1 https://talentsprint.com/pages/artificial -intelligence -machine -learni ng-iiit-h-
program/program -details.pdf
2 https://learning.oreilly.com/library/view/learning -robotics
using/9781783287536/cover.html
3 http://www.qboticslabs.com
4 https://subscrip tion.packtpub.com/book/big_data_and_business_intelligence
5 https://scikit -learn.org/0.16/modules/generated/sklearn.lda.LDA.html
6 https://machinelearningmastery.com/ensemble -machine -learning -algorithms -
python -scikit -learn/
7 https://www .coursera.org/learn/machine -learning#syllabus
8 https://data -flair.training/blogs/python -ml-data-preprocessing/

Suggested list of experiments
Practical
No Problem Statemen t
1 Implementation of Logic programming using LISP /PROLOG -DFS for water jug
problem / BFS for tic -tac-toe problem/ Hill -climbing to solve 8 - Puzzle Problem.
2 Introduction to Python Programming: Learn the different libraries - NumPy, Pandas,
SciPy, Matp lotlib, Scikit Learn.
3 Implementation of Linear Regression, Logistic regression , KNN - classification.
4 Implementation of dimensionality reduction techniques: Features Extraction and
Selection, Normalization, Transformation, Principal Components Analysi s.
5 Implementation of K -Means and K -medoid clustering algorithm.
6 Implementation of Classifying data using Support Vector Machines (SVMs).
7 Implementation of Bagging Algorithm: Decision Tree, Random Forest.
8 Implementation of Boosting Algorithms: AdaBoost, Stochastic Gradient Boosting,
Voting Ensemble.
9 Deployment of Machine Learning Models.

Assessment :
Term Work: Will be based on Continuous Assessment
1. Laboratory work will be based on the syllabus with minimum 10 experiments.
Experiments 20 marks
Attendance 5 marks
2. Practical will be evaluated by the subject teacher and documented according to a rubric
End Semester Practical Examination:
Practical and oral examination will be based on suggested practical list and entire syllabus.

Page 103

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term Work Practical Oral Total
MCAL22 Soft Skills
Development
Lab 02 01 50 -- -- 50

Pre-requisite: Decent working knowledge of the English language (including Grammar) is a mu st,
keeping in mind that most business/management transactions in India and internationally are
conducted in the English language
Lab Course Objectives : Course aim to
Sr. No. Course Objective
1 Inculcate the essential skills that professionals need to di stinguish themselves and
make a positive impact on their work and social lives
2 Provide better understanding of corporate culture and to improve their etiquettes,
interpersonal skills and professional image
3 Develop holistically and ensure comprehensiv e learning.
Lab Course Outcomes:
Sr. No. Outcome Bloom Level
CO1  Develop interpersonal skills that help in communication,
teamwork, leadership and decision making. Apply ing
CO 2 Methodically study, formulate and interpret different facets of
organizati onal behavior. Evaluating
CO 3 Develop holistic leaders and technocrats helping in individual and
organizational growth. Creating
Description:
Module
No Detailed Contents Hrs
1 Soft Skills Introduction:
Soft-Skills Introduction What is Soft Skills? Sig nificance of Soft -Skills –
Soft-Skills Vs. Hard Skills - Selling Soft - Skills – Components of Soft
Skills – Identifying and Exhibiting Soft -Skills
SelfLearning Topics: Types of Soft, Hard Skills 02
2 Communication :
Concept and meaning of communication, m ethods of communication,
verbal and non -verbal communication, techniques to improve
communication. Communication in a business organization: Internal
(Upward, Downward, Horizontal, Grapevine). External Communication, 7
C‟s of communication. Active Listenin g, Differences between Listening
and Hearing, Critical Listening, Barriers to Active Listening, Improving
Listening, Intercultural sensitivities, Business etiquette when dealing with
people from different nationalities
Practical (Role plays, case studies) 03

Page 104

Self LearningTopics : Problems/Barriers in communication

3 Written/ Business Communication :
Written Communication: Principles of Correspondence, language and style
in official letter (full block format, modified block format), Business letters
(enquir y to complaints and redressal), Application letter, CV writing, , E -
mail etiquette, Documentation of Meetings, Notice, Agenda, Minutes of
Meetings.
Practical (Practice on CV, Business Letters, Applications, Notice, Agenda,
Minutes of Meetings)
Self Learnin g Topics: Impact of modern Technology on
Business Communication the paperless office, use of modern devices 04
4 Presentation Skills :
Presentation techniques, Planning the presentation, Structure of
presentation, Preparation, Evidence and Research, Deliv ering the
presentation, handling questions, Time management. Visual aids.
Practical - Presentation by students in groups of maximum 3 on
Organizational Behavior topics allocated by faculty.
Topics have to cover –
1. Personality: Meaning, Personality Determ inants, Traits, Personality types
and its, impact on career growth,
2. Individual / Organizational Decision Making.
3.Attitude: Meaning, Components of Attitude, changing attitude and its
impact on career growth
4. Perception and Values.
5. Motivation and L eadership: Concept, Importance.
6. Goal setting: SMART (Specific, Measurable, Attainable, Realistic,
Timely) Goals, personal and professional goals
7. Time and Self -Management.
8. Learning in a group, Understanding Work Teams, Dynamics of Group
Behavior, T echniques for effective participation
9. Etiquette - General & Business Etiquette, Body language
10. Emotional intelligence of self and SWOC
11. Threats v/s Challenges
12. Dos and Donts of a presentation/ meetings Online & offline.(presenter
& members)
Self LearningTopics : Voice modulation,
Tone, Pitch,Knowledge and self confidence 08
5 Effective Public Speaking :
Public Speaking, Selecting the topic for public speaking, Understanding the
audience, Organizing the main ideas, Language and Style choice in th e
speech, Delivering the speech,Voice Clarity.
Practical (Extempore)
Self LearningTopics : Preparation, Attire, Posture and Delivery techniques 03
6 Group Discussions :
Group Discussion Skills, Evaluation components, Do‟s and Don‟ts. 03

Page 105

Practical (Group Disc ussions)
SelfLearningTopics: --
7 Interview Techniques :
Interview Techniques, Pre -Interview Preparation, Conduct during interview,
Verbal and non -verbal communication, common mistakes. Preparation of
CV.
Practical (Role plays, mock interviews, Telepho nic Interviews, Body
Language, Facial Expression)
SelfLearningTopics : Sample communications and
exercises, audio -visual presentations 03
Reference Books :

Reference
No Reference Name
1 Business Communication (Revised Edition),Rai&Rai , Himalaya Publish ing
House.
2 Soft skills: an integrated approach to maximise Personality,
Chauhan&Sharma,Wiley India publications.
3 Business Communication: A practice oriented approach, Kalia and
ShailjaAgarwal.
4 Business Communication – Meenakshi Raman, Prakash Sing h, Oxford
Publication
5 Stephen Robbins & Judge Timothy: Organization Behavior, Pearson Education
6 K. Aswathappa – Organizational Behavior: Text, cases & games, Himalaya
Publishing House.
7 Pareek, Udai, Understanding Organizational Behaviour, Oxford U niversity
Press, New Delhi.
8 Taylor & Chandra, “Communication for Business: A Practical Approach,”
Pearson
9 Doctor & Doctor, “Business Communication,” Sheth Publishers.
Sugested list of experiments
Practical No Problem Statement
1 Role Plays
2 Manag ement Activities/Games
3 Case Studies
4 Presentations
5 Extempore Public Speaking
6 Group Discussions
7 Mock Interviews
Assessment :
Term Work: Will be based on Continuous Assessment

1. Laboratory work will be based on the syllabus with minimum 10 exper iments.
Experiments 40 marks
Attendance 10 marks
2. Practical will be evaluated by the subject teacher and documented according to a rubric

Page 106

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term Work Practical Oral Total
MCALE231 Image
Processing
Lab 2 1 25 30 20 75
Pre-requisite: Basic understanding of fundamentals of any programming language
Lab Course Objectives
Sr.No. Course Objective
1 To Understand basics of Digital Image Processing concepts and implement ba sic
techniques for handling images
2 To understand tools that can be used to implement image processing algorithms
3 To implement various techniques for image enhancement, segmentation and some
basic morphological operations
Lab Course Outcomes
Sr.No Outcome Bloom Level
CO1 Understand different image file formats and their structure Understanding
CO 2 Explain how Digital images are manipulated using various image
enhancement techniques Understanding
CO 3 Learn the signal processing algorithms and te chniques in image
enhancement and image restoration. Applying
CO 4 Implement digital transforms Creating
CO 5 Be able to understand and implement certain image compression
techniques. Understanding
Description:
Module Detailed Contents Hrs
I Image Enhancement :
Spatial domain and Frequency domain techniques
Self Learning Topics: color image enhancement 4
II Discrete Fourier Transform:
To find DFT/FFT forward and Inverse Transform of Image.
Self Learning Topics: Image transforms 5
III Discrete c osine Transform :
To find DCT forward and Inverse Transform of Image.
Self Learning Topics : Sine Image Transforms 4

Page 107

IV Image Segmentation and Image Restoration :
The detection of discontinuities - Point, Line and Edge detections, Hough
Transform, Thresh olding, Region based segmentation Chain codes.
Self Learning Topics: Image segmentation techniques. 5
V Image Data Compression :
Fundamentals of compression, Basic compression Methods.
Self Learning Topics: Difference between lossy and lossless compressi on 4
VI Morphological Operation :
Morphological operations: Dilation, Erosion, Opening, Closing.
Self Learning Topics: Erosion, Dilation, Hit or Miss operation. 4
Reference Books :

Reference
No Reference Name
1 R.C.Gonzalez & R.E.Woods, Digital Image Processing, Pearson Education,
3rd edition, ISBN. 13:978 -0131687288
2 S. Jayaraman Digital Image Processing TMH (McGraw Hill) publication,
ISBN - 13:978 -0-07- 0144798
3 Gonzalez, Woods & Steven, Digital Image Processing using MATLAB, Pearson
Education, ISBN -13:978 -0130085191
4 William K. Pratt, “Digital Image Processing”, John Wiley, NJ,
4th Edition,200
5 Sid Ahmed M.A., “Image Processing Theory, Algorithm andArchitectures”,
McGraw -Hill, 1995.Umbaugh, “Computer Vision”.
6 Anil K.Jain,Fundamen tals of Digital Image Processing,Prentice Hall of India,2nd
Edition, 2004.
Web References:
Reference
No. Reference Name
1 https:// www.ict.gnu.ac.in › sites › files › subject syllabus pdf

2 https:// www.gtu.ac.in › syllabus › sem7
3 https:// www.nptel.ac.in › content › syllabus_pdf

4 https:// www.ktuweb.com › page_showdoc › dopage =syllabus

5 https://www.cse.iitkgp.ac.in › syllabus

6 https:// www.vit.edu › images › btech_syllabus_entc_16 -17

7 https:// www.iitkgp. ac.in › fac-profiles › showprofile
8 https:// www.cse.iitb.ac.in › ~ajitvr › cs663_fall2018

Suggested list of experiments
Practical
No Problem Statement
1 Program to display image using read an d write operation
2 Program to enhance image using image arithmetic and logical operations.
3 Program to implement Image Negative,
4 Program to implement Thresholding of an Image

Page 108

5 Program to Implement smoothing or averaging filter in spatial domain
6 Program to produce the Histogram, Equalized Histogram, and Equalized image of
an input image
7 Program for smooth an image using low pass filter in frequency domain
8 Program for sharpen the image using high pass filter in frequency domain
9 Program to find DFT/FFT forward and Inverse Transform of Image
10 Program to find DCT forward and Inverse Transform of Image
11 Program to find Edges using Prewit/ Sobel/ Fri -chen / Robert operators
12 Program to find edges using canny Edge Detection
13 Program to implement Huffman coding technique for image compression
Assessment :
Term Work: Will be based on Continuous Assessment
1. Laboratory work will be based on the syllabus with minimum 10 experiments.
Experiments 20 Marks
Attendance 5 marks
2. Practical will be evaluated by the subject teacher and documented according to a rubrics.
End Semester Practical Examination:
Practical and oral examination will be based on suggested practical list and entire syllabus.

Page 109

Course
Code Course Nam e Contact
Hours Credits
Assigned Examination Scheme
Term Work Practical Oral Total
MCALE232 Internet of
Things Lab 02 01 25 30 20 75

Pre-requisite: 1. Knowledge of C and C++ Programming.
2. Basics of Cloud.
Lab Course Objectives: The Course aim to
Sr. No. Course Objective
1  Learn basic electronic components and to get familiar with arduino
software/hardware and arduino simulator.
2 Interface various I/O devices and sensors with arduino.
3 Interface IoT device with cloud.
4 Develop skills requi red to build real -life IoT based projects.
Lab Course Outcomes:
Sr.No. Outcome Bloom Level
CO1 Identify basic electronic components and make use of arduino
software/hardware and arduino simulator. Apply ing
CO 2 Experiment with various I/O devices and se nsors with Arduino. Apply ing
CO 3 Build IoT application using Cloud. Creating
CO 4 Develop IoT based projects. Creat ing
Description:
Module Detailed Contents Hrs
1 Module: Introduction to Basic Components :
 Familiarization with Arduino and perform nec essary software installation.
 Breadboard Basics
 Programming the Arduino
o Arduino, circuits, and code: Bringing everything together
Self Learning Topics: Basic electronic components such as LED, resistors,
battery etc. 2
2 Module: Switches, LEDs, and More:
 Programs based on interfacing LED‟s, Switches/push buttons and
Speakers/Buzzer, LCD/ Seven Segment Display with Arduino
Self Learning Topics: Basics of Switches /push buttons , LED,
Speakers/Buzzer, LCD/ Seven Segment Display 4
3 Module: Analog Values
 Programs based on interfacing LED‟s, Potentiometer, Photoresistor with
Arduino
 Programs using PWM pins of Arduino
 Programs using Serial Monitor of Arduino
 Programs based on interfacing DHT11 temperature sensor
 Programs based on interfacing Passive infrare d sensors (PIR), Ultrasonic 6

Page 110

of Arduino
Self Learning Topics: Basic of Analog values, PWM concepts
4 Module: Servo Motors:
 Programs based on interfacing LED‟s, Servo Motor, Potentiometer with
Arduino
Self Learning Topics: Basics of servo motors, potenti ometer 2
5 Module: IoT in Cloud:
 Interfacing IoT device with Cloud
Self Learning Topics: Computer Network and Cloud Concepts 4
6 Module: Mini Project:
 Mini projects such as Home automation, Robots, Wearable projects, art
projects etc.
Self Learning Topi cs: Real life problem statement 8

Reference Books :
Reference
No. Reference Name
1 Make: Learn electronics with Arduino, Jodi Culkin and Eric Hagan, Maker
Media
2 Programming Arduino: Getting started with sketches, Simon Monk , TMH
3 Getting Started wi th Arduino: A Beginners Guide, Brad Kendal,
4 Make: Getting Started with Arduino, Massimo Banzi, Michael Shiloh,
Makermedia
5 Make: Getting Started with Sensors, KimmoKarvinen, TeroKarvinen,
Makermedia
6 Learn Electronics wit Arduino, Don Wilcher, Apre ss
7 From Machine -to-Machine to the Internet of Things: Introduction to a New Age
of Intelligence, Jan Holler VlasiosTsiatsis Catherine Mulligan Stefan Aves and
StamatisKarnouskos David Boyle
8 VijayMadisetti and ArshdeepBahga, “Internet of Things (A Han ds-on-
Approach)”, 1 st Edition, VPT, 2014
9 The Internet of Things in the Cloud:A Middleware Perspectiv,By Honbo Zhou
10 Rethinking the Internet of Things A Scalable Approach to Connec ting
Everything, Francis daCosta, Apress
Web References:
Reference No Reference Name
1 http://www.tinkercad.com
2 https://www.arduino.cc/
3 https://www.makerspaces.com/15 -simple -arduino -uno-breadboard -projects/
4 https://thingspeak.com/
Suggested list of experiments:
 All Programs to be done using Simulation Tool like Tinkercad or any other simulation tool.
Interfacing IoT device with cloud using any cloud platform like ThingSpeak, AWS etc.

Practical
No Problem Statement
1 Program to blink Arduino onboard LED and To interface external LED with
Arduino and write a program to turn ON LED for 1 sec after every 2 seconds.
2 To interface 5 LED‟s with Arduino and write a program to blink 6 LEDs, one at a

Page 111

time, in a b ack and forth formation.
3 To interface Push button with Arduino and write a program to turn ON LED when
push button is pressed.
4 To interface Push button, Speaker/buzzer with Arduino and write a program to turn
ON LED and generate a note or tone when push button is pressed.
5 To interface 2 Push buttons, a Speaker with Arduino and write a program to turn
ON LED and generate a 2 different notes on two button keyboard.
6 To interface Seven Segment Display (SSD) with Arduino and write a program to
blink SSD.
7 To interface Seven Segment Display (SSD) with Arduino and write a program to
print numbers from 1 to 4 on SSD.
8 To interface LCD, push button, potentiometer with Arduino and write a program to
display message on LCD when push button is pressed.
9 To interface LCD, push button, potentiometer with Arduino and write a program to
display the no. of times (count) the push button is pressed on LCD.
10 To interface LED‟s, potentiometer with Arduino and write a program to turn on or
off more of the LE Ds by turning the potentiometer knob.
11 To interface LED, Photo resistor (LDR) with Arduino and write a program to
increase and decrease the brightness of the LED based on the amount of light
present.
12 To interface LED‟s with Arduino and write a progr am to show the fading effect on
LED‟s.
13 To interface DHT11 sensor with Arduino and write a program to display
temperature and humidity data on serial monitor.
14 To interface PIR/ Ultrasonic sensor with Arduino and write a program to turn on
and off LE D depending on motion detection/sound detection.
15 To interface servo motor/DC motor with Arduino and write a program to sweep a
servo back and forth through its full range of motion/ to control a DC motor.
16 To interface LED with Arduino and write a p rogram to send sensor data to the cloud
using ThingSpeak/ AWS and receive notification.
17 To interface Temperature sensor with Arduino and write a program to send sensor
data to the cloud using ThingSpeak/ AWS and receive notification.
18 To build a min i project based on interfacing any combination of sensors with
Arduino and cloud.

Assessment :
Term Work: Will be based on Continuous Assessment
1. Laboratory work will be based on the syllabus with minimum 10 experiments.
Experiments 20 marks
Attendance 5 marks
2. Practical will be evaluated by the subject teacher and documented according to a rubric

End Semester Practical Examination:

Practical and oral examination will be based on suggested practical list and entire syllabus.

Page 112

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term Work Practical Oral Total
MCALE233 Robotic Process
Automation 02 01 25 30 20 75

Pre-requisite: 1. Knowledge of C and C++ Programming.
2. Software Engineering (UML)
Lab Course Objectives:
Sr. No. Course Objective
1 Identification of Use Cases for creating BOTs
2 Build, Edit and Run BOTs
3 Describe how Automation Anywhere‟s RPA tool can be used for creating software
robots
4 Develop and apply IQ BOTs
Lab Course Outcomes: On successful compl etion of course learner/student will be able to
Sr. No. Course Outcome Bloom Level
CO1 Define the key concepts of Robotic Process Automation and
evolution. Remembering
CO2 Demonstrate development of BOT with specific tools Understanding
CO3 Apply RPA co mmands to automate atsks Applying
CO4 Summarize this tool as a summation of Robotic Process
Automation , Cognitive Analytics, and Workforce Analytics Evaluating
Description:
Module Detailed Contents Hr
s
1 Module: Introduction to GUI of Tool
Demonstrate good understanding of Recorders, Editor, and various Basic
Commands to build simple tasks / Bots for automating simple processes,
Develop simple BOT, Control Room Features:
Control Room Issues & Login, create Creator & Runner in Control Room, run
Bot from Control Room, Schedule Bot from Control Room, working with
credential managers, credentials vaults in control room
Self Learning Topics: Use of Web Control Room of AAE. Contro l Room,
demonstrate client, Audit Log in Control Room 2
2 Module: Automation anywhere Basic Commands:
MESSAGE BOX, Comment & Variables Type, Clipboard, Delay & Wait,
Log To File, Launch Website, - Open Program / File, Read From CSV / Text
File, Object Cloning with Insert Keystroke, Variable Operation with Loop, 4

Page 113

Window Actions, String operation (Before After - Compare - Find), String
operation (Join - Length - Lower Case), String Operation (Replace - Reverse -
Split), String Operation (Sub String - Trim - Upper Case), error handling
Self Learning Topics: Play Sound & System
3 Module: Automation anywhere Advanced Commands :
FTP / SFTP, Excel Automation, PDF Integration, Send Email, Email
Automation, REST Web Service, SOAP Web Service, Database, PGP ( Pretty
Good Privacy), Manage Windows Control, OCR (Optical Character
Recognition) of Analog values,
Self Learning Topics: PWM concepts, schedulers and triggers 4
4 Module: RPA for Excel Commands:
Automation of excel commands for different real -world busi ness use cases
using A2019 Excel Commands and Generation of reports for data analysis,
decision making, and other business process automation requirements
Self Learning Topics: Automation of excel commands 4
5 Module: Working with PDF Documents:
Self Lear ning Topics: Real life problem statement 2
6 Module: Advanced RPA Techniques:
Manipulating web -based components like textbox, IQBotDahsboard, IQBot
Commands, Build resiliency within a bot
Self Learning Topics: Manipulating web -based component such as drop
down 4
7 Module: Use of UI Path Tool:
UIPath Orientation, UI Path Structure, Control Flow, Error Handling
Self Learning Topics: UI Control Flow 3
8 Automation of RPA Case study 3
Reference Books :
Reference
No. Reference Name
1 The Robotic Process Auto mation Handbook: A Guide to Implementing RPA
Systems By Tom Taulli
2 Value -Driven Robotic Process Automation Enabling Effective Digital
Transformation by Mathias Kirchmer, Peter Franz and Danny Bathmaker
3 Learning Robotic Process
Automation - Alok Mani T ripathi, Copyright  2018 Packt Publishing
Web References:
Referenc
e No Reference Name
1 https://www.udemy.com/course/robotic -process -automation/
2 https://www.udemy.com/course/lea rn-automation -anywhere/
3 https://university.automationanywhere.com/rpa -learning -trails/automation -
anywhere -university -essential -level -prep-courses -mba-students/

Page 114

4 https://www.udemy.com/course/learn -advanced -rpa-automation -anywhere -with-
iqbot -and-wlm/
5 https://www.edureka.co/blog/automation -anywhere -examples
6 https://docs.automationanywhere.com/bundle/enterprise -
v11.3/page/enterprise/topics/aae -client/bot -creator/commands/commands.html
Suggested list of experiments:
NOTE: All Programs are designed for RPA tool : Automation Anywhere
Interfacing IoT device with cloud using any cloud platform like ThingSpeak, AWS etc.
Practical
No Problem Statement
1 Use of recorder, editors and basic commands to build simple tasks.
2 Run Bot from Control Room and Schedule Bot from Control Room.
3 Automa te action of getting the title of active window.
4 Automate action of closing a notepad window.
5 Automate task of replacing few characters from a string
6 Automate task of copying files from a source folder to destination folder.
7 Extract a table fro m webpage
8 Automate task of extracting a text from a window and display text
9 Automate task of writing text into Notepad file
10 Extract data from JSON file and display output in message box
11 To automate the task of extracting the data from an Exce l File according to some condition and
storing the extracted data in another File.
12 To automate the task of extracting the data from multiple PDF documents and storing the data
into a CSV file.
13 Manipulate web -based components like textbox, drop down
14 Extract data from website and store itin excel or database
15 Demonstrate Scheduler and trigger
16 Design IQ BOT and resilience BOT
17 Apply UIPath tool for some examples.
Assessment :
Term Work: Will be based on Continuous Assessment
1. Laboratory wo rk will be based on the syllabus with minimum 10 experiments.
Experiments 20 marks
Attendance 5 marks
2. Practical will be evaluated by the subject teacher and documented according to a rubric
End Semester Practical Examination:Practical and oral exami nation will be based on
suggested practical list and entire syllabus.

Page 115

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term
Work Practical Oral Total
MCALE234 Computer
Vision Lab 02 01 25 30 20 75

Pre-requisite: Fundamen tal Knowledge of Computer Graphics and Image Processing
Lab Course Objectives:
Sr.No. Course Objective
1 Learn basic Image Processing techniques used in Computer Vision
2 Illustrate various components used in Computer Vision
3 Implement Motion Tracking and Face Detection
4 Understand applications of CNN in Computer Vision
Lab Course Outcomes: On successful completion of course learner/student will be able to
Sr.No. Outcome Bloom Level
CO1 Understand Open CV Framework Understand ing
CO 2 Develop a pplications using basic image processing techniques used
in Computer Vision Apply ing
CO 3 Design Applications to Detect Motion and Face in an image Apply ing
CO 4 Create a Applications using CNN Creating
Description:
Uni
t No Detailed Contents Hrs
1 Mod ule: Open CV and Python
Running Python Programs, Frameworks for CV,UnderstandingOpenCV,
Programs using OpenCV 02
2 Module: Basic Image Handling using python
Reading, Writing and Displaying Images, Plotting images, points and lines, Image
contours and hist ograms, Histogram equalization, Interactive annotation, Gray 06

Page 116

level transforms, Image Transformations, Image Derivatives
Self Learning Topics: Image Denoising
3 Module: Image Transformations
Program based on: Line Detection -Hough Transforms, Harris co rner detector,
Edge Detection, SIFT - Scale -Invariant Feature Transform, Matching Geotagged
Images, Homographies, Warping images, Creating Panoramas :Camera Models
and Augmented reality, Light effects
Self Learning Topics: Drawing on Images 06
4 Module: E xploring Structure from Motion
Motion Detector Using OpenCV, Motion Detection using Video, Plotting the
motion Direction Graph
Self Learning Topics: Object Classification 04
5 Module: Face Detection and Tracking
Face detection, Pedestrian detection, Face recognition, Eigenfaces, Viola -Jones
Algorithm, Haar -like Features, Integral Image, Training Classifiers
Self Learning Topics: Measuring features 04
6 Module: Convolutional Nerual Networks for CV
Object Detection and Identification using CNN, Building a C NN, Project
Self LearningTopics:Dogs and cats case study 06
Reference Books :

Reference
No Reference Name
1 Solem, Jan Erik. Programming Computer Vision with Python: Tools and
algorithms for analyzing images. " O'Reilly Media, Inc.", 2012.ISBN:
144934193
2 Demaagd, Kurt. Practical Computer Vision with SimpleCV: Making Computers
See in Python. 2012.ISBN: 9781449337865
3 Jähne, Bernd, Horst Haussecker, and Peter Geissler, eds. Handbook of computer
vision and applications. Vol. 2. San Diego: Academic press , 1999.ISBN:
0123797713
4 Baggio, Daniel Lélis. Mastering OpenCV with practical computer vision
projects. Packt Publishing Ltd, 2012.ISBN: 1849517827
Web References:
Reference
No Reference Name
1 http://groups.csail.mit.edu/vision/
2 https://medium.com/readers -writers -digest/beginners -guide -to-computer -vision -
23606224b720

Page 117

3 https://vision.in.tum.de/rese arch
4 Deeplearning.ai
5 http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html
6 http://groups.csail.mit.edu/vision/

Suggested list of experiments
Practical No Problem Statement
1 Program for Reading, Writing and Displaying Images
2 Program for Changing Color Spaces
3 Program to resize Images
4 Program to Rotate Images
5 Programs using Histogram Equalization
6 Programs for Edge detection
7 Programs for Line Detection
8 Programs using Scale Invariant Feature Transform (SIFT)
9 Programs for Motion Detection
10 Programs for Face Detection
11 Programs to differentiate objects
Assessment :
Term Work: Will be based on Continuous Assessment
1. Laboratory work will be based on the syllabus with minimum 10 experiments.
Experiments 20 marks
Attendance 5 marks
2. Practical will be evaluated by the subject teacher and documented according to a rubric
End Semester Practical Examination:

Practical and oral examination will be based on suggested practical list and entire syllabus.

Page 118

Cours e
Code Course
Name Contact
Hours Credits
Assigned Examination Scheme
Term Work Practical Oral Total
MCALE235 Embedded
Systems
Lab 02 01 25 30 20 75
Pre-requisite: Basic understanding of C / C++ and Python Programming.
Lab Course Objectives
Sr.No. Course Objective
1 Understand basics of Embedded Systems and methods for programming.
2 Understand tools that can be used to write and execute programs for 8051
microcontrollers.
3 Implement some basic programs in Embedded C for 8051 microcontroller.
4 Execute some basic interfacing methods.
Lab Course Outcomes: On successful completion of course learner/student will be able to
Sr.No
. Outcome Bloom Level
CO1 Understand the programming environment of the
8051microcontroller Understand ing
CO 2 Explain how microcontrollers can be programmed using embedded C
programming Understand ing
CO 3 Learn execution of Embedded C programming using simulators Apply ing
CO 4 Implement some basic hardware interfacing programs for 8051 /
ARM / Raspberry Pi / Arduino Analyse
Description:
Module Detailed Contents Hrs
1 Introduction to Kiel or any other Simulator:
Introduction to Keil or any other simulator for executing Embedded C
programs 2
2 Embedded C Programming:
Introduction to Basics of Embedded C Programming,
Program Structure and execution Methodology,
Basic programs in Embedded C : Variable and Constant Declaration and 4

Page 119

Basic Input Output, Arrays
3 Embedded C Programming : Operators and Control Structures:
Basic programs on Operators and Control Structures 5
4 Embedded C Programming : Functions :
Basic Programs to demonstrate the use of Functions. 5
5 8051 Interfacing:
Basic Programs for interfacing various I/O ports and other devices with 8051
microcontroller. 6
6 Raspberry Pi / Arduino platforms:
Introd uction to basic interfacing methods on Raspberry Pi / Arduino or
similar platforms using C / C++ / Java / Python 4
Reference Books :

Reference
No Reference Name
1 Shibu K V, “Introduction to Embedded Systems”, Tata McGraw Hill
2 Raj Kamal, “Embedded Sys tems : Architecture, Programming and Design”, 2e,
Tata McGraw Hill
3 K Uma Rao, AndhePallavi, “The 8051 and MSP430 Microcontrollers :
Architecture, Programming and Applications, Wiley
4 Bahadure, Chandrakar, “ Microcontrollers and Embedded System Design” , Wiley
5 Raj Kamal, “Embedded Systems : Architecture, Programming and Design”, Tata
McGraw Hill.
6 Michael J. Pont, “Embedded C”, Pearson Education, 2nd Edition

Web References:
Reference
No. Reference Name
1 www. nptel.ac.in
2 www.keil.com
2 www.arduino.cc
3 www.raspberrypi.org
Suggested list of experiments
Practical
No Problem Statement
1 Program fo r basic Input/output.
2 Program to declare, initialize and use basic data types.
3 Program to implement control structures : If -Else
4 Program to implement control structures : While Loop
5 Program to implement control structures : for Loop
6 Program to implement Functions
7 Program to read and write a byte to a Pin of 8051

Page 120

8 Program to continuously toggle an LED connected to an output pin of 8051
9 Program to display numbers on a Seven Segment Display connected to 8051
10 Program to generate time delay
11 Introduction to Raspberry Pi / Arduino processor kits and its interfaces
12 Program for interfacing LED / LCD Panel using Python / C++ / Java on Raspberry
Pi / Arduino
13 Program for interfacing a DC Motor / Switches using Python / C++ / Java on
Raspberry Pi / Arduino

Experiment No. 1 to 10 can be performed using simulators / emulators like Keil / Proteus / Mbed
Studio. Experiment No. 11 to 13 can be performed using any of the kits available.
Assessment :
Term Work: Will be based on Continuou s Assessment

1. Laboratory work will be based on the syllabus with minimum 10 experiments.

Experiments 20 Marks
Attendance 5 marks
2. Practical term work will be evaluated by the subject teacher and documented
accordingly.

End Semester Practical Exam ination:

Practical and oral examination will be conducted by the University based on suggested
practical list and entire syllabus.

Page 121

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term
Work Practical Oral Total
MCA L24 Advanced Web
Technologies 04 02 50 30 20 100
Pre-requisite:
1) Understanding of Object Oriented Programming concepts
2) Basic knowledge of web technologies
Lab Course Objectives:
Sr.No. Course Objective
1  Understand advanced windows and web development t echniques using dot NET
2 Use Microsoft ADO.NET to access data in web Application
3 Impart understanding of Web Techniques and Design Web Services
4 Learn advanced web framework MVC with razor
Lab Course Outcomes:
Sr.No. Outcome Bloom
Level
CO1 Deve lop Web applications using various controls and programming
techniques. Apply ing
CO 2 Implement Data Binding applications using ADO.NET Analyzing
CO 3 Solve identity management problems in web Applications application
using session management and AJAX concepts. Evaluating
CO 4 Create modern web applications using Web Services and MVC5 Creating
Description:

Module
No Detailed Contents Hrs
1 Module: Basics of C#
Windows Forms Application, Classes and Objects, UI Controls, Inheritance,
Interfaces, Ab stract Classes
Self-Learning Topics : Indexers and Strings Manipulations 4
2 Module: Introduction to ASP.NET
Design Simple web pages(Data types, variables, operators,ASP.net Objects),
Basic Server side controls,
Working with CrossPage, Postback And Auto postback ,Advanced Web server
controls (validation, Calendar, AdRotator, Navigation, File upload),Build an 12

Page 122

Applications using Angular JS,JQuery and NodeJS, Websites using Master
Pages (creating master and content pages)
Self-Learning Topics : Themes and sk ins
3 Module: Database Programming in ASP.NET
Connected and disconnected Architecture of ADO.NET , Commands, Datasets,
Data Readers, Data Adapters, Working with Stored Procedures,
Data bound controls ( DataList, DetailsView, FormView, GridView, ListVie w,
Repeater), LINQ with ASP.NET,LINQ Introduction, Mapping data model to
an Object model, Introducing query syntax, Entity Framework
Self-Learning Topics : Charts and Data Pagers 10
4 Module: Session Management and AJAX
Client Side State Management - View State, Query String, Cookie, Hidden
Fields ,Server Side State Management Various State Management Techniques
- Profiles, Session State, Application State, cache ,ASP.NET Applications with
AJAX , AJAX Controls, Testing an ASP.NET Ajax application, Global. asax
and Web Config,Caching
Self-Learning Topics : Web Parts 8
5 Module: Web Services and WCF
Creating and Consuming a XML Web Service -Simple and Database ,Creating
and Consuming a WCF service – Simple and Database
Self-Learning Topics : Caching Web servi ce responses 6
6 Module: ASP.NET MVC
Designing MVC application, Creating a Simple Data -Entry Application with
validations, Using Automatically Implemented Properties, Using Object and
Collection Initializers, Using Extension Methods, Using Lambda Express ions,
Programs based on MVC Pattern, FORMS AND HTML HELPERS, Define
and access the model type ,
Reduce duplication in views, Specify a default layout, Pass data values to the
view from the controller , Generate different content based on data values, Add
a namespace to a view
Self-Learning Topics : Xamarin application 12
Reference Books :

Reference
No Reference Name
1 Spaanjaars, Imar. Beginning ASP. NET 4.5. 1: in C# and VB. John Wiley &
Sons, 2014. ISBN: 1861009038
2 Evjen, Bill, Scott Hanselman, and Devin Rader. Professional ASP. NET 3.5
SP1 Edition: In C# and VB. John Wiley & Sons, 2011. ISBN: 0470187573
3 Freeman, Adam. "Pro asp. netmvc 5 platform." Pro ASP. NET MVC 5
Platform. Apress, Berkeley, CA, 2014. ISBN: 1430265418
4 Allen, K. Scott, et al. Professional ASP. NET MVC 5. Wrox Press, 2014. ISBN:
1118794753
5 Walther, Stephen. ASP. Net 4.5 Unleashed. Pearson Education India, 2012.
ISBN: 067233688X
6 Nagel, Christian, Jay Glynn, and Morgan Skinner. Professional C# 2008 John
Wiley & Sons, 2014. ISBN: 0470191376
7 MacDonald, Matthew. ASP. NET: The Complete Reference. McGraw -Hill,
Inc., 2002. ISBN: 0072125764
8 Schildt, Herbert. C# 4.0: the complete reference. Tata McGraw -Hill Education,
2010.
Web References:

Page 123

Reference
No Reference Name
1 https://docs.microsoft.com/en -us/aspnet/core/?view=aspnetcore -3.1
2 ttps://docs.microsoft.com/en -us/aspnet/mvc/overview/getting -
started/introduction/getting -started
3 https://docs.microsoft.com/en -us/dotnet/csharp/programming -guide/
4 https://www.w3schools.com/asp/default.ASP
5 en.wikipedia.org › wiki › Web_service
6 https://docs.microsoft.com/en -
us/aspnet/c ore/mvc/views/razor?view=aspnetcore -3.1

Suggested list of experiments
Practical No Problem Statement
1 Design UI based applications using basic Windows forms Controls
2 Design Applications using Classes and Objects
3 Design Applications using Inher itance and Abstract Classes
4 Design a Web Application for an Organization with Regisration forms and
advanced controls
5 Create website using master page concept.
6 Build an angular web application.
7 Design a webpage to demonstrate a connection orien ted architecture.
8 Design a webpage to demonstrate a disconnected architecture.
9 Create a webpage that demonstrates the use of data bound controls of
ASP.NET.
10 Design a webpage to demonstrate the working of a simple stored procedure.
11 Design a we bpage to demonstrate the working of parameterized stored
procedure.
12 Design a webpage to display the use of LINQ.
13 Build websites to demonstrate the working of entity framework in dot net.
14 Design Web Applications using Client Side Session Managem ent
15 Design Web Applications using Server Side Session Management Techniques
16 Design Web Application to produce and Consume a web Service
17 Design Web Application to produce and Consume a WCF Service
18 Design MVC based Web applications.

Assessm ent:
Term Work: Will be based on Continuous Assessment

1. Laboratory work will be based on the syllabus with minimum 10 experiments.
Experiments 40 marks
Attendance 10 marks
2. Practical will be evaluated by the subject teacher and documented according t o a rubric
End Semester Practical Examination:
Practical and oral examination will be based on the suggested practical list and entire
syllabus.

Page 124

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term Work Practical Oral Total
MCAL25 User Interface
Lab 02 01 25 30 20 75

Pre-requisite: Basic knowledge of Web Technologies and Software Engineering.
Lab Course Objectives
Sr.No. Course Objective
1 Understand the importance of User Interface Design (UI) Process.
2 Analyze how to design Effective and Efficient User Interfaces for intended users.
3 Learn techniques for Prototyping and Evaluating User Experience.
4 Apply the concept of Good UI and User Experience (UX).
Lab Course Outcomes:
Sr.No. Outcome Bloom Level
CO1 Interpret user needs and context of User Interface design
Specification Analyzing
CO2 Demonstrate the tools and techniques for designing informing
models Applying
CO3 Develop high fidelity prototype for end to end solution. Applying
CO4 Apply best pr actices for evaluating user experience. Applying
Description:
Module Detailed Contents Hrs
1 The UI life cycle:
Introduction to UI life cycle and UI tools.
Self Learning Topics: phases and importance of UI life cycle 4
2 Requirement gathering:
Include the business purpose and user needs.
Self Learning Topics: Understand the user, types of users, requirement
gathering techniques, contextual enquiry. 2
3 Analysis:
User analysis,Task analysis,Domain analysis
Self Learning Topics: Identifying the types of tasks, design objects model,
contextual analysis. 4
4 Design:
Scenario, Storyboard designs.
Self Learning Topics: Principles of good design, Mental model 4
5
Build and test the low fidelity prototype:
Build a prototype. Paper prototype, Wireframe
Prepare a briefing for test users.(test the prototype)
Self Learning Topics: Types of prototypes 4

Page 125

6 Implementation:
Working implementation of the chosen project. Light weight page loading,
optimal design.
Self Learning Topics: Implementation tool, user fri endly design. 6
7 Testing:
Evaluate the interface with a small user test and write a final reflection
Self Learning Topics : Testing Techniques 2
Reference Books :

Reference
No Reference Name
1 Norman, Donald , The Design of Everyday Things, Basic Books , ISBN 978-0-
465-06710 -7
2 Steve Kru g, Don't Make Me Think, Revisited: A Common Sense, New
Riders,ISBN ,Third edition, 978 -0-321-96551 -6
3 Golden Krishna ,The Best Interface Is No Interface, New Riders, First Edition,
ISBN 978-0-133-89041 -9.
4 Theo Mandel, The Elements of User Interface Design, Wiley, First Edition,
978-0471162674
5 Wilbert O. Galitz, The Essential Guide to User Interface Design : An
Introduction to GUI Design Principles and Techniques, Wiley , Second Edition,
978-812650 2806
6 Rex Hartson and Pardha S Pyla, The UX Book, Morgan Kaufmann,
9780123852410
Web References:
Reference
No Reference Name
1 http://jjg.net/ia/elements.pdf
2 http://www.boxesandarrows.com
3 https://www.nngroup.com/articles/
List of Experiments:
1 Introduction to UI life cycle and UI tools.
2 Project Proposal and Requirement Gathering (Choose the project)
The project should be a web, desktop, or mobile interfa ce. If the chosen project is a mobile
application, note that it must at least be possible to simulate the project, since one of the
prototypes will be such a simulation that can be evaluated.
3 Analysis
Problem statement:
Briefly state the problem(s) th at the project will seek to solve. Take the user's point of view.
Consider what the user's goals are, and what obstacles lie in the way.

Output :
 Write up a user analysis, task analysis (identify three tasks of the chosen problem),
and domain analysis cl early, concisely, and completely.
 Design a persona.
 A problem object model or entity -relationship diagram.

Page 126

4 Design
Creation of Scenario
Write a scenario that involves all three of the tasks identified for the chosen project.
Output:
 Explain the Scena rio
 Sketch the scenario (use any tool or hand sketches)
 Draw a mental model.
5 Prototype
Creating a Paper Prototype and High Fidelity prototype (Wire Frame)usingFigma tool.
Output
 Paper prototype
 Wireframe.
6 Implementation
The code should be optimal a nd user friendly.

7 Usability Evaluation of the Design
Testing of User Interface from Third Party( Test scripts)
Output
 Test Script


Assessment :
Term Work: Will be based on Continuous Assessment

1. Laboratory work will be based on any two case studies or mini projects based on the
above syllabus.
Experiments 20 marks
Attendance 5 marks

2. Practical will be evaluated by the subject teacher and documented according to a rubric

End Semester Practical Examination:
Practical and oral examination will be based on suggested practical list and entire syllabus.

Page 127

Course
Code Course
Name Contact
Hours Credits
Assigned Examination Scheme
Term Work Practical Oral Total
MCAL26 Networking
with Linux 02 01 25 30 20 75

Pre-requisite: Linux, Computer N etworks
Lab Course Objectives
Sr.No. Course Objective
1  Train to install Network Simulation tool on Linux
2  Familiarize to a Network Simulation Tool
3 Design various network topologies using Network Simulation tool
4 Analyze network traffic using net work sniffing software
Lab Course Outcomes:
Sr.No. Outcome Bloom Level
CO1 Demonstrate installation and configuration of Network simulator Understand ing
CO 2 Construct network topologies using Network Simulator Apply ing
CO 3 Analyze network traffic u sing network sniffing software Analyzing
CO 4 Design and develop solutions to complex network problems using
Network Simulator and Network Software Creating
Description:
Module
No. Detailed Contents Hrs
1 Introduction to Network Simulation and sniff ing software
 Installation of NS3 on Linux
 Installation of NetAnim
 Installation of Wireshark
Self learning: Linux Operating System Commands for installation 6
2 Client Server Network topology using NS -3
 Program to Create simple topology
 Programs to differe nt types of topologies
 Program for complex topologies
 Program for client server networks
Self learning:Network Programming in Java 10
3 Animating the Network
 Introduction to NetAnim
 Animation a network using NetAnim
Self learning: Other Animation tools available with NS3 4
4 Analyzing Network traffic
 Monitoring the Network using WireShark
Self learning: Parameters used for analyzing Network Traffic 2
5 Real time problem Solving
 Mini Project of Creating Complex Networks using NS3,
NetAnim and WireShark
Self learning: Integrating NS3,NetAdmin and Wireshark 4
Reference Books :

Page 128

Reference No Reference Name
1 1. Learning Network Programming with Java by Richard M. ...
2 Java Network Programming, Third Edition, by Elliotte Rusty Harold.Oreily Pub
3 TCP/IP So ckets in Java, Second Edition: Practical Guide for Programmers (The
Practical Guides)2nd Edition by Kenneth L. Calvert , Michael J. ...
Web References:
Referenc
e No Reference Name
1 https ://www.nsnam.org/docs/release/3.9/manual.pdf
2 https://www.nsnam.org/tutorials/NS -3-LABMEETING -1.pdf
3 https://www.nsnam.org/w iki/Installation
4 https://www.nsnam.org/releases/ns -3-30/download/
5 https://www.nsnam.com/2014/ 08/installing -netanim -software -for-ns3-in.html
6 https://www.wireshark.org/download.html
7 https://ww w.wireshark.org/docs/wsug_html_chunked/ChBuildInstallWinInstall.ht
ml
8 https://www.howtogeek.com/104278/how -to-use-wireshark -to-capture -filter -and-
inspect -packets/
Suggested list of experiments
Practical No Problem Statement
1 Installation of NS -3 in Linux
2 Installation of NetAnim
3 Installation of WireShark
4 Program to simulate traffic between two nodes
5 Program to simulate star topology
6 Program to simulate bus topology
7 Program to simulate mesh topology
8 Program to simulate hybrid topology
9 Program to simulate UDP server client
10 Program to simulate DHCP server and n clients
11 Program to simulate FTP using TCP protocol
12 Animate a simple network using NetAnim in Network Simulator
13 Animate Three way handshake for TCP connection using NetAnim
14 Program to assign IPv4 Addresses in NS3
15 Analyze the network traffic using WireShark
16 Analyze the performance parameters of network using Wire Shark
Assessment :
Term Work: Will be based on Continuous Assessment
1. Laboratory work will be based on the syllabus with minimum 10 experiments, mini
project has to be assessed internally.
Experiments 20 marks
Attendance 5 marks
Practical will be evaluated by the subject teacher and documented according to a rubric
End Semester Practical Examination:
Practical and oral examination will be based on suggested practical list and entire syllabus.

Page 129

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term
Work Pract. Oral Total
MCAP2
1 Mini Project – 1 B 02 01 50 - -- 50

Pre-requisite: NIL
Lab Course Objectives: The course is aimed to
Sr. No. Course Objective
1 Conceptualize knowledge with emphasis on team w ork, effective communication,
critical thinking and problem solving skills.
2 Adapt to a rapidly changing environment by having learned and applied new skills
and new technologies.
3 Acquaint with the process of applying basic computer applications and p rovide
solutions to the problems in various application domains.
Lab Course Outcomes: On successful completion of course learner/student will be able to
Sr. No. Course Outcome Bloom Level
CO1 Demonstrate the ability to produce a technical document. Under standing
CO2 Apply software project management skills during project work. Applying
CO3 Build small groups to work effectively in team on medium scale
computing projects. Creating
CO4 Design and evaluate solutions for complex problems. Creating


Guide lines for Mini Project:
1. Students shall form a group of 2 to 3 students .
2. Students should do survey and identify needs, which shall be converted into problems in
consultation with the faculty Supervisor/Guide/HOD/Internal Comm ittee of faculties.
The projec t contact hours shall be allotted in the time table and 2 hours work load shall be
considered for the guide/ supervisor .
3. Students shall submit an implementation plan in the form of Gantt/PERT/CPM chart,
which will cover weekly activity of mini project.
4. A log book to be prepared by each group, wherein the group can record weekly work
progress, Guide/Supervisor can verify and record notes/comments.
5. Faculty may give inputs during mini project activity; however, focus shall be on self -
learning.

Page 130

6. Students in a group shall understand the problem effectively, propose multiple solutions
and select the best possible solution in consultation with G uide/ Supervisor.
7. Students shall convert the best solution into a working model using various components
of their doma in areas and demonstrate.
8. The solution to be validated with proper justification and project report to be compiled in
standard format of University of Mumbai.

Assessment of Mini Project:
I) Term work (25 Marks):
 The progress of the mini project to be eva luated on a continuous basis.
 In continuous assessment focus shall also be on each individual student, assessment based
on individual‟s contribution in group activity, their understanding and response to questions.
 Distribution of Term work marks shall be as below;
o Marks awarded by guide/supervisor based on log book : 10
o Self contribution and use of skill set in project : 10
o Quality of Project report : 05

II) Mini Project Internal Examination (25 Marks):
 Report should be prepared as per the guidelin es issued by the University of Mumbai.
 The students shall present a seminar on Mini project and demonstrate their understanding of
need/problem.
 Mini Project shall be evaluated through a presentation and demonstration of working model
by the student proje ct group to a pane l of examiner at Institute level.
 Mini Project shall be assessed based on following points:
 Quality of survey/ need identification.
 Clarity of Problem definition based on need.
 Innovativeness in solutions.
 Feasibility of proposed problem solutions and selection of best solution.
 Cost effectiveness.
 Societal impact.
 Full functioning of working model as per stated requirements.
 Effective use of skill sets.
 Contribution of an individual as a member or leader.
 Clarity in written and oral comm unication.

Page 131

Semester III

Page 132

Program Structure for
Second Year Master of Computer Applications
UNIVERSITY OF MUMBAI (With Effect from 2021 -2022)
Semester III
Teaching Scheme

Course
Code Course Name Teaching Scheme
(Contact Ho urs) Credits Assigned
Theory Pract. Tutoria
l Theory Pract. Tutorial Total
MCA31 Big Data Analytics
and Visualization 3 -- -- 3 -- -- 3
MCA32 Distributed System
and Cloud
Computing 3 -- -- 3 -- -- 3
MCAE33 Elective - 3 3 -- -- 3 -- -- 3
MCAE34 Electi ve - 4 3 -- 1 3 -- 1 4
MCAL31 Big Data Analytics
and Visualization
Lab -- 2 -- -- 1 -- 1
MCAL32 Distributed System
and Cloud
Computing Lab -- 2 -- -- 1 -- 1
MCALE33 Elective 3 Lab -- 2 -- -- 1 -- 1
MCAL34 Skill based Lab
Mobile Computing
Lab -- 4 -- -- 2 -- 2
MCAL35 Software Testing
Quality Assurance
Lab -- 2 -- -- 1 -- 1
MCAP31 Mini Project: 2 A -- 2 -- -- 1 -- 1
Total 12 14 1 12 07 1 20

Page 133

Program Structure for
Second Year Master of Computer Applications
UNIVERSITY OF MUMBAI (With Eff ect from 2021 -2022)
Semester III
Examination Scheme


Course
Code Course Name
Examination Scheme

Theory Term
Work Pract &
oral
Total
Internal
Assessment End Sem
Exam
Exam.
Duration
(in Hrs)
CA Test Avg
MCA 31 Big Data
Analytics and
Visualization 20 20 20 80 3 -- -- 100
MCA32 Distributed
System and Cloud
Computing 20 20 20 80 3 -- -- 100
MCAE33 Elective - 3 20 20 20 80 3 -- -- 100
MCAE34 Elective - 4 20 20 20 80 3 25 -- 125
MCAL31 Big Data
Analytics a nd
Visualization Lab -- -- -- -- -- 25 50 75
MCAL32 Distributed
System and Cloud
Computing Lab -- -- -- -- -- 25 50 75
MCALE33 Elective 3 Lab -- -- -- -- -- 25 50 75
MCAL34 Skill based Lab
Mobile
Computing Lab -- -- -- -- -- 50 50 100
MCAL35 Software Testing
Quality Assurance
Lab -- -- -- -- -- 25 50 75
MCAP31 Mini Project: 2 A -- -- -- --
-- 50 - 50
Total -- -- 80 320
-- 225 250 875

Page 134

Elective 3


Sr. No. Course Code Course Name Lab Course Code
1 MCAE331 Blockchain MCALE331
2 MCAE332 Deep Learning MCALE332
3 MCAE333 Game Development MCALE333
4 MCAE334 Ethical Hacking MCALE334
5 MCAE335 Quantum Computing MCALE335


Elective 4

Sr. No. Course Code Course Name
1 MCAE341 Intellectual Property Rights
2 MCAE342 Green Computing
3 MCAE343 Management Information System
4 MCAE344 Cyber Security and Digital Forensics
5 MCAE345 Entrepreneurship Management

Page 135

Course
Code Course
Name Teaching Scheme Credits Assigned
MCA31 Big Data
Analytics
and
Visualizatio n Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 -- 80 100
Prerequisite: Some prior knowledge about SQL, Data Mining, DBMS would be benefi cial.
Course Objectives:
Sr.No. Course Objective
1 ● Provide an overview of exciting and growing field of big data analytics
2 Enchase the programming skills using big data technologies such as map reduce,
NoSQL, Hive, Pig
3 Use Spark shell and Spark appl ications to explore, process, and analyze distributed data
4 Teach the component of visualization and understand why visualization is important for
data analysis

Course Outcomes:
Sr.No. Outcome Bloom Level
CO1 Demonstrate the key issues in big data man agement and its associated
application for business decision Understanding
CO2 Develop problem solving and critical thinking skills in fundamental
enabling techniques like Map Reduce , NoSQL, Hadoop Ecosystem Apply ing
CO3 Use of RDD and Data Frame to create Application in Spark. Apply ing
CO4 Cond Implement exploratory data analysis using visualization Apply ing

Module Detailed Contents Hrs.
1 Introduction to Big Data and Hadoop:
Introduction to Big Data, Big Data characteristics, Types o f Big Data,
Traditional vs. Big Data ,Big Data Applications.
Hadoop architecture: HDFS,YARN 2, YARN Daemons.
Hadoop Ecosystem.
Self-Learning Topics: Yet Another Resource Negotiator YARN 1.X 6
2 HDFS and Map Reduce
HDFS: HDFS architecture, Features of HD FS,Rack Awareness,HDFS
Federation 6

Page 136

Map Reduce :The Map Task, The Reduce Task, Grouping by Key,Partitioner
and Combiners, Detail of Map Reduce Execution.
Algorithm Using Map Reduce:
Matrix and Vector Multiplication by Map Reduce
Computing Selection and Proje ction by Map Reduce
Computing Grouping and Aggregation by Map Reduce
Self-Learning Topics: Concept of Sorting and Natural Joins
3 NoSQL:
Introduction to NoSQL, No SQL Business drivers
NoSQL Data architecture patterns: key value stores, Column family Sto res,
Graph Stores, Document Stores.
NoSQL to manage big data: Analyzing big data with shared nothing
architecture, choosing distribution master slave vs. peer to peer.
HBASE overview,HBASE data model, Read Write architecture.
Self-Learning Topics: Cassandr a Case Study 5
4 Hadoop Ecosystem: HIVE and PIG
HIVE: background, architecture, warehouse directory and meta -store, HIVE
query language, loading data into table, HIVE built -in functions, joins in
HIVE, Partitioning.
HiveQL: querying data, sorting and aggr egation,
PIG : background, architecture, PIG Latin Basics, PIG execution
modes, PIG processing – loading and transforming data, PIG built -in
functions, filtering, grouping, sorting data Installation of PIG and PIG Latin
commands.
Self-Learning Topics :Cloudera IMPALA 6
5 Apache Kafka: Kafka Fundamentals, Kafka architecture,
Case Study: Streaming real time data (Read Twitter Feeds and Extract the
Hashtags)
Apache Spark:
Spark Basics, Working with RDDs in Spark, Spark Framework, aggregating
Data with Pa ir RDDs, Writing and Deploying Spark Applications, Spark SQL
and Data Frames.
Self-Learning Topics: KMeans and Page Rank in Apache Spark 9
6 Data Visualization: Explanation of data visualization, Challenges of big data
visualization, Approaches to big da ta visualization, D3 and big data, Getting
started with D3, Another twist on bar chart visualizations, Tableau as a
Visualization tool, Dashboards for Big Data - Tableau.
Self-Learning Topics: Splunk via web Interface. 8

Reference Books :
Reference
No Reference Name
1 Tom White, “HADOOP: The definitive Guide” O Reilly 2012, Third Edition,
ISBN: 978 -1-449-31152 -0
2 Chuck Lam, “Hadoop in Action”, Dreamtech Press 2016, First Edition ,ISBN:13

Page 137

9788177228137
3 Shiva Achari,” Hadoop Essential “ PACKT Publicat ions, ISBN 978 -1-78439 -
668-8
4 RadhaShankarmani and M. Vijayalakshmi ,”Big Data Analytics “Wiley
Textbook Series, Second Edition, ISBN 9788126565757
5 Jeffrey Aven,”Apache Spark in 24 Hours” Sam‟s Publication, First Edition,
ISBN: 0672338513
6 Bill Cham bers and MateiZaharia ,”Spark: The Definitive Guide: Big Data
Processing Made Simple “ O‟Reilly Media; First edition, ISBN -10: 1491912219;
7 James D. Miller,” Big Data Visualization” PACKT Publications.ISBN -
10: 1785281941
Web References:
Reference
No Refe rence Name
1 https://hadoop.apache.org/docs/stable/
2 https://pig.apache.org/
3 https://hive.apache.org/
4 https://spark.apache.org/documentation.html
5 https://help.tableau.com/current/pro/desktop/en -us/default.htm
Assessment :
Continuous Assessment: 20 marks
Followi ng measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The fac ulty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test sha ll be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -questions of
5 marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of ea ch module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 138

Course
Code Course
Name Teaching Scheme Credits Assigned
MCA32 Distributed
System and
Cloud
Computing Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 -- 80 100

Pre-requisite: Computer Networks, Operating Systems
Course Objectives: The course aim to
Sr.No. Course Objective
1  Introduce conce pts of Distributed Operating System, design issues IPC and RMI.
2 Understand the concepts of clock synchronization and shared memory.
3 Analyze various algorithms in Distributed System Management, File management
and process management.
4 Analyze the pr inciples and paradigm of Cloud Computing.
5 Understand the various design issues and challenges in cloud computing
Course Outcomes: On successful completion of course learner/student will be able to
Outcome Bloom Level
CO1 Illustrate principles and com munication protocols of Distributed
systems Understanding
CO 2 Analyze clock synchronization and various algorithms
Analyzing
CO 3 Analyze Distributed shared memory and management concepts. Analyzing
CO 4 Analyze Cloud computing and cloud models Anal yzing

Modul
e Detailed Contents Hrs
1 Module : Introduction to Distributed Computing Concepts:
Basic concepts of distributed systems, distributed computing models, issues
in designing distributed systems
Inter Process Communication 09

Page 139

Fundamental concepts re lated to inter process communication including
message passing mechanism, Concepts of group communication
Remote Communication
Remote Procedural Call (RPC), Remote Method Invocation (RMI)
Self Learning Topics: Case study on Java RMI
2 Module : Clock s ynchronization:
Introduction of clock synchronization, Global state, Mutual Exclusion
Algorithms, Election algorithms.
Self Learning Topics: Synchronization in Wireless Networks 04
3 Module : Distributed Shared Memory:
Fundamental concepts of DSM, types o f DSM, various hardware DSM
systems, Consistency models, issues in designing and implementing DSM
systems.
Self Learning Topics: MemNet Architecture 05
4 Module : Distributed System Management:
Resource Management Scheduling Algorithms, Task Assignment, Lo ad
balancing approach, Load sharing approach
Process Management
Process Migration Mechanism, Thread models
Distributed File System
Concepts of a Distributed File System (DFS), file models

Self Learning Topics: Case Study of anyone distributed system 06
5 Module : Introduction to Cloud Computing:
Cloud Computing history and evolution, benefits of cloud computing.
Cloud Computing Architecture
Cloud Architecture model, Types of Clouds: Public Private & Hybrid
Clouds, Cloud based services: Platform as a serv ice (PaaS), Software as a
service (SaaS), Infrastructure as a service (IaaS)
Self Learning Topics: Cluster computing, Grid computing, Fog computing 06
6 Module : Classification of Cloud Implementations:
Amazon Web Services, Microsoft Azure & Google Cloud -- Compute
Services, Storage Services, Network Services, Database services, Additional
Services.
Google AppEngine (GAE), Aneka, Comparativestudy of various Cloud
Computing Platforms.
Cloud Issues and Challenges
Cloud computing issues and challenges like Sec urity, Elasticity, Resource
management and scheduling, QoS (Quality of Service) and Resource
Allocation, Identity and Access Management
Self Learning Topics: Widows Azure Platform Appliance 10

Page 140

Reference Books :
Reference
No Reference Name
1 Prade ep K. Sinha , Distributed Operating System: Concepts and Design, PHI
Learning, ISBN No. 978 -81-203-1380 -4
2 Dr. SunitaMahajan , Seema Shah, Distributed Computing ,Oxford University
Press,Second Edition, ISBN No. 978 -01-980-9348 -0
3 Andrew S. Tanenbaum , Distributed Operating Systems, Pearson Education ,
ISBN No. 978-81-317-0147 -8
4 James Broberg and Andrzej M. Goscinski, Cloud Computing: Principles and
Paradigms Wiley, First edition, ISBN No. 978 -04-708-8799 -8
5 Anthony T.Velte, Toby J.Velte, Robert Els enpeter, Cloud Computing: A Practical
Approach, Tata McGraw Hill, First Edition, ISBN No. 978-00-706-8351 -8
6 RajkumarBuyya, Christian Vecchiola, S. ThamaraiSelvi, Mastering Cloud
Computing, Tata Mcgraw Hill, ISBN No. 978 -12-590-2995 -0
Web References:
Reference
No Reference Name
1 https://nptel.ac.in/courses/106/104/106104182/
2 https://webee.technion.ac.il/~idish/sigactNews/
3 https://curlie.org/Computers/Computer_Science/Distributed_Computing/
4 https://nptel.ac.in/courses/106102114/
5 https://n ptel.ac.in/courses/106104024/
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coor dinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be con ducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
End Semester The ory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -questions of 5
marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 141

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE33
1 Block Chain Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 -- 80 100
Pre-requisite:
Basic knowledge of cryptography, networking, distributed s ystems and expertise in object oriented
programming.
Course Objectives: Course aim to
Sr.
No. Course Objective
1 Provide the overview of the structure and mechanisms of Blockchain
2 Explain permissioned and decentralized Blockchain concepts
3 Understa nd cryptocurrency transactions and mining Blockchain.
4 Understand and write the smart contracts in Ethereum.
5 Understand the applications of Blockchain technology
6 Understand the hyperledger case studies in Blockchain.
Course Outcomes:
Sr. No. Outc ome Bloom Level
CO1 Explain Blockchain technologies and their components. Understanding
CO 2 Interpret the uses of cryptographic techniques in Blockchain Understanding
CO 3 Demonstrate the use of hyperledger fabric and its components Understanding
CO 4 Build the smart contracts in Ethereum Applying
CO 5 Analyze the use of Blockchain technology in various domains Analyzing

Module Detailed Contents Hrs
1 Module: Introduction: Basics of blockchain, History, Uses of
Blockchain, Structure of a block, Tr ansactions, Public Ledger,
Distributed Consensus. Peer to peer systems, centralized and
decentralized systems, Types of blockchain
Self-learning Topics: Basics of cryptography (Symmetric and
Asymmetric) RSA algorithm 04
2 Module: Cryptographic Primitives: Cryptographic hash functions –
collision free, hiding, puzzle friendly (properties), Hash Chain, Hash
tree- Merkle Tree, Public Key cryptography, Digital signatures. Use of
hash functions and digital signatures in blockchain 06

Page 142

Self-learning Topics: Basics o f data structure (Linked lists), Hash
Functions
3 Module: Bitcoin: Basics (Structure of block, creation of coins),
Double Spending, Script (FORTH), Mining Process, Objectives of
consensus mechanisms, Consensus in Bitcoin – Proof of Work, Sybil
Attack, Proof of Elapsed Time, Proof of Stake, Proof of Burn
Self-learning Topics: Other Cryptocurrencies. 08
4 Module: Permissioned Blockchain: Smart Contracts, Distributed
Consensus, Faults in DC, Algorithms – Paxos, RAFT, Byzantine Fault
Tolerance, Practic al BFT
Self-learning Topics: Distributed algorithms, Object oriented
Programming Concepts 06
5 Module: Ethereum: History, Architecture, Accout Types , Gas,
Transactions, Structure (Blocks, Transactions), Accounts, Ether, Gas,
Ethereum Virtual Machine, Eth ereum Mining process, Solidity.
Hyperledger Fabric : Features of hyperledger, Architecture, ordering
service, Transaction Flow, Membership and Identity Mangement,
Gossip Protocol
Self-learning Topics: BitcoinVsEthereum, EthereumVsHyperLedger 10
6 Module: C ase Study : Blockchain in Government (Digital Identity, Tax
Payments, Land Registration, Audit and Compliances), Supply Chain
Management, Financial Services 06

Reference Books :

Reference
No Reference Name
1 Arvind Narayanan, Joseph Bonneau, Edward Felt en, Andrew Miller, Steven
Goldfeder, Bitcoin and Cryptocurrency Technologies, Princeton University Press
2 Don Tapscott,AlexTapscott, Blockchain Revolution, ISBN No. 9781101980132
3 Mark Gates, Blockchain ultimate Guide to understanding Blockchain,
Bitco in,Cryptocurrencies, Smart Contracts and Future of money, Wise Fox
Publishing
4 VikramDhillon, David Metcalf, Max Hooper, Blockchain Enabled Applications,
Apress, ISBN No.13:978 -1-4842 -3081 -7
5 Melanie Swan,Blockchain Blueprint for a new economy, O'Reill y, First Edition,
ISBN No.978 -1-491-92049 -7
6 MayukhMukhopadhyay, Ethereum Smart Contract Development, Packt
publishing, First Edition, ISBN No.978 -1-78847 -304-0
7 Chris Dannen, Introducing Ethereum and Solidity, Apress, ISBN No.978 -1-4842 -
2535 -6
8 Mart in Quest, Cryptocurrency Master Bundle
9 Nitin Gaur, Luc Desrosiers, Petr Novotny, Venkatraman Ramakrishna, Anthony
O'Dowd, Salman A. Baset, Hands -On Blockchain with Hyperledger, Packt
Web References:
Referenc
e No Reference Name

Page 143

1 https://blockexplorer.com/
2 https://en.wikipedia.org/wiki/Digital_signature
3 https://www.usenix.org/legacy/events/osdi99/full_papers/castro/castro_html/castro.ht
ml
4 https://www.hyperledger.org/projects/fabric
5 https://hyperledger -fabric.readthedocs.io/en/release -2.0/
6 https://eprint.iacr.org/2017/375.pdf
7 https://hbr.org/2017/01/the -truth -about -blockchain
8 https://bitcoin.org/bitcoin.pdf
9 https://blockgeeks.com/guides/what -is-blockchain -technology/
10 https://www.cs.hmc.edu/~geoff/classes/hmc.cs070.200101/homework10/hashfuncs.h
tml
11 https://www.globalsign.com/en/ssl -information -center/what -is-public -key-
cryptography
12 https://searchsecurity.techtarget.com /definition/asymmetric -cryptography
13 https://www.cryptocompare.com/coins/guides/what -is-a-block -header -in-bitcoin/
14 https://github.com/ethereum/wiki/wiki/White -Paper
15 http://ethdocs.org/en/latest/introduction/index.html
16 https://blockgeeks.com/guides/proof -of-work -vs-proof -of-stake/
17 https://www.coindesk.com/learn/ethereum -101/how -ethereum -works
18 https://ww w.coindesk.com/short -guide -blockchain -consensus -protocols
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of th e
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks . The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.

End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -questions of
5 marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 144

Course
Code Course
Name Teaching Scheme Credit s Assigned
MCAE332 Deep
Learning Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 -- 80 100
Prerequisite: Basic knowledge of mathematical a nd machine learning concepts.
Course Objectives
Sr.No. Course Objective
1 To explain the concept of neural network and deep learning.
2 To understand appropriate learning rules for each of the architectures and learn several neural
network paradigms.
3 To understand major deep learning algorithms and the problem settings for problem solving
4 To learn different regularization techniques used in deep learning.
5 To understand the optimization algorithms used for training of deep learning models.
6 To learn deep learning algorithms -CNN and RNN to solve real world problems.
`Course Outcomes:
Sr. No. Outcome Bloom Level
CO1 Demonstrate concepts, architectures and algorithms of Neural Networks to
solve real world problems. Understanding
CO 2 Identif y deep feed -forward networks and different regularization techniques
used in Deep Learning. Applying
CO 3 Identify challenges in Neural Network optimization and different optimization
algorithms used in Deep learning models Applying
CO 4 Analyze deep lea rning algorithms which are more appropriate for various
types of learning tasks in various domains Analyzing

Module
No. Detailed Contents Hrs
01 ANN Algorithms :
Supervised Learning Network - McCulloch –Pitts Unit and Thresholding logic, Linear
Separabili ty, Multi -layer Perceptron Networks, Back -Propagation Network, factors 6

Page 145

affecting Backpropagation Training, Unsupervised Learning Networks - MaxNet.
Self learning Topic: -Mexican Hat Net.
02 Deep Feed -forward Networks :
Introduction to Deep Learning, Lear ning XOR, Gradient -Based Learning, Hidden Units,
Architecture Design, Other Architectural Considerations.
Self learning Topic: - Applications of Deep neural networks. 6
03 Regularization :
Regularization for Deep Learning - Dataset Augmentation, Noise Robu stness, Semi -
Supervised Learning, Multi -Task Learning, Early Stopping, Parameter Tying and
Parameter Sharing, Sparse Representations, Bagging and Other Ensemble Methods,
Dropout.
Self learning Topic: -Regularized Linear Regression. 7
04 Optimization for Training Deep Models:
Need for Optimization, Challenges in Neural Network Optimization, Basic Algorithms,
Parameter Initialization Strategies, and Algorithms with Adaptive Learning Rates -
AdaGrad, RMSProp, and Approximate Second -Order Methods -Newton‟s Meth od.
Self learning Topic: -Conjugate Gradients Method. 6
05 Convolutional Networks :
Motivation, Pooling, Convolutional layers, Additional layers, Residual Nets,
Applications of deep learning.
Self learning Topic: -Application of CNN. 7
06 Recurrent and Rec ursive Nets:
Unfolding Computational Graphs, Recurrent Neural Networks, Bidirectional RNNs,
Encoder -Decoder Sequence -to-Sequence Architectures, Deep Recurrent Networks,
Recursive Neural Networks.
Self learning Topic: -Application of RNN. 8
Reference Book s:
Reference No Reference Name
1 Dr. S. N. Sivanandam and Dr. S. N. Deepa, Principles of Soft Computing, John Wiley
2 S. Rajasekaran & G.A. VijayalakshmiPai, Neural Networks, Fuzzy Logic and Genetic
Algorithm: Synthesis and Applications, Prentice Hall o f India.
3 Goodfellow I., Bengio,Y., and Courville, A., Deep Learning, MIT Press, 2016
4 Christopher M Bishop., Pattern Recognition and Machine Learning, McGraw -Hill,
ISBN No0 -07-115467 -1.

Page 146

5 Satish Kumar, Neural Networks: A Classroom Approach, Tata McG raw-Hill
Education, 2004.
6 Simon Haykin, Neural Networks and Learning Machines, 3rd Edition Prentice Hall of
India, ISBN -10: 0 -13-147139 -2.
7 Anandita Das., Artificial Intelligence and Soft Computing for Beginners,
ShroffPublication.ISBN 9789351106159.
8 Raul Rojas, Neural Networks: A Systematic Introduction, 1996 ISBN 978 -3-540-
60505
9 Deep Learning Tutorial Release 0.1, LISA lab, University of Montreal
Web References:
Reference No Reference Name
1 https://olympus.greatlearning.in/courses/10905/pages/courseoutline?module_item_id=
445065
2 https://www.youtube.com/watch?v=ve -Tj7kUemg&feature=youtu.be
3 https://www.coursera.org/specializations/deep -learning
4 https://www.tutorialspoint .com/python_deep_learning/index.htm
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/H OD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
End Semes ter Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on the entire syllabus wherein 4 sub -
questions of 5 marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 147

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE 33
3 Game
Development Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 -- 80 100

Pre-requisite:
Basic understanding of any Object Oriented Programming using C#
Course Objectives : Course aim to
Sr.No. Course Objective
1 Learn basic Principles of Game Development
2 Understand components required to design a Game
3 Learn how to script gaming applications
4 Evaluate and use techniques of game development
Course Outcomes:
Sr.No. Outcome Bloom Level
CO1 Demonstrate Principles of Game Development Understand ing
CO 2 Build applications using various components of Game
development Apply ing
CO 3 Develop multilayered and interactive games Analyzing
CO 4 Solve Problems in 2D game development Creating

Module Detailed Contents Hrs
1
Module: Introduction to Game Development
Game Development, Genres of Game Development,Game development
applications, Role of a Game Developer,A Playcentric Desi gn
Process,Designing Your Game,Managing game development projects,The
Structure of Games,Frameworks and tools for Game Development
Self Learning Topics: Game Design Strategies 6
2 Module:Game Development Components
Game Objects,Models,Materials and Texture s,Trrain,Environments,Lights
and Cameras, Sound Effects 7

Page 148

Self Learning Topics: Game Art
3 Module:Scripting for Game development
Difference between unity and C# scripting, Scripting basics, Variables,
Operators, Conditionals, Iterations, Methods, Input,Cla sses
Self LearningTopics: Exception Handling 7
4 Module: Managing State and Transitions
Identifying the Action Objects, Developing a State Machine, Lookup Table,
Object Metadata, Processing the Auxiliary Objects, Handling Object
Visibility, Handling Specia l Cases,Collisons
Self LearningTopics: Gaming Ethics 7
5 Module: Physics and Special Effects
Adding New Assets, Combining Physics and Keyframe Animation, Particle
Systems, Other Special Effects,Collisions,Prefabs and animations, Unity
Physics Joints,Unity 2D Effectors
Self LearningTopics: Virtual World 7
6 Module:Inventory Logic
Using Layers,Creating the Inventory Screen,Adding Inventory
Icons,Organizing the Inventory Objects, InventoryLayout,Inventory
overflow
Self LearningTopics: Dialog Trees 6

Reference Books :

Referen
ce No Reference Name
1 Fullerton, Tracy. Game design workshop: a playcentric approach to creating
innovative games. CRC press, 2014.ISBN: 1482217171
2 Schell, Jesse. The Art of Game Design: A book of lenses. CRC press, 2015. ISBN:
149875 9564
3 Blackman, Sue. Beginning 3D Game Development with Unity 4: All -in-one, multi -
platform game development. Apress, 2013. ISBN: 1430248998
4 Goldstone, Will. Unity game development essentials. Packt Publishing Ltd, 2009. ISBN:
184719818x
5 Murray, Je ff W. C# game programming cookbook for Unity 3D. CRC Press, 2014.
6 Paris Buttfield -addison , Jon Manning , Tim Nugent,Unity Game Development
Cookbook: Essentials For Every Game, O'reilly Media, ISBN: 1491999152
7 Geig, Mike. Sams Teach Yourself Unity Ga me Development in 24 Hours. Pearson
Education, 2014. ISBN -13: 978 -0-672-33696 -6
8 Norton, Terry. Learning C# by developing games with unity 3D. Packt Publishing Ltd,
2013. ISBN: 1849696586
9 Saunders, Kevin, and Jeannie Novak. Game development essentials : Game interface

Page 149

design. Cengage Learning, 2012. ISBN -13: 978 -1-305-11054 -0

Web References:
Referen
ce No Reference Name
1 www.unity.com
2 https://en.w ikipedia.org/wiki/Video_game_development
3 https://www.gamedesigning.org/video -game -development/
4 https://github.com/Kavex/GameDev -Resources
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The facul ty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks each. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of each test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
End Semester Theory Examination:

1. Question paper will comprise of total 06 questions, each car rying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -questions of 5
marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightag e of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 150

Cou
rse
Code Course
Name Teaching Scheme Credits Assigned
MCAE334 Ethical
Hacking Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 -- 80 100
Pre-requisite: Networking concepts, Structured Query Language, encryption algorithms
Course Objectives : course aim to
Sr.No. Course Objectiv e
1 Teach students to think like an ethical hacker and at the same time follow the code
of professional ethics and the prescribed cyber laws.
2 Make oneself aware of the cybercrimes that are taking place in the real world.
3 Learn about the different h acking tools and techniques and practically use these
tools to gain better understanding of the ethical hacking concepts.
4 Provide a deep understanding of security issues, threats and concerns in the cyber
world and provide countermeasures to curb hackin g.
Course Outcomes: On successful completion of course learner/student will be able to
Sr.No. Outcome Bloom Level
CO1 Recall the networking, sql, and encryption algorithm concepts to
further study ethical hacking techniques, threats, tools and
prevention against attacks. Remembering
CO 2 Understand ethical hacking concepts, cases, ethics and cyberlaws. Understanding
CO 3 Apply available hacking tools to find a solution to a given hacking
issue. Applying
CO 4 Analyze and classify the real -world hacking cases and situations. Analyzing

Module Detailed Contents Hrs
1 Module: Introduction to ethical Hacking:
What is ethical hacking? Types of hacking, advantages, disadvantages and
purpose of hacking, Types of hackers, Code of ethics, Types of attacks and
attack vector types, Prevention from hackers, The Indian IT Act 2000 and 04

Page 151

Amendments to the Indian IT Act(2008) ,Phases of hacking.
Self-Learning Topics: ethical hacking tools
2 Module: Footprinting and Reconnaissance.
What is footprinting? Active and pas sive footprinting, purpose of footprinting
, objectives of footprinting, footprinting threats, Types of footprinting,
footprinting countermeasures.
Self-Learning Topics: footprinting tools 05
3 Module: Scanning networks, Enumeration and sniffing:
Scanning networks:
Network scanning and its types, objectives of network scanning, scanning
live systems, scanning techniques -TCP Connect / Full Open Scan, Types of
Stealth scans,
port scanning countermeasures, IDS evasion techniques, Banner grabbing
and its tools, vulnerability scanning, proxy servers, anonymizers, IP spoofing
and its countermeasures.
Enumeration and Sniffing:
What is Enumeration? Enumeration techniques, Enumeration types,
Enumeration countermeasures, what is sniffing? Wiretrapping and its types,
packet sniffing, sniffing threats, how sniffers work?, sniffing methods -ARP
spoofing and MAC flooding, active and passive sniffing, types of sniffing
attacks, sniffing countermeasures, sniffing detection techniques.
Self-Learning Topics: Scanning, enumerati on and sniffing tools. 08
4 Module: Trojans and other Attacks:
Worms, viruses, Trojans, Types of worms, viruses and worms, Preventing
malware attacks, types of attacks : (DoS /DDoS), Waterhole attack, brute
force, phishing and fake WAP, Eavesdropping, Man -in-the-middle, buffer
overflow, DNS poisoning, ARP poisoning, Identity Theft, IoT Attacks, BOTs
and BOTNETs, Steganography - text, image and audio and video, types of
Social Engineering: Physical social engineering , Remote social engineering
and hybrid s ocial engineering.
Self-Learning Topics: case studies, malware tools and steganographic
tools. 08
5 Module: Hacking web servers, web applications and sql injection:
Session hijacking:
What is session hijacking? , why session hijacking is successful? sessi on
hijacking techniques, session hijacking process, Types of session hijacking, 08

Page 152

session hijacking countermeasures: protecting and preventing,
Hacking web servers and web applications:
Causes of webservers being compromised, web server attacks, stages of we b
server attacks, defending against web server attacks, web application
components, its working, architecture, web server attack vectors, web
application threats and counter measures.
SQL Injection:
What is SQL injection, SQL injection threats, SQL injecti on attacks, SQL
injection detection, Types of SQL injection, SQL injection methodology,
SQL injection prevention and countermeasures.
Self-Learning Topics: tools of session hijacking, web servers and
applications and
SQL injection.
6 Module: Wireless network hacking, cloud computing security,
cryptography, Pen testing:
Types of wireless Architecture, wireless encryption techniques -WEP and
WPA, breaking WEP/WPA and defending WPA encryption, wireless
Sniffing, Characteristics, types of cloud computing s ervices, models and
benefits, threats and attacks, cryptography and its objectives, cryptography
types, cryptography attacks, what is Pen Testing, need for pen testing, types
and techniques of pen testing, phases of pen testing.
Self-Learning Topics: Tool s of WEP/WPA, cloud computing, cryptography,
Pen testing. 07


Reference Books :

Reference
No Reference Name
1 Matt Walker, All -In-One-CEH -Certified -Ethical -Hacker -Exam -Guide.
2 Manthan Desai Basics of ethical hacking for beginners.
3 SunitBelapure and Nina Godbole, Cyber Security: Understanding Cyber Crimes,
Computer Forensics and Legal Perspectives.
4 Srinivasan, J. Suresh, Cloud Computing: A practical approach for learning and
implementation, Pearson.
5 Sean -Philip Oriyano, Sybex, Certified Eth ical Hacker Study Guide v9, Study Guide
Edition,2016.
6 Emmett Duley and Chuck Easttom ,Comptia Security+ Study Guide.
7 Alana Maurushat, Ethical Hacking.
8 TutorialsPoint Professionals, Ethical Hacking by TutorialsPoint.
Web References:
Refer ence
No Reference Name
1 Code of ethics link https://cert.eccouncil.org/code -of-ethics.html
2 https://arc.bukancoder.co/Certified -Ethical -Hacker -Module -V8/

Page 153

3 https://www.edureka.co/blog/steganography -tutorial
4 https://www.guru99.com/how -to-hack -using -social -enginering.html
Assessment :
Continuous Ass essment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done through out the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
End Semester Theory Examination:

1. Question paper will comprise of total 0 6 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -questions of 5
marks each will be asked.
4. Remaining questions will be randomly selected from all t he modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 154

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE335 Quantum
Computing Contact Hours
Theory Tutorial Theory Tutorial Total
3 -- 3 -- 3
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 -- 80 100

Pre-requisite: Basic understanding of Physics and Mathematics
Course Objectives : Cour se aim to
Sr.No. Course Obje ctive
1  Impart the basic concepts of the emerging field of Quantum Computing
2 Learn and use various Quantum Computing algorithms
3 Demonstrate the working of basic quantum computing operations
4 Identify the basic requirements for implementing Quantu m Computers
Course Outcomes: On successful completion of course learner/student will be able to
Sr.No. Outcome Bloom Level
CO 1 Understand basic principles and components of Quantum
Computing Understand ing
CO 2 Analyze Quantum Computing algorithms
Analyzing
CO 3 Design programs to perform basic Quantum Computing operations Creating
CO 4 Identify classes of problems that can be solved using Quantum
Computing Apply ing

Module Detailed Contents Hrs
1 Module: Overview of Traditional Computing
Computers and the Strong Church -Turing thesis, Circuit Model of
Computation, Linear Algebra Formulation of the Circuit Model,
Reversible Computation, Dirac Notation, Operators, Functions of
Operators
Self-Learning Topics: Basic Linear Algebra 6

Page 155

2 Module: Qubits a nd General Quantum Operations
State of a Quantum System, Composite Systems, Measurement,
Mixed States and General Quantum Operations: Mixed States,
Partial Trace, General Quantum Operations
Self-Learning Topics : Binary Operations 8
3 Module: Quantum Model of Computation
The Quantum Circuit Model, Quantum Gates: 1 Qubit Gates,
Universal Sets of Quantum Gates, Discrete Set of Universal
Operations.
Self-Learning Topics : Basic Gates 8
4 Module: Programming for a QPU
One Qubit: Physical Qubit, Introducing the Circle Notation, QPU
Instructions; Multiple Qubits: Circle Notation for Multi -Qubit
Registers, Single Qubit Operations in Multi -Qubit Registers, QPU
Instructions; Quantum Teleportation
Self-Learning Topics : Additional QPU Instructions for Multiple
Qubits 8
5 Module: Quantum Arithmetic & Logic
Arithmetic on a QPU, Building Increment and Decrement
Operators, Adding Two Quantum Integers, Negative Integers,
Quantum Conditional Execution, Mapping Boolean Logic to QPU
Operations, Basic Quantum Logic.
Self-Lear ning Topics : Overview of Quantum Phase Estimation 6
6 Module: QPU Applications
Real Data: Non -integer Data, QRAM, Matrix Encodings: How can
a QPU Operations represent a Matrix; Quantum Supersampling
(QSS): What can a QPU do for Computer Graphics, Conven tional
Supersampling, Computing Phase -Encoded Images.
Self-Learning Topics : Shor‟s Factoring Algorithm 4
Reference Books :

Reference
No Reference Name
1 Kaye P, Laflamme R, Mosca M. An introduction to quantum computing. Oxford
university press; 2007. I SBN No. 0198570007
2 Johnson, Eric R., NicHarrigan, and Mercedes Gimeno -Segovia. Programming
Quantum Computers: Essential Algorithms and Code Samples. O‟Reilly; 2019.

Page 156

ISBN No. 1492039683
3 Nielsen MA, Chuang I. Quantum computation and quantum information .
Cambridge University Press; 2012. ISBN No. 9780511976667
4 Silva V. Practical Quantum Computing for Developers. Apress; 2018. ISBN No.
9781484242179
5 Rieffel EG, Polak WH. Quantum computing: A gentle introduction. MIT Press;
2011. ISBN No. 97802625266 78
6 Aaronson S. Quantum computing since Democritus. Cambridge University
Press; 2013. ISBN No. 9780521199568

Web References:
Reference
No Reference Name
1 https://cra.org/ccc/wp -content/uploads/sites/2/2018/11/Next -Steps -in-Quantum -
Computing.pdf
2 https://www.ibm.com/quantum -computing/
3 https://eandt.theiet.org/content/articles/2019/04/quantum -for-dummies -the-
basics -explained/
4 https://www.cl. cam.ac.uk/teaching/1718/QuantComp/
5 https://nptel.ac.in/courses/104104082/
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with th e permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 s ub-questions of 5
marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 157

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE341 Intellectual
Property
Rights Contact Hours
Theory Tutorial Theory Tutorial Total
3 1 3 1 4
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 25 80 125
Pre-requisi te: Nil
Course Objectives: The course aim to
Sr. No. Course Objective
01 Describe the concept of intellectual property, explain the classification of intellectual
property rights, and elaborate on the justification and scope of IPRs.
02 Create awareness of the rights and infringements of rights and related protections of
inventions, creations, and ideas using various IPRs under IP Laws of India as well as
International Treaty procedures.
03 Describe registration process of various intellectual property i n India as well as abroad.
04 Understand the circumstances under which a granted patent/copyright/design etc., could
be revoked, opposed, taken away, or licensed.
05 Familiarize with the databases and tools for searching and filing of patents and other
IPRs.
06 Discuss the National IPR Policy of India.

Course Outcomes: On successful completion of the course learner/student should be able to
Sr. No. Course Outcome Bloom Level
CO1 Define the key concepts of Intellectual Property and IP Infringements. Remembering
CO2 Understand and acquire knowledge of IPR policy followed in India. Understanding
CO3 Demonstrate the know -how required to identify, assess, and apply for
IP rights protection under various applicable laws and treaties in force. Applying
CO4 Analyze the development, registration procedure, protection,
compliance, and enforcement of various intellectual property rights. Analyzing

Page 158

Module Detailed Contents Hrs.
1 Module: Introduction and Historical Backgrou nd
Introduction:
The Concept of Property: Its Definition, Its Features, and Classification –
Intellectual Property as Creations of the Human Mind – Justifying Intellectual
Property : Arguments for and Against It – Types of IPRs: Patent, Copyright,
Trademar k, Trade Secret, Industrial Design, Geographical Indication,
Semiconductor Integrated Circuit Layout Design, Plant Variety & Farmer‟s Rights;
Genetic Resources and Traditional Knowledge
Historical Background: Evolution of IPRs through Various International
Agreements, Treaties, & Conventions: From Paris Convention (1883) To WTO -
TRIPS Agreement (1995) – Global IPR Organizations: WIPO (1967) and WTO
(1995)
Self-Learning Topics: Relevance of Intellectual Property in Today‟s Knowledge
Economy 05
2 Module: Pate nts
Introduction to Patent: What is a Patent? – Conditions for Grant of Patent –
Patentable Inventions and Inventions Not Patentable – Process and Product Patents –
Patent Specifications – The Process for Obtaining a Patent in India and Abroad –
PCT Patent – Post-Grant Opposition, Revocation and Compulsory Licensing –
Rights Granted to a Patentee – Patent Infringement & Its Remedies – Patent Search
and Databases – e-filing of Patent Application
Emergence of Technology Patents:
Patenting the Inventions of I nformation Technology: Patenting Computer
Programs and Software – Software Patents vs Software Copyrights: Lessons for
India – Patenting of Biotechnology Inventions (or Patenting Life)
Self-Learning Topics: Biotech Patents in India 09
3 Module: Copyrig hts
Introduction to Copyright: Nature of Copyright – Copyright as a Property,
Statutory Right, Idea versus Expression – Requirements for Copyrights – Idea-
Expression Dichotomy: Merging of the Idea with Expression, Originality & Fixation
– Various Works Pro tectable Under Copyrights – Authorship and Ownership –
Registration of Copyrights – Term of the Copyright – Copyright Infringement, Its
Remedies & Penalties.
Copyrights in the Digital Age – Internet and Copyright – Copyrights in Computer
Software – Copyrig hts for Electronic Database – Digital Copyright Protection in
India
Self-Learning Topics: Fair Use – Instances of Fair Use: Using Copyrighted Works
in Education and Library 09
4 Module: Trademarks and Trade Secrets
Trademarks: Introduction – The Rationa le and Functions of a Trademark –
Different Types of Trademarks – Categories of Trademark Distinctiveness –
Recognizing a Good Trademark – What Cannot be Registered as a Trademark? –
Registration & Renewal of a Trademark – Rights Granted by Trademark 08

Page 159

Regis tration – Different Classes of Trademark Infringement – Acts of Trademark
Infringement & Remedies
Trade Secrets: Trade Secret and its Characteristics - Kinds and Examples of Trade
Secrets - Protection of Trade Secrets - Patents and Trade Secrets
Self-Learn ing Topics: Origin of Trademarks System in India – Misappropriation of
Trade Secrets
5
Module: Designs and Geographical Indications
Design: Defining a Design – Essentials of a Design – Registration & Term of
Designs – Copyright in Registered Designs – Conditions for Registration of
Industrial Designs – Procedure for Registration of Industrial Designs – Infringement
of Industrial Designs and Remedies Against Infringement – The Hague Agreement
Geographical Indications: Introduction - Concept of Geograph ical Indications -
Kinds of Geographical Indications – Registration of GIs – Benefits of Registering
GIs – Infringement of a Registered GI and Remedies Thereof
Self-Learning Topics: IPRs forSemiconductor Integrated Circuit Layout Design 07
6 Module: Har nessing Intellectual Property for National Development
India‟s New National IPR Policy, 2016: Vision Statement, Mission Statement and
Objectives – IPR Administration System in India – Govt of India Initiatives &
Schemes towards Promoting IPR
Self-Learning Topics: Managing of Intellectual Property in Organizations 02
Reference Books :
Ref
No Reference Name
1 Nithyananda, K. V., Intellectual Property Rights: Protection and Management, Cengage
Learning (2017), First Edition, ISBN: 9789386668578
2 NeerajPand ey&KhusdeepDharni, Intellectual Property Rights, PHI Learning (2014),
First Edition, ISBN: 812034989X, 9788120349896
3 Sreenivasulu N.S, Law Relating to Intellectual Property, Partridge Publishing (2013),
First Edition, ISBN: 1482813939 , 9781482813937
4 Ramakrishna B & Anil Kumar H.S, Fundamentals of Intellectual Property Rights – For
Students, Industrialists, and Patent Lawyers, Notion Press (2017), First Edition,
ISBN: 1946556327, 9781946556325
5 Siva Vaidhyanathan, Intellectual Property: A Very Short I ntroduction, Oxford
University Press (2017), Second Edition, ISBN: 9780195372779
6 Ahuja V. K., Law Relating to Intellectual Property Rights, Lexis Nexis (2017), Third
Edition, ISBN: 9788131251652

Web References:
Reference
No Reference Name
1 National IPR Policy 2016 - http://cipam.gov.in/wp -
content/uploads/2017/10/National -IPR-Policy -English -.pdf
2 Intellectual Property – The Future, CIPAM, 2017 - http://cipam.gov.in/wp -

Page 160

content/uploads/2017/09/bookletIPR.pdf
3 WIPO Intellectual Property Handbook –
(https://www.wipo.int/edocs/pubdocs/en/intproperty/489/wipo_pub_489.pdf )
5 https://www.startupindia.gov.in/
6 https://dipp.gov.in/
7 http://ipindia.nic.in/
Intellectual Property Rights: Tutorials
Sr. No Detailed Contents Hrs.
01 Study of a real -world case on Patents 02
02 Study of a real -world case on Copyrights 02
03 Study of a real -world case on Trademarks 02
04 Study of a real -world case on Trade Secrets 02
05 Study of a real -world case on Industrial Design 02
06 Study of a real -world case on Geographical Indication 02
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous as sessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as p er the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
Term Work : 25 marks
The term work will be based on the tutorial performance of the student .
End Semester Theory Examination:
1. Question paper w ill comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -questions of
5 marks each will be asked.
4. Remaining questions will be randomly se lected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 161

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE342 Green
Computing Contact Hours
Theory Tuto rial Theory Tutorial Total
3 1 3 1 4
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 25 80 125
Pre-requisite:
 Knowledge of computer peripherals
 Knowledge of data storage devices
 Some awareness towards Envir onment as a whole

Course Objectives: The course aim to
Sr.
No. Course Objective
01 Explain why Green IT is important to the enterprise over all
02 Create awareness among stakeholders and promote green initiatives in their
environments leading to a gree n movement.
03 Adopt special skills such as knowledge about energy efficiency, ethical IT
assets disposal, carbon footprint estimation.
04 Create eco -friendly environment.
05 Conduct basic equipment usage audits
06 Improve energy efficiency of their personal computing environment as well as the
enterprise -wide computing environment

Course Outcomes: On successful completion of course learner/student will be able to
Sr.
No. Course Outcome Bloom Level
CO1 Acquire expertise for improving the energy ef ficiency for laptops and
personal computers by reducing the power consumption requirements Remembering
CO2 Assess enterprise -wide and personal computing and computing energy
consumption Understanding
CO3 Recognize the necessity for long -term sustainabili ty in IT Understanding
CO4 Formulate plans for reducing IT heating and cooling requirements Creating
CO5 Evaluate the regulatory and governance issues surrounding IT Evaluating
CO6 Choose the best sustainable hardware for their applications Analyzin g

Page 162

Module Detailed Contents Hrs.
1 Trends and Reasons to Go Green:
 Overview andIssues
 ConsumptionIssues
o Minimizing PowerUsage
o Cooling
Self-Learning Topics: Current Initiatives andStandards 05
2 Introduction to Green IT:
 GreenIT
 Holistic Approach to Gr eeningIT
 Greening byIT (can be used for case study also)
o Using RFID for EnvironmentalSustainability
o SmartGrids
o Smart Buildings and Homes
o Green Supply Chain andLogistics
o Enterprise -Wide EnvironmentalSustainability
Self-Learning Topics: Awareness toImplementa tion
06
3 Green Hardware
 Introduction,
 Life Cycle of a Device or Hardware,
 Reuse, Recycle and Dispose
Green Software
 Introduction
 Energy -Saving SoftwareTechniques
Sustainable Software Development
Self-Learning Topics: Changing the way we work 07
4 Green Data Centers
 Data Centre ITInfrastructure
 Data Centre Facility Infrastructure: Implications for Energy
Efficiency
 IT InfrastructureManagement
 Green Data Centre Metrics
Green Data Storage
 Introduction
 Storage Media PowerCharacteristics
 Energy Management Tec hniques for HardDisks
 System -Level EnergyManagement
Green Networks and Communications
 Introduction
 Objectives of Green NetworkProtocols
 Green Network Protocols andStandards
Self-Learning Topics: Refer some latest IEEE papers on the
relevant topics 08
5 Enterprise GreenIT Strategy:
 Introduction
 Approaching Green IT Strategies
 Business Drivers of Green IT Strategy
 Organizational Considerations in a Green ITStrategy
 Steps in Developing a Green ITStrategy
 Metrics and Measurements in GreenStrategies 06

Page 163

Enterprise Green IT Readiness
 Background: Readiness and Capability
 Development of the G -Readiness Framework
 Measuring an Organization‟s G -Readiness
Self-Learning Topics: Sustainable IT Roadmap
6 Managing Green IT
 Introduction
 Strategizing GreenInitiatives
 Imple mentation of GreenIT
 InformationAssurance
 Communication and SocialMedia

Green Cloud Computing and Environmental Sustainability
 Cloud Computing and Energy Usage Model:
 Features of Clouds Enabling Green Computing
 Towards Energy Efficiency of Cloud Computing
 Green Cloud Architecture
The Future of Green IT
 Green Computing and theFuture
 Megatrends for GreenComputing
 Tele-presence Instead ofTravel
 Tele-commuting Instead ofCommuting
 Deep GreenApproach
Self-Learning Topics: Green IT Regulations and Standards 08
Reference Books :

Reference
No. Reference Name
1 Green IT: Reduce Your Information System's Environmental Impact While Adding to
the Bottom Line,Toby Velte, Anthony Velte, Robert Elsenpeter, 2008, McGraw Hill.
2 Harnessing Green IT,San Murugesan, G. R. Gan gadharan, 2013, WILEY.
3 Green Computing -Tools and Techniques for saving energy, money and resources, Bud
E. Smith, 2014, CRCPress.
4 GREEN IT FOR SUSTAINABLE BUSINESS PRACTICE, Mark G. O‟Neill, An ISEB
FoundationGuide.
5 Green Computing and Green IT Be stPractices, Jason Harris
6 The Green of IT – How Companies Can Make a Difference for the Environment, John
Lamb, IBM Press (2009).
7 Green Project Management, Richard Maltzman and David Shirley, CRC Press a Taylor
and Francis Company (2010)
8 Foundatio ns of Green IT, Marty Poniatowski, Prentice Hall, 2009
Web References: http://www.carbonfootprint.com
https://www.ene rgystar.gov/

Page 164

Tutorials

Sr. No. Detailed Contents Hrs.
1 Calculating the Energy Consumption or Carbon Footprint for a given location
(eg: your College, Residence, or a specific building) and suggesting means of
reducing Energy consumption or Carbon Footp rint respectively 02
2 Use of Greening by IT Tools in a live location and submitting a report which
indicates Before and After effects 02
3 Calculating the amount of E -waste generated from a given location (eg: your
College, Residence, or a specific buil ding) and monitoring the process of proper
handling of E -waste. 02
4 Preparing a report on how Green Data Center can be feasibly applied to your
Institute. Verifying the report from Industry Expert. Calculating the cost of
implementing Green Data Center 02
5 Developing an Green IT Strategy for a given location (eg: your College,
Residence, or a specific building) and submitting a report for the same 02
6 Studying which of the latest Green IT techniques (eg: - Remote Maintenance
using Tools, E -Learning & E -Training, Web Conferencing & E -Webinar
Meetings, E -Signatures, Virtual Filing & Cloud Computing) can be applied to
your Institute and submitting report for the same. 02
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the co ntinuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of th e mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test shall be one hour.
Internal Assessmen t: 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
Term Work : 25 marks
The term work will be based on the tutorial performance of the student .
End Semester Theory Examination:
1. Quest ion paper will comprise of total 06 questions, each carrying 20marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -
questions of 5 marks each will be asked.
4. Remaining questions will be r andomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture
hours as mentioned in the syllabus.

Page 165

Course
Code Course Name Teaching Scheme Credits Assigned
MCAE343 Management
Information
System Contact Hours
Theory Tutorial Theory Tutorial Total
3 1 3 1 4
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 25 80 125
Pre-requisite: knowledge of computer peripheral, knowledge of information and securit y
Course Objectives: The course aim to
Sr.No. Course Objective
1  Understand the nature of management information systems and their applications
in business.
2 Identify the major management challenges in building and using information
systems.
3 Learn an d explore IT security and Infrastructure. of management information
systems.
4 Understand the ERP and its component.

Course Outcomes: On successful completion of course learner/ student will be able to
Sr.No. Outcome Bloom Level
CO 1 Understand theoret ical aspects of Management Information
Systems. Understand ing
CO 2 Know the procedures and practices for handling information
system effectively. Understand ing
CO 3 Acquire knowledge in various Decision Support Systems. Remember ing
CO 4 Recognize the ne cessity of IT security and Infrastructure in
Management Information Systems. Applying

Module Detailed Contents Hrs
1 Management Information Systems (MIS):
Perspectives on Information Systems, Nature and scope of MIS,
Characteristics of MIS, Need and Rol e of MIS, Impact of MIS, functions
and future of MIS, MIS: A support to the management, MIS:
organization effectiveness, MIS for a digital firm
Self Learning Topics: Case Study on digital firm 6

Page 166

2 Information System and MIS:
Organisations and Information Systems: Modern Organisation,
Information Systems in Organisations, Managing Information Systems in
Organisations
Concepts of Management Information Systems: Data and
Information, Information as a Resource, Information in Organisational
Functions, Types of Information Technology, Types of Information
Systems, Decision Making with MIS, Communication in Organizations.
Self Learning Topics: Case Study: Management Issues - Challenges for
Managers 7
3 Decision Support System, Knowledge Management and
Management o f Global Enterprise:
Decision Support System(DSS), DSS Models, Group Decision Support
System(GDSS), Knowledge based Expert System(KBES), Enterprise
Resource Planning(ERP) System, ERP Model and Modules, Benefits of
ERP, Supply Chain Management(SCM), Informa tion Management in
SCM, Customer Relationship Management(CRM)
Self Learning Topics: Study of EMS and MIS 8
4 Business Intelligence for MIS:
Business Intelligence and MIS, what is Business Intelligence (BI), Tools
and Techniques of BI, why is BI Developed? How is BI used? Process of
generation of BI, MIS and BI.
Self Learning Topics: Case illustration of BI 6
5 Managing Information Systems and Information Technology
Infrastructure:
Managing Information System: Challenges of Managing the IT
Function, Vendor Management, IT Governance,
Information Technology Infrastructure and Choices: What is the IT
Infrastructure?, IT Infrastructure Decisions, Infrastructure Components,
Networks
Self Learning Topics: Case Study of Managing Information System 6
6 Information Security:
Introduction, Threats and Vulnerability, Controlling Security Threat and
Vulnerability, Managing Security Threat in E -Business, Measures of
Information Security, Information Security Management.
Self Learning Topics: Network Security, and Cyber Security for
Information 7

Reference Books :
Reference
No Reference Name
1 Management Information Systems - A global digital Enterprise perspective, 5th
edition - By W.S.Jawdekar, TMG Publications
2 MIS: Managing Information Systems in Business, Governm ent and Society, 2ed
by Rahul De, Wiley
3 Management Information System, James O„Brien, 7th edition, TMH
4 Management Information Systems, Loudon and Loudon, 11th edition, Pearson.

Page 167

Web References:
Reference
No Reference Name
1 https://en.wikipedia.org/wiki/Management_information_system
MIS: Tutorial
Sr. No. Detailed Contents Hrs
1


2

3 Subject Instructor/Mentor can form the groups of the students and they can
discuss the case studies with the guidance of Instructor/Mentor to
understand and explore the applications of MIS in various Industries.
Case studies can be chosen in the area like application of MIS in functional
area and service sectors i.e. Banking, Insurance, Health C are, Aviation,
Food Industry and Education etc.
Case studies based on various opensource technologies can also be included
in discussion to understand the software supports in decision making of
MIS. 12

Assessment :
Continuous Assessment: 20 marks
Followi ng measures can be used for the continuous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The fac ulty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of the test sha ll be one hour.
Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
Term Work : 25 marks
The term work will be based on the tutorial performance of the student .
End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -questions of 5
marks each will be asked.
4. Remaining questions will be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 168

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE34
4 Cyber
Security and
Digital
Forensics Contact Hours
Theory Tutorial Theory Tutorial Total
3 1 3 1 4
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 25 80 125
Pre-requisite: NIL
Course Objectives : Course aim to
Sr.No. Course Objective
1 ● Understand basics of cyber security
2 Acquire the knowledge of various tools and methods used in cyber crime
3 Learn the fundamentals of digital forensic
4 Apply appropriate skills and knowledge for solving digit al forensic problems
Course Outcomes: On successful completion of course learner/student will be able to
Sr.No. Outcome Bloom Level
CO1 Demonstrate understanding of basic concepts in cyber security Understanding
CO 2 Make use of various tools and method s used in cybercrime Applying
CO 3 aaaaa Adapt fundamental knowledge of digital forensics annan Creating
CO 4 Determine skills and knowledge for solving digital forensics Problems Evaluating

Module Detailed Contents Hrs
1 Module: Introduction to Cyber Security
Cybercrime and origins of the world, Cybercrime and information security,
Classifications of cybercrime, Cybercrime and the Indian ITA - 2000, A global
Perspective on cybercrimes.
Self learningTopic: Amendments to the I ndian IT Act(2008). 4
2 Module: Cyber offenses & Cybercrimes
How criminal plan the attacks, Industrial Spying/Industrial Espionage, Hacking,
Online Frauds, Pornographic Offenses, E -Mail Spoofing, Spamming,data
diddling , salami attack, Cyber defamation, I nternet Time Theft,SocialEngg,
Cyber stalking, Cyber café and Cybercrimes, Botnets, Attack vector, Cloud
computing, Proliferation of Mobile and Wireless Devices, Trends in Mobility,
Credit Card Frauds in Mobile and Wireless Computing Era.
Self learning Top ic: Security Challenges Posed by Mobile Devices. 7
3 Module:Tools and Methods Used in Cybercrime
Phishing, Password Cracking, Keyloggers and Spywares, Virus ,worms and
trojans, Steganography, DoS and DDoS Attacks, SQL Injection, Buffer 6

Page 169

OverFlow, Attacks o n Wireless Networks,Identity Theft (ID Theft)
Self learning Topic: Various types of viruses,Worms and Trojans
4 Module: Introduction to Digital Forensics
Introduction to Digital Forensics and its uses. Need of digital Forensics, Digital
forensic life cycle, Relevance of the OSI 7 Layer Model to Computer Forensics,
Forensics and Social Networking Sites: The Security/Privacy Threats,
Challenges in Computer Forensics, Special Tools and Techniques, Forensics
Auditing and Antiforensics.
Self learning Topi c: Various digital forensic models/ framework 5
5 Module: Data Recovery and Evidence Collection
Data Recovery: Defined, data backup and recovery, role of backup in data
recovery, Data recovery solutions, Hiding and recovering Hidden data
Evidence Collecti on and Data Seizure: What is digital evidence, rules of
evidence, Characteristics of evidence, Types of evidence, Volatile evidence,
General procedure for collecting evidence, Methods of collection and collection
steps, Collecting and archiving, Evidence h andling procedures, Challenges in
evidence handling Duplication and Preservation of Digital Evidence
Self learningTopic: Symmetric and Asymmetric Encryption 8
6 Module: Network Forensic and Steganography
Network Forensics : Network Fundamentals, Network T ypes, Network security
tools and attacks, Intrusion Detection Systems (types and advantages and
disadvantages)
Email Investigations – E-Mail protocol, E -Mail as Evidence, Working of E -
Mail, Steps in the E - Mail communication, IP Tracking, E -Mail Recovery,
Android Forensic -Android forensic - The evolution of Android, The Android
model, Android security, The Android file hierarchy, The Android file system,
Android Data Extraction Techniques: Manual data extraction,Logical data
extraction, Physical data extrac tion
Cyber Forensics Tools: Tool Selection, hardware, Software, Tools (FKT, PKT)
Steganography – categories of steganography in Forensics (Text, Image, Audio)
Self learning Topic: Various forms of Internet Frauds 10

Reference Books :
Reference
No Reference Name
1 Nina Godbole, SunitBelapurCyber Security Understanding Cyber Crimes,
Computer Forensics and Legal Perspectives –, Wiley India Publications
Released: April 2011
2
John Sammons, “The Basics of Digital Foren sics”, Elsevier 2012
3
Computer Forensics, Computer Crime Scene Investigation. By John R. Vacca,
Charles River Media, INC. 2nd Edition

Page 170

4
Jain, Dr. dhananjay R. Kalbande,Digital Forensic The Fascinating world of
Digital forensic
5
Anthony Reyes,The Bes t Damn Cybercrime and Digital Forensic Book Period,,
Jack Wiles
6
Practical Mobile Forensics: SatishBommisetty,RohitTamma and Heather
Mahalik, Pack Publishing LTD 2014, ISBN -978-1-78328 -831-1
7 Investigating Network Intrusions and Cybercrime EC -Council | Press
8 Computer Forensic investing Network Intrusions and cyber crime by Course
Technology
9 Michael Gregg & David Kim,Inside Network Security Assessment: Guarding
Your IT Infrastructure, Pearson Publication
10 Suresh T. Vishwanathan -The Indian Cyb er Law ; Bharat Law House New Delhi

Web References:
Referen
ce No Reference Name
1 Computer Forensic Training Center Online http://www.cftco.com/ Computer
Forensics World
2 http://www.computerforensicsworld.com/ Computer Forensic Services
3 http://www.computer -forensic.com/
4 Digital Forensic Magazine http://www.digitalforensicsmagazine.com/
5 The Journal of Digital Forensics, Security and Law http://www.jdfsl.org/
6 Journal of Digital Forensic Practice http://www.tandf.co.uk/1556728 1
7 https://www.researchgate.net/publication/220846511
8 https://www.researchgate.net/publication/306301164
9 https://www.researchgate.net/publication/308646775_An_introduction_to_steganogr
aphy_methods

TUTORIAL :
Sr.No Detailed content Hrs .
1 Given a list of cases, identify whether the it falls under the category of virus,
worms or trojans. 1
2 Two real life case study related to data diddling, salami attack and social
engineering. Also , explaining w hat precautions needs to be taken from these
attacks. 1

Page 171

3 Any real life cases that were booked under the following sections:
1.Section 65
2. Sections 66A,66B,66C,66D,66E,66F 1
4 Various types of viruses, worms and trojans and explain how they work. 1
5 SQL injection technique. Make a presentation slide and demonstrate. 1
6 Take any 2 cyber crimes, explain in detail as a presentation. Also download its
related video to demonstrate it in the class. 1
7 Understanding relevance of the OSI 7 Layer Model to Computer Forensics 1
8 Screen lock bypassing techniques and different of password cracking methods 1
9
Cyber Forensics Tools: Tool Selection, hardware, Software, Tools (FKT, PKT) 1
10 Investigate and browse recovered e -mails in „R -Mail‟ tool. 1
11 Investigation of information of captured packets by using „Wireshark‟ tool. 1
12
Recovering deleted data from an Android device by using the „FKT‟ tool.
1

Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the contin uous assessment as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mo de as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks each. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of each test shall be one hour.
Internal Assessm ent: 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
Term Work : 25 marks
The term work will be based on the tutorial performance of the student .
End Semester Theory Examination:
1. Question paper will comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on the entire syllabus wherein 4 sub -
questions of 5 marks each will be asked.
4. Remaining questions wi ll be randomly selected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 172

Course
Code Course
Name Teaching Scheme Credits Assigned
MCAE345 Entreprene
urship
Manageme
nt Contact Hours
Theory Tutorial Theory Tutorial Total
3 1 3 1 4
Examination Scheme
Theory Term Work End Sem Exam Total CA Test AVG
20 20 20 25 80 125
Pre-requisite: Nil
Course Objectives: The course aim to
Sr.
No. Course Objective
1 Instill a spirit of entrepreneurship among the student participants.
2 Provide an overview of the competences needed to become an entrepreneur.
3 Understand growth and managing strategies of venture and Social Responsibilities
4 Understand how to des ign effective and efficient Business Plan for intended users.
5 Understand role of Small -Scale Industries (SSI) & Institutions Supporting Small
Scale Enterprise
6 Recognize the importance of entrepreneurship and identify the profile of
entrepreneurs and their role in economic growth.

Course Outcomes: On successful completion of course learner/student will be able to
Sr. No. Outcome Bloom Level
CO1 Understand the concepts and fundamentals of Entrepreneurship. Understanding
CO 2 Understand the growth an d development strategies for venture and
Social Responsibilities Understanding
CO 3 Identify the Role of Small -Scale Industries (SSI) & Institutions
Supporting Small Scale Enterprise. Applying
CO 4 Analyse the process of Business Idea generation and conv erting the
idea into a Business Model. Analyzing
CO 5 Evaluate the effectiveness of different entrepreneurial strategies,
policies and measures for promoting small industries. Evaluating
CO 6 Create presentations and marketing strategies that articulate
financial, operational, organizational, market, and sales knowledge
for value creation. Creating

Page 173

Module
No. Detailed Contents Hour
s
1 Module: Overview of Entrepreneurship: The Entrepreneurial
Perspective
Concept and Definitions: Manager, Entrepreneur , Entrepreneurship and
Intrapreneurship, Importance and Significance of Growth of Entrepreneurial
Activity, Traits, Characteristics, Skills and Qualities of Entrepreneurs,
Classification and Types of Entrepreneurs, Emerging trends and issues in
Entrepreneu rship.
Self-learning topics: Differences Between Entrepreneurs,
Intrapreneurs&Ultrapreneurs 5
2 Module: Creativity and New Venture Management
Creative Business Ideas: Identify and Recognizing Opportunities:
Observing Trends and solving problems, Creativit y: Concept, Components
and types, Sources of New Venture Ideas: Concept, Pre -selection Process,
Sources of Business Idea, Preliminary Research, Business Idea Evaluation,
Other Analysis.
Writing a Business Plan: Introduction of Business Plan, Guidelines for
writing A Business Plan, Layout of Business Plan (Executive summary,
Business Description, Industry Analysis, Market Analysis, Management
Team and Company Structure, Operations Plan, Product Design and
Development Plan, Financial Projections and Critical Risk Assessment,
Harvest Strategy, Milestone Scheduling ), Presenting the Business Plan to
Investors. Why some Business Plans Fail.
Self-learning topics: Writing business plan for benefiting to an entrepreneur 8
3 Module: Small Scale Industries Management
Introduction to Small Industry: Introduction, Concept of small industry,
Position in India, Role of small industries in economic development.
Definition of Small -scale Industries, Undertakings, SSI Policy Statement,
Procedure for SSI Registration, The Stre ngths and Weakness of Small
Business. Reasons for the significance of small sector, various forms of
small -scale enterprises, Small Industries during various five -year Plans,
Policies and measures for promoting small industries.
Self-learning topics: Growt h and Performance of Small -Scale Industries
(SSI) in India, Problems for SSI. 7
4 Module: Entrepreneurship Development and Government
Role of Central Government and State Government in promoting
Entrepreneurship - Introduction to various incentives, subs idies and grants
- Export Oriented Units - Fiscal and Tax concessions available
Role of following agencies in the Entrepreneurship Development -
District Industries Centers (DIC), Small Industries Service Institute (SISI),
Entrepreneurship Development Inst itute of India (EDII), National Institute
of Entrepreneurship & Small Business Development (NIESBUD), National
Entrepreneurship Development Board (NEDB)
Self-learning topics: List out all the Central & State Government policies
implemented for Entrepreneur ship Development. 7

Page 174

5 Module: Marketing the Product or Service
Small Business Marketing: Strategy and Research: Concept, Marketing
Strategies, Market Research. Product: Heart of Marketing Mix, Purchasing,
Selecting Suppliers, Managing and controlling Inve ntor. Place: Location
Types, Layout & Design. Price and Promotion: Economics of Pricing,
Breakeven Analysis, Pricing -Setting Techniques, Credit Policies,
Promotions.
Self-learning topics: Role of Digital Marketing for an entrepreneur as
promoting their pro duct 6
6 Module: Growth and Development of the Venture & Social
Responsibility
Small Business Growth: Growing Firm, Transition to Professional
Management, The Next Step: An Exit Strategy, Leadership in Action:
Leadership Attributes, Negotiations, Delega tion, Motivation Employees,
HRM: Job Analysis, Recruitments, Selections, Trainings, Compensations,
Introduction of Social Responsibility, Corporate Social Responsibility
(CSR), Dimensions of CSR.
Self-learning topics: Operation management responsibilities in managing
Small Business. 7
Reference Books :

Reference No Reference Name
1 Barringer, Ireland, “Entrepreneurship: Successfully Learning New Ventures”, Pearson,
Latest Edition
2 Robert D Hisrich, Michael P Peters, Dean A Shepherd, Entrepreneurship, S ixth
Edition, The McGraw Hill Company.
3 Pocket Mentor “Creating A Business Plan”, Harvard Business School Press, Boston,
Massachusetts
4 David Butler “Enterprise Planning Development - Small Business Start -up Survival and
Growth”, Butterworth -Heinemann
5 Entrepreneurship and Small Business Management by Dr. C L Bansal, HarAnand
Publications Pvt. Ltd. New Delhi, 2012
6 Entrepreneurship by Lall, Madhurima. Sahai, Shikha. Excel Books, New Delhi, 2008,
2nd Edition
7 Strategic Entrepreneurship “A Decision -making approach to new venture creation and
management” Philip A. Wickham, Pearson Education Society
8 “Small Business Management” Entrepreneurship and Beyond, 5th Edition, Timoth S.
Hatten
9 Vasant Desai, The Dynamics of Entrepreneurial Development and M anagement, 2015,
Himalaya Publishing House.
10 PoornimaCharantimath, Entrepreneurship Development - Small Business Enterprise,
Pearson.
11 Dr TN Chhabra, Entrepreneurship Development, Sun India Publications, New Delhi
12 Dr CN Prasad, Small and Medium En terprises in Global Perspective, New century
Publications, New Delhi

Page 175

Web References:
Reference
No Reference Name
1 http://niesbud.nic.in/
2 http://msme.gov.in/
3 http://ssi.nic.in/
4 www.womenentrepreneursindia.com
5 www.msmetraining.gov.in
6 https://www.startupindia.gov.in
7 https://www.makeinindia.com
8 https://mygov.in
9 www.dcmesme.gov.in
10 www.nsic.co.in
EM: Tutorials
Sr. No. Detailed Contents Hrs.
1 Entrepreneurial Tasks. 01
2 Entrepreneurship Development in rural areas (Agriculture/Allied Business) 01
3 Women Entrepreneurship Development. (Case Study) 01
4 Team Building Activities (Board of Members/ Employees) 01
5 Entrepreneurship in Service Sector. 01
6 Preparing Business Plan 01
7 Scenarios for fundraising in Entrepreneurship 01
8 E-Business Brainstorming Activities 01
9 Impact of Globalizat ion and Liberalization on SSI. 01
10 Risk Management in Entrepreneurship. 01
11 Social Development through Entrepreneurship. 01
12 CSR Case Study. 01
Assessment :
Continuous Assessment: 20 marks
Following measures can be used for the continuous assessme nt as
Assignments /Quiz /Case studies /Projects / Any other measure with the permission of the
Director/Principal/HOD/Coordinator
The continuous evaluation has to be done throughout the Semester. The faculty can use the
flexibility of the mode as per the requirement of the subject.
Test: 20 marks
Assessment consists of one class tests of 20 marks each. The Class Test is to be conducted when
approx. 50 -60% of the syllabus is completed . Duration of each test shall be one hour.

Page 176

Internal Assessment : 20 marks
The Internal Assessment marks (out of 20) will be the average of the Class test and the
Continuous Assessment.
Term Work : 25 marks
The term work will be based on the tutorial performance of the student .
End Semester Theory Examination:
1. Question paper w ill comprise of total 06 questions, each carrying 20 marks.
2. Total 04 questions need to be solved.
3. Question No: 01 will be compulsory and based on entire syllabus wherein 4 sub -questions of 5
marks each will be asked.
4. Remaining questions will be randomly s elected from all the modules.
5. Weightage of each module will be proportional to number of respective lecture hours as
mentioned in the syllabus.

Page 177

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term Work Practical Oral Total
MCAL31 Big Data Analytics
and Visualization 02 01 25 30 20 75
Prerequisite: Basic Understanding of SQL, Java Programming and Python
Lab Course Objectives
Sr.No. Course Objective
1 ● Understand Various Components of Had oop for instance Hadoop2.x, HDFS, Map
Reduce
2 Understand and gain knowledge of NoSQL DB and Data Modelling Concept
3 Teach Hadoop Ecosystem Projects Hive and Pig and its Programming Modules.
4 Learn Functional programming in spark and execute and creat e spark applications.
5 Teach Data Visualization and its importance using Tableau
Lab Course Outcomes:
Sr.No. Outcome Bloom Level
CO1 Demonstrate HDFS Commands in Hadoop Understanding
CO 2 Apply Map Reduce Programming Paradigm to solve the algorithm ic
problems Applying
CO 3 Build No SQL Database and Query it Using Mongo DB Applying
CO 4 Analyze the Data Using Hadoop Ecosystem Projects: Hive and Pig Analyze
CO 5 Explain RDD and Data Frame Creation in Apache Spark Evaluate
CO 6 Create various Visu alizations using Tableau. Creating
Description:
Module
No Detailed Contents Hrs.
1 Set up and Configuration Hadoop Using Cloudera
Creating a HDFS System with minimum 1 Name Node and 1 Data Nodes
HDFS Commands
Self-Learning Topics : Set up Hadoop in Lin ux Environment 2
2 Map Reduce Programming Examples
Word Count.
Union, Intersection and Difference.
Matrix Multiplication.
Self-Learning Topics : Natural Join Programming Example 4

Page 178

3 Mongo DB: Installation and Creation of database and Collection
CRUD Doc ument: Insert, Query, Update and Delete Document.
Self-Learning Topics: HBASE Commands 4
4 Hive: Introduction Creation of Database and Table, Hive Partition, Hive Built
in Function and Operators, Hive View and Index.
Self-Learning Topics : Configure Hive M etastore to MySQL 4
5 Pig: Pig Latin Basic
Pig Shell, Pig Data Types, Creating a Pig Data Model, Reading and Storing
Data, Pig Operations
Self-Learning Topics: 4
6 Spark: RDD, Actions and Transform ation on RDD ,
Ways to Create -file, data in memory, ot her RDD.
Lazy Execution, Persisting RDD
Self-Learning Topics: Machine Learning Algorithms like K -Means using
Spark. 2
7 Visualization: Connect to data, Build Charts and Analyze Data, Create
Dashboard, Create Stories using Tableau
Self-Learning Topics: Tableau using web. 6
Reference Books :
Reference
No Reference Name
1 Tom White, “HADOOP: The definitive Guide” O Reilly 2012, Third Edition,
ISBN: 978 -1-449-31152 -0
2 Chuck Lam, “Hadoop in Action”, Dreamtech Press 2016, First Edition ,ISBN:13
978817722813 7
3 Shiva Achari,” Hadoop Essential “ PACKT Publications, ISBN 978 -1-78439 -
668-8
4 RadhaShankarmani and M. Vijayalakshmi ,”Big Data Analytics “Wiley
Textbook Series, Second Edition, ISBN 9788126565757
5 Jeffrey Aven,”Apache Spark in 24 Hours” Sam‟s Publ ication, First Edition,
ISBN: 0672338513
6 Bill Chambers and Matei Zaharia, ”Spark: The Definitive Guide: Big Data
Processing Made Simple “ O‟Reilly Media; First edition, ISBN -10: 1491912219;
7 James D. Miller,” Big Data Visualization” PACKT Publications. I SBN -
10: 1785281941

Web References:
Reference No Reference Name
1 https://hadoop.apache.org/docs/stable/
2 https://hive.apache.org/
3 https://pig.apache.org/
4 https://spark.apache.org/documen tation.html
5 https://help.tableau.com/current/pro/desktop/en -us/default.htm

Page 179

Suggested list of experiments
Practical No Problem Statement
1 HDFS: List of Commands (mkdir, to uchz, copy from local/put, copy to local/get,
move from local, cp, rmr, du, dus, stat)
2 Map Reduce:
1. Write a program in Map Reduce for WordCount operation.
2. Write a program in Map Reduce for Union operation.
3. Write a program in Map Reduce for Intersecti on operation.
4. Write a program in Map Reduce for Grouping and Aggregation.
5. Write a program in Map Reduce for Matrix Multiplication
3 MongoDB :
1. Installation
2. Sample Database Creation
3. Query the Sample Database using MongoDB querying commands
a. Create Collect ion
b. Insert Document
c. Query Document
d. Delete Document
e. Indexing
4 Hive:
1. Hive Data Types
2. Create Database & Table in Hive
3. Hive Partitioning
4. Hive Built -In Operators
5. Hive Built -In Functions
6. Hive Views and Indexes
7. HiveQL : Select Where , Select OrderBy , Select G roupBy , Select Joins
5
Pig:
1. Pig Latin Basic
2. Pig Data Types,
3. Download the data
4. Create your Script
5. Save and Execute the Script
6. Pig Operations : Diagnostic Operators , Grouping and Joining , Combining &
Splitting , Filtering , Sorting
6 Spark :
1. Downloading Da ta Set and Processing it Spark
2. Word Count in Apache Spark.
7 Visualization using Tableau:
Tableau: Tool Overview, Importing Data, Analyzing with Charts, Creating
Dashboards, Working with maps, Telling Stories with tableau.
Assessment :
Term Work: Will b e based on Continuous Assessment
1. Laboratory work will be based on the syllabus with minimum 10 experiments.
Experiments 20 marks
Attendance 5 marks
2. Practical will be evaluated by the subject teacher and documented according to a rubrics.
End Semeste r Practical Examination:
Practical and oral examination will be based on suggested practical list and entire syllabus.

Page 180

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term Work Practical Oral Total
MCAL34 Distributed
System and
Cloud
Computing Lab
02 01 25 30 20 75

Pre-requisite: Basic overview of Distributed systems and Cloud Computing.
Lab Course Objectives:
Sr.No. Course Objective
1  Understand the concepts of Remote Process Communication, Remote Procedure
Call and Rem ote Method Invocation.
2 Understand the concepts of Remote Object Communication.
3 Understand the mutual exculsion concept.
4 Understand the implementation of Cloud Computing Services.
5 Learn implementation of Identity Management using Cloud Computin g concept.
6 Learn use of various tools and techniques to develop efficient, dynamic
applications.

Lab Course Outcomes:
Sr.No. Outcome Bloom Level
CO1 Develop Remote Process Communication, Remote Procedure Call
and Remote Method Invocation concepts . Applying
CO 2 Develop Remote Object Communication programs . Creating
CO 3 Develop mutual exclusion concept using Token ring algorithm.

Creating
CO 4 Implementation of Cloud Computing Services. Applying
CO 5 Implementation of Identity Management us ing Cloud
Computingconcept. Applying
CO 6 Design Apps using Cloud Computing for windows Azure / Amazon
AWS using Windows Azure Platform Training Kit and Visual
Studio and Google App Engine by using Eclipse IDE.
Creating
Description:
Module Detailed Con tents Hrs.

Page 181

1 Module: Remote Process Communication:
Develop a multi -client chat server application where multiple clients chat
with each other concurrently. The messages sent by different clients are
first communicated to the server and then the server, on behalf of the
source client, communicates the messages to the appropriate destination
client.
Self Learning Topics: Other applications based on Remote process
communication
02

2 Module: Remote Procedure Call:
A remote procedure call is an inter process communication technique
that is used for client -server -based applications. A client has a request
message that the RPC translates and sends to the server. This request
may be a procedure or a function call to a remote server. When the
server receives the request, it sends the required response back to the
client. The client is blocked while the server is processing the call and
only resumed execution after the server is finished.
Self Learning Topics: Other types of call semantics
04

3 Module: Remote M ethod Invocation:
The Remote Method Invocation is an API that provides a mechanism to
create distributed application in java. The client invokes methods via an
interface. These methods are implemented on the server side.
Self Learning Topics: Concept of cl ient and server applications, remote
interface, RMI registry tools
04

4 Module: Remote Object Communication:
Pass remote objects from the server to the client. The client will receive
the stub object (through remote interfaces) and saves it in an object
variable with the same type as the remote interface. Then the client can
access the actual object on the server through the variable.
Self Learning Topics: Concept of JDBC
04

5 Module: Mutual Exclusion:
Token ring algorithm solves the mutual exclusion existing in the process
communication.
Self Learning Topics: Other algorithms of Mutual Exclusion
02

6 Module: Implementation of Cloud Computing Services:
Cloud Computing provides different services such as SaaS, PaaS, IaaS,
Storage as service and many more. Storage as a Service is a business
model in which a large company rents space in their storage
infrastructure to a smaller company or individual.
Self Learning Topics: Other types of Cloud Services
02

7 Module: Implementation of Identity Manageme nt using Cloud
Computing concept:
The main goal of identity management is to ensure that only
authenticated users are granted access to the specific applications,
systems or IT environments for which they are authorized.
Self Learning Topics: Other tools to implement the technique 02

Page 182

8 Module: App Development using Cloud Computing:
Make use of various tools and techniques to develop efficient, dynamic
applications.
Self Learning Topics: Other Technique of application Development and
its Complexity 06


Reference Books :

Reference No Reference Name

1 Pradeep K. Sinha, Distributed Operating Systems concepts and design, PHI,
ISBN No. 978 -81-203-1380 -4
2 Herbert Schildt, The Complete Reference JAVA, Tata McGraw -Hill,7th
Edition,
ISBN No. 978 -0-07-1631 77-8
3 Horstmann, Cornell, Core Java 2 Volume I Fundamentals, Sun Micro System,
7th Edition, ISBN No -13:978 -0131482029
4 Horstmann, Cornell, Core Java 2 Volume II Advanced Features, Sun Micro
System, 7th Edition, ISBN No -13:978 -0131118263
5 Dr. Kumar Sa urabh, Cloud Computing insights into new -era infrastructure,
Willey ISBN No.10:8126528834
6 RajkumarBuyya, James Broberg, AndrzejGoscinski, Cloud Computing
Principles and Paradigms, Willey Publication, ISBN No. 9780470887998
7 GautamShroff, Enterprise C loud Computing Technology, Architecture,
Applications, Cambridge University Press, ISBN No. 978 -0-521-13735 -5
Web References:
Reference
No Reference Name

1 https://onlinelibrary.wiley.com/
2 https ://nptel.ac.in/courses/106106168/
3 https://nptel.ac.in/courses/106/105/106105167/
4 http://www.tutorialspoint.com
5 http://www.javapoint.com
6 https://aws.amazon.com/
Suggested list of experiments
Practical N o Problem Statement

1 To develop a program for multi -client chat server using Socket
2 To implement a Server calculator using RPC concept. (Make use of datagram)
3 To implement a Date Time Server using RPC concept. (Make use of
datagram)
4 To retrieve day, time and date function from server to client. This program
should display server day, time and date. (Use Concept of JDBC and RMI for
accessing multiple data access objects)
5 The client should provide an equation to the server through an interface . The
server will solve the expression given by the client.
6 Using MySQL create Library database. Create table Book (Book_id,
Book_name, Book_author) and retrieve the Book information from Library
database using Remote Object Communication concept.
7 Using MySQL create Elecrtic_Bill database. Create table Bill

Page 183

(consumer_name, bill_due_date, bill_amount) and retrieve the Bill
information from the Elecrtic_Bill database using Remote Object
Communication concept.
8 Implementation of mutual exclusion usin g Token ring algorithm.
9 Implementation of Storage as a Service using Google Docs
10 Implementation of Identity Management.
11 To develop Application for windows Azure / Amazon AWS using Windows
Azure Platform Training Kit and Visual Studio.
12 To develop applications using Google App Engine by using Eclipse IDE

Assessment :
Term Work: Will be based on Continuous Assessment
1. Laboratory work will be based on the syllabus with minimum 10 experiments.
Experiments 20 marks
Attendance 05 marks
2. Practi cal will be evaluated by the subject teacher and documented according to a rubric
End Semester Practical Examination:

Practical and oral examination will be based on suggested practical list and entire syllabus.

Page 184

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term Work Practical Oral Total
MCALE331 Block chain Lab 02 01 25 30 20 75
Pre-requisite: Basic programming skill in Python/ Java Script/Java.
Lab Course Objectives : The course aim to
Sr. No. Course Obj ective
1 Impart a thorough understanding of cryptographic algorithm and hash functions
2 Understand the concepts of Bitcoin and Smart Contract
3 Understand the concepts of Solidity language
4 Understand the deployment of Dapp in Ethereum
Lab Course Ou tcomes: On successful completion of course learner/student will be able to
Sr. No. Outcome Bloom Level
CO1 Implement encryption algorithms and hash functions Applying
CO2 Construct a bitcoin blocks and validating Applying
CO3 Construct a smart contract in Ethereum Applying
CO4 Develop and deploy Dapp in Ethereum Applying
Description:
Module Detailed Contents Hrs.
1 Module: Cryptography: Symmetric Encryption using Ceaser Cipher,
Asymmetric Encryption using RSA, Hash Functions (SHA -256), Merkle
Tree ( Implementation in Python/Java Script/C++) 06
2 Module:Cryptocurrency: Concept of Bitcoin, block, blockchain,
Immutable ledger , Public and Private Blockchain. (Implementation in
Python/Java Script/C++) 06
3 Module: Solidity Programming: Introducing Solidi ty, Sample Code,
Layout of Source File, Structure of a Contract, State Variables, Functions
Types, Reference Types, Units, Special Variables and Functions,
Expressions and Control Structures, Function Calls, Error Handling,
Visibility for Funct ions and State Variable 06
4 Module: Ethereum:
Ethereum Virtual Machine (EVM): Accounts, Transactions, Gas, Ether,
Memory
Dapp architecture: Developing a DApp, Compile and Deploy the Smart
Contract, Publish the DApp, Connecting to DApp, Ganache Output for
Transaction Migration 06
5 Module: Case Study: Use cases based on Hyper Ledger 02

Page 185

Reference Books :
Reference
No. Reference Name
1 David H. Hoover, Kevin Solorio, and Randall Kanna, Hands -On Smart Contract
Development with Solidity and Ethereum: Fr om Fundamentals to Deployment,
O‟Reilly Publications, ISBN -13: 978 -1492045267
2 Jimmy Song, Programming Bitcoin: Learn How to Program Bitcoin from
Scratch, O‟Reilly Publications, ISBN -13: 978 -1492031499
3 RiteshModi, Solidity Programming Essentials: A Beginner's Guide to Build
Smart Contracts for Ethereum and Blockchain, Packt Publications,
4 Chris Dannen, Introducing Ethereum and Solidity: Foundations of
Cryptocurrency and Blockchain Programming for Beginners, Apress
Web References:
Reference
No Reference Name
1 https://solidity.readthedocs.io/en/v0.6.7/
2 https://remix -ide.readthedocs.io/en/latest/#
3 https://www.sitepoint.com/solidity -for-beginners -a-guide -to-getting -started/
4 https://www.tutorialspoint.com/solidity/index.htm
5 https://bitco in.org/en/getting -started
6 https://docs.python.org/3/library/hashlib.html
Suggested list of experiments:
Practical
No. Problem Statement
1 Implementation of Ceaser Cipher (Symmetric Encryption)
2 Implementation of RSA Algorithm (Asymmetric Encryption)
3 Implementation of SHA -256
4 Implementation of Binary Tree
5 Implement the creation of Bitcoin Block (Genesis Block)
6 Implement the creation of a Blockchain (Adding the blocks to the chain and
validating)
7 Implement the creation of a public/priva te Blockchain
8 Implementation of an immutable Ledger
9 Simple Experiments using Solidity Program Constructs (if -then, while etc...)
10 Creation of smart contract in Ethereum
11 Creation of Dapp in Ethereum
12 Mini Project
Assessment :
Term Work: Will be based on Continuous Assessment

1. Laboratory work will be based on the syllabus with minimum 10 experiments.
Experiments 20 marks
Attendance 5 marks
2. Practical will be evaluated by the subject teacher and documented according to a rubric
End Semest er Practical Examination:
Practical and oral examination will be based on suggested practical list and entire syllabus.

Page 186

Course
Code Course Name Contact
Hours Credits
Assigned Examination Scheme
Term
Work Practical Oral Total
MCALE332 Deep
Learning Lab 02 01 25 30 20 75
Prerequisite: Basic understanding of machine learning concepts. Lab Course Objectives
Sr.No. Course Objective
1 To understand dataset and pre -processing to build neural network models.
2 To apply appropriate learning rules for each of the architectures and build several neural network
models.
3 To learn different regularization and optimization techniques used in deep learning
4 To identify the problems, choose relevant deep learning algorithms and analyze the results for
respective applications.
`Lab Course Outcomes:
Sr.No. Outcome Bloom Level
CO1 Demonstrate Tensor flow/Keras deep -learning workstations. Understanding
CO2 Choose appropriate data preprocessing techniques to build neural network models. Applying
CO 3 Analyze different regularization and optimization techniques used in deep
learning. Analyzing
CO 4 Build neural network models using deep learning algorithms -CNN and RNN to
solve real world problems.
Creating
Description:
Module
No Detailed Contents Hrs
I Introduction to Tensor flow/Keras -Installation, Importing Libraries and Modules.
Self Learning Topic: -Setting up a deep -learning workstation. 2
II Working with Dataset -Loading the dataset, Splitting dataset into training and testing data
sets.
Self Learning Topic: -Data representations for neural networks 2
III Data Preprocessing Techniques - Numerical Data, Feature Scaling, Handling Missing
Values, Categorical Data and String Data Types, Encoding, Data Splitting.
Self Learning Topic: - Outliers detection. 2
IV Artificial Neural Networks - McCulloch -Pitts neuron, single layer perceptron network,
multi -layer perceptron network, Back propagation network.
Self Learning Topic: - Adaline Network 6
V Regularization Techniques - Dataset Augmentation, Early Stopping, D ropout.
Self Learning Topic: - Optimization techniques(any one) 2