TE Information Technology Sem V VI1 Syllabus Mumbai University

TE Information Technology Sem V VI1 Syllabus Mumbai University by munotes

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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, (Publi cation 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 and Open Learni ng (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 Lifelong 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, Dep artment 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, Ratnagiri 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.

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AC: 29/6/2021

Item No. : 6.12

UNIVERSITY OF MUMBAI

Bachelor of Engineering
in
Information Technology
Third Year with Effect from AY 2021 -22

(REV - 2019 ‘C’ Scheme) from Academic Year 2019 – 20
Under
FACULTY OF SCIENCE & TECHNOLOGY

(As per AICTE guidelines with effect from the academic year
2019 –2020 )

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Date : 29/6/2021

Dr. S. K. Ukarande Dr Anuradha Muzumdar
Associate Dean Dean
Faculty of Science and Technology Faculty of Science and Technology
University of Mumbai University of Mumbai

Sr. No. Heading Particulars
1 Title of the Course Third Year Bachelor of Information Technology
2 Eligibility for Admission
After Passing Second Year Engineering as per the
Ordinance 0.6243
3 Passing Marks 40%
4 Ordinances /
Regulations ( if any) Ordinance 0.6243
5 No. of Years / Semesters 8 semesters
6 Level Under Graduation

7 Pattern Semester

8 Status Revised

9 To be implemented from
Academic Year With effect from Academic Year: 2021 -2022

AC: 29/6/2021

Item No.
6.12

UNIVERSITY OF MUMBAI

Syllabus for Approval

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Preamble

To meet the challenge of ensuring excellence in engineering 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 education. The major emphasis of accreditation process is to measure the outcomes of the program that is
being accr edited. In line with this Faculty of Science and Technology (in particular Engineering)of University of
Mumbai has taken a lead in incorporating philosophy of outcome based education in the process of curriculum
development.
Faculty resolved that course ob jectives and course outcomes are 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 learning process. Choice based Credit and gr ading system 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 enhan ce the quality of education.
Credit assignment for courses is based on 15 weeks teaching learning process, however content o f courses is to be
taught in 13 weeks and remaining 2 weeks to be utilized for revision, guest lectures, coverage of content beyond
syllabus etc.
There was a concern that the earlier revised curriculum more focused on providing information and knowledge
across various domains of the said program, which led to heavily loading of students in terms of direct contact
hours. In this regard, faculty of science and technology resolved that to minimize the burden of contact hours, total
credits of entire program will be of 170, wherein focus is not only on providing knowledge but also on building
skills, attitude and self learning. Therefore in the present curriculum skill based laboratories and mini projects are
made mandatory across all disciplines of engineering in second and third year of programs, which will definitely
facilitate self learning of students. The overall credits and approach o f curriculum proposed in the present revision
is in line with AICTE model curriculum.
The present curriculum will be implemented for Second Year of Engineering from the academic year 2020 -21.
Subsequently this will be carried forward for Third Year and Final Year Engineering in the academic years 2021 -
22, 2022 -23, respectively.

Dr. S. K. Uka rande Dr. Anuradha Muzumdar
Associate Dean Dean
Faculty of Scie nce and Technology Faculty of Science and Technology
University of Mumbai University of Mumbai

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Incorporation and Implementation of Online Contents from
NPTEL/ Swayam Platform

The curriculum revision is mainly focused on knowledge component, skill based activities and project based
activities. Self learning opportunities are provided to learners. In the revision process this time in particular
Revised syllabus of ‘C ‘ scheme whe rever possible additional resource links of platforms such as NPTEL,
Swayam are appropriately provided. In an earlier revision of curriculum in the year 2012 and 2016 in Revised
scheme ‘A' and ‘B' respectively, efforts were made to use online contents more appropriately as additional
learning materials to enhance learning of students.
In the current revision based on the recommendation of AICTE model curriculum overall credits are reduced
to 171, to provide opportunity of self learning to learner. Learners are now getting sufficient time for self
learning either through online courses or additional projects for enhancing their knowledge and skill sets.
The Principals/ HoD’s/ Faculties of all the institute are required to motivate and encourage learners to u se
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 facilitat e their enhanced learning based on their interest.

Dr. S. K. Ukarande Dr Anuradha Muzumdar
Associate Dean Dean
Faculty of Science and Technology Faculty of Science and Technology
University of Mumbai University of Mum bai

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Preface By Bo ard of Studies Team

It is our honor and a privilege to present the Rev-2019 ‘C’ scheme syllabus of Bachelor of Engineering
in Information Technology (effective from year 2019 -20) with inclusion of cutting edge technology.
Information Technology is comparatively a young branch among other engineering disciplines in the
University of Mumbai. It is evident from the placement statistics of various colleges affiliated to the
University of Mumbai that IT branch has ta ken the lead in the placement.

The branch also provides multi -faceted scope like better placement and promotion of entrepreneurship
culture among students, and increased Industry Institute Interactions. Industries views are considered
as stakeholders wil l design of the syllabus of Information Technology. As per Industries views only 16
% graduates are directly employable. One of the reasons is a syllabus which is not in line with the latest
technologies. Our team of faculties has tried to include all the latest technologies in the syllabus. Also
first time we are giving skill -based labs and Mini -project to students from third semester onwards
which will help students to work on latest IT technologies. Also the first time we are giving the choice
of electiv e from fifth semester such that students will be master in one of the IT domain. The syllabus
is peer reviewed by experts from reputed industries and as per their suggestions it covers future trends
in IT technology and research opportunities available due to these trends.

We would like to thank senior faculties of IT department of all colleges affiliated to University of
Mumbai for significant contribution in framing the syllabus. Also on behalf of all faculties we thank
all the industry experts for their valuable feedback and suggestions. We sincerely hope that the revised
syllabus will help all graduate engineers to face the future challenges in the field of information and
technology

Program Specific Outcome for graduate Program in Information Technology

1. Apply Core Information Technology knowledge to develop stable and secure IT system.
2. Design, IT infrastructures for an enterprise using concepts of best practices in information
Technology and security domain .
3. Ability to work in mul tidisciplinary projects and make it IT enabled.
4. Ability to adapt latest trends and technologies like Analytics, Blockchain, Cloud, Data science.

Board of Studies in Information Technology - Team
Dr. Deven Shah ( Chairman)
Dr. Lata Ragha (Member)
Dr. Vaishali D. Khairnar (Member)
Dr. Sharvari Govilkar (Member)
Dr. Sunil B. Wankhade (Member)
Dr. Anil Kale (Member)
Dr. Vaibhav Narwade (Member)
Dr. GV Choudhary (Member)

Ad-hoc Board Information Technology
University of Mumbai

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Program Structure for Third Year Information Technology
Semester V & VI
UNIVERSITY OF MUMBAI
(With Effect from 2021 -2022)

Semester V

Course
Code
Course Name Teaching
Scheme
(Contact
Hours)
Credits Assigned
Theory Pract. Theory Pract. Total
ITC501 Internet Programming 3 -- 3 -- 3
ITC502 Computer Network Security 3 -- 3 3
ITC503 Entrepreneurship and E -
business 3 -- 3 -- 3
ITC504 Software Engineering 3 -- 3 -- 3
ITDO501 X Department Optional Course
– 1 3 -- 3 -- 3
ITL501 IP Lab -
- 2 -- 1 1
ITL502 Security Lab -
- 2 -- 1 1
ITL503 DevOPs Lab -
- 2 -- 1 1
ITL504 Advance DevOPs Lab - 2 -- 1 1

ITL505 Professional Communication
& Ethics -II (PCE -II)
-
-
2*+2
--
2
2
ITM501 Mini Project – 2 A Web Based
Business Model -
- 4$ -- 2 2
Total 15 16 15 08 23

Course
Code

Course Name Examination Scheme
Theor
y Term
Work Prac
/oral Total

Internal Assessment End
Sem
Exam Exam.
Duration
(in Hrs)
Test1 Test2 Avg
ITC501 Internet Programming 20 20 20 80 3 -- -- 100
ITC502 Computer Network Security 20 20 20 80 3 -- -- 100
ITC503 Entrepreneurship and E -
business 20 20 20 80 3 -- -- 100
ITC504 Software Engineering 20 20 20 80 3 -- -- 100
ITDO501 X Department Optional Course
– 1 20 20 20 80 3 -- -- 100
ITL501 IP Lab -- -- -- -- -- 25 25 50
ITL502 Security Lab -- -- -- -- -- 25 25 50
ITL503 DevOPs Lab -- -- -- -- -- 25 25 50

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ITL504 Advance DevOPs Lab -- -- -- -- -- 25 25 50

ITL505 Professional Communication &
Ethics -II (PCE -II)
--
--
--
--
--
50
--
50
ITM501 Mini Project – 2 A Web Based
Business Model -- -- -- -- -- 25 25 50
Total -- -- 100 400 -- 175 125 800

* Theory class to be conducted for full class
$ indicates work load of Learner (Not Faculty), for Mini -Project. S tudents can form groups with minimum
2(Two) and not more than 4(Four). Faculty Load: 1hour per week per four groups.

ITDO501X Department Optional Course – 1

ITDO5011 Microcontroller Embedded Programming
ITDO5012 Advance Data Management Technologies
ITDO5013 Computer Graphics & Multimedia System
ITDO5014 Advanced Data structure and Analysis

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Program Structure for Third Year Information Technology
Semester V & VI
UNIVERSITY OF MUMBAI
(With Effect from 2021 -2022)

Semester VI

Course
Code
Course Name Teaching Scheme
(Contact Hours)
Credits Assigned
Theory Pract.
Tut. Theory Pract. Total
ITC601 Data Mining &
Business Intelligence 3 -- 3 -- 3
ITC602 Web X.0 3 -- 3 3
ITC603 Wireless Technology 3 -- 3 -- 3
ITC604 AI and DS – 1 3 -- 3 -- 3
ITDO601
X Department Optional
Course – 2 3 -- 3 -- 3
ITL601 BI Lab -- 2 -- 1 1
ITL602 Web Lab -- 2 -- 1 1
ITL603 Sensor Lab -- 2 -- 1 1
ITL604 MAD & PWA Lab -- 2 -- 1 1
ITL605 DS using Python Skill based
Lab -- 2 -- 1 1
ITM601 Mini Project – 2 B Based on
ML -- 4$ -- 2 2
Total 15 14 15 07 22

Course
Code

Course Name Examination Scheme
Theory Term
Work Prac
/oral Total

Internal Assessment End
Sem
Exam Exam.
Duration
(in Hrs)
Test1 Test2 Avg
ITC601 Data Mining &
Business Intelligence 20 20 20 80 3 -- -- 100
ITC602 Web X.0 20 20 20 80 3 -- -- 100
ITC603 Wireless Technology 20 20 20 80 3 -- -- 100
ITC604 AI and DS – 1 20 20 20 80 3 -- -- 100
ITDO601
X Department Optional
Course – 2 20 20 20 80 3 -- -- 100
ITL601 BI Lab -- -- -- -- -- 25 25 50
ITL602 Web Lab -- -- -- -- -- 25 25 50
ITL603 Sensor Lab -- -- -- -- -- 25 25 50
ITL604 MAD & PWA Lab -- -- -- -- -- 25 25 50
ITL605 DS using Python Lab
(SBL) -- -- -- -- -- 25 25 50

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ITM601 Mini Project – 2 B Based on
ML -- -- -- -- -- 25 25 50
Total -- -- 100 400 -- 150 150 800

$ indicates work load of Learner (Not Faculty), for Mini -Project. S tudents can form groups with minimum
2(Two) and not more than 4(Four). Faculty Load: 1hour per week per four groups.

ITDO601X Department Optional Course – 2
ITDO6011 Software Architecture
ITDO6012 Image Processing
ITDO6013 Green IT
ITDO6014 Ethical Hacking and Forensic

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Course Code Course Name Teaching Scheme
(Contact Hours)
Credits Assigned
Theory Practical Theory Practical Total
ITC501 Internet
Programming 03 -- 03 -- 03

Course Code Course Name Examination Scheme
Theory
Term
Work Pract
/ Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test2 Avg.
ITC501 Internet
Programming 20 20 20 80 03 -- -- 100

Course Objectives:
Course Outcomes:
Sr. No. Course Outcomes Cognitive levels of
attainment as per
Bloom’s Taxonomy
On successful completion, of course, learner/student will be able to:
1 Select protocols or technologies required for various web applications. L1,L2,L3, L4
2 Apply JavaScript to add functionality to web pages. L1, L2, L3
3 Design front end application using basic React. L1,L2,L3,L4,L5, L6
4 Design front end applicat ions using functional components of React. L1,L2,L3,L4,L5,L6
5 Design back -end applications using Node.js. L1,L2,L3,L4,L5,L6
6 Construct web based Node.js applications using Express. L1,L2,L3,L4,L5,L6

Prerequisite: Knowledge of basic programming, network fundamentals and operating systems.

Sr. No. Course Objectives
The course aims:
1 To orient students to Web Programming fundamental.
2 To expose students to JavaScript to develop interactive web page development
3 To orient students to Basics of REACT along with installation
4 To expose students to Advanced concepts in REACT
5 To orient students to Fundamentals of node.js
6 To expose students to node.js applications using express framework.

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DETAILED SYLLABUS:

Sr.
No. Module Detailed Content Hours CO Mapping
0 Prerequisite Introduction and basics of HTML, CSS 02 -
I Web
programming
fundamentals
Working of web browser, HTTP protocol,
HTTPS, DNS, TLS, XML introduction, Json
introduction, DOM, URL, URI, REST API.
Self-learning Topics: : Nginx server 03 CO1
II Java script:
Introduction to ES6, Difference between ES5 and
ES6. Variables, Condition, Loops, Functions,
Events, Arrow functions,
Setting CSS Styles using JavaScript, DOM
manipulatio n, Classes and Inheritance.
Iterators and Generators, Promise, Client -server
communication, Fetch

Self-learning Topics: Asynchronous JavaScript,
JSON 06 CO2
III React
fundamentals
Installation, Installing libraries, Folder and file
structure, Components, Component lifecycle,
State and Props, React Router and Single page
applications, UI design, Forms, Events,
Animations, Best practices.

Self-learning Topics : React vs Angular vs Vue 07 CO3
IV Advanced
React:
Functional components - Refs, Use effects,
Hooks, Flow architecture, Model -View -
Controller framework, Flux, Bundling the
application. Web pack.

Self-learning Topics: React Native 07 CO4
V Node.js:
Environment setup, First app, Asynchronous
programming, Callback concept, Event loops,
REPL, Event emitter, Networking module,
Buffers, Streams, File system, Web module.

Self-learning Topics: Node.js with Mongodb. 07 CO5
VI Express:
Introduction, Express router, REST API,
Generator, Authentication, sessions, Integrating
with React.

Self-learning Topics: Commercial deployment. 07 CO6

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Text Books:

1. Rediscovering JavaScript, Master ES6, ES7, and ES8, By Venkat Subramaniam · 2018
2. Learning React Functional Web Development with React and Redux, Alex Banks and
Eve Porcello, O’Reilly
3. Learning Redux, Daniel Bugl, Packt Publication
4. Learning Node.js Development, Andrew Mead, Packt Publishing
5. RESTful Web API Design with Node.js 10, Valentin Bojinov, Packt Publication

References :

1. Web Development with Node and Express, Etha n Brown, O’Reilly

Online Resources :

2. https://reactjs.org/tutorial/tutorial.html
3. https://react -redux.js.org/introduction/quick -start
4. https://webpack.js.org/
5. https://www.youtube.com/watch?v= -27HAh8c0YU

Assessment:
Internal Assessment (IA) for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to 50%
of syllabus content must be covered in First IA Test and remaining 40% to 50% of syllabus
content must be covered in Second IA Test
 Question paper format
 Question Paper will comprise of a total of six questions each carrying 20 marksQ.1 will be
compulsory and should cover maximum contents of the syllabu s
 Remaining questions will be mixed in nature (part (a) and part (b) of each question must be
from different modules. For example, if Q.2 has part (a) from Module 3 then part (b) must be
from any other Module randomly selected from all the modules)
 A total of four questions need to be answered

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Course Code Course Name Teaching Scheme
(Contact Hours)
Credits Assigned
Theory Practical Theory Practical Total
ITC502 Computer
Network
Security 03 -- 03 -- 03

Course Code Course Name Examination Scheme
Theory
Term
Work Pract
/ Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test2 Avg.
ITC502 Computer Network
Security 20 20 20 80 03 -- -- 100

Course Objectives:

Course Outcomes:

Sr.
No.

Course Outcomes Cognitive levels of
attainment as per
Bloom’s Taxonomy
On successful completion, of course, learner/student will be able to:
1 Explain the fundamentals concepts of computer security and network
security. L1, L2
2 Identify the basic cryptographic techniques using classical and block
encryption methods. L1
3 Study and describe the system security malicious software. L1, L2
4 Describe the Network layer security, Transport layer security and
application layer security. L1, L2
5 Explain the need of network management security and illustrate the need
for NAC. L1, L2
6 Identify the function of an IDS and firewall for the system security. L1,L2, L3

Prerequisite: Basic concepts of Computer Networks & Network Design, Operating System Sr. No. Course Objectives
The course aims:
1 The basic concepts of computer and Network Security
2 Various cryptographic algorithms including secret key management and different authentication
techniques.
3 Different types of malicious Software and its effect on the security.
4 Various secure communication standards including IPsec, SSL/TLS and email.
5 The Network management Security and Network Access Control techniques in Computer Security.
6 Different attacks on networks and infer the use of firewalls and security protocols.

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DETAILED SYLLABUS:
Sr.
No. Module Detailed Content Hours CO
Mapping
0 Prerequisite Basic concepts of Computer Networks & Network Design,
Operating System 02 --
I Introduction
to Network
Security &
cryptography Computer security and Network Security(Definition), CIA,
Services, Mechanisms and attacks, The OSI security
architecture, Network security model. Classical Encryption
techniques (mono -alphabetic and poly -alphabetic
substitution techniques: Vig enere cipher, playfair cipher,
transposition techniques: keyed and keyless transposition
ciphers). Introduction to steganography.

Self-learning Topics: Study some more classical
encryption techniques and solve more problems on all
techniques. Homomorphi c encryption in cloud computing 07 CO1
II Cryptography:
Key
management,
distribution
and user
authentication Block cipher modes of operation,Data Encryption Standard,
Advanced Encryption Standard (AES). RC5 algorithm.
Public key cryptography: RSA algorithm.
Hashing Techniques: SHA256, SHA -512, HMAC and
CMAC,
Digital Signature Schemes – RSA, DSS. Remote user
Authentication Protocols, Ker beros, Digital Certificate:
X.509, PKI

Self-learning Topics: Study working of elliptical curve
digital signature and its benefits over RSA digital signature. 09 CO2
III Malicious
Software SPAM, Trojan horse, Viruses, Worms, System Corruption,
Attack Agents, Information Theft, Trapdoor, Keyloggers,
Phishing, Backdoors, Rootkits, Denial of Service Attacks,
Zombie

Self-learning Topics: Study the recent malicious software’s
and their effects. 04 CO3
IV
IP Security,
Transport
level security
and Email
Security

IP level Security: Introduction to IPSec, IPSec Architecture,
Protection Mechanism (AH and ESP), Transport level
security: VPN. Need Web Security considerations, Secure
Sockets Layer (SSL)Architecture, Transport Layer Security
(TLS), HTTPS , Secure Shell (SSH) Protocol Stack. Email
Security: Secure Email S/MIME
Screen reader support enabled.

Self-learning Topics: Study Gmail security and privacy
from Gmail help
07 CO4
V Network
Management
Security and
Network
Access
Control Network Management Security:SNMPv3,
NAC:Principle elements of NAC,Principle NAC
enforcement methods, How to implement NAC Solutions,
Use cases for network access control

Self-learning Topics: Explore any open source network
management security tool 06 CO5

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Textbooks:

1 William Stallings, Cryptography and Network Security, Principles and Practice, 6th Edition,
Pearson Education, March 2013.
2 Behrouz A. Ferouzan, “Cryptography & Network Security”, Tata Mc Graw Hill.
3 Mark Stamp’s Information Security Principles and Practice, Wiley
4 Bernard Menezes, “Cryptography & Network Security”, Cengage Learning.

References:

1 Applied Cryptography, Protocols, Algorithms and Source Code in C, Bruce Schneier, Wiley.
2 Cryptography and Network Security, Atul Kahate, Tata Mc Graw Hill.
3 www.rsa.com

Online References:

Sr. No. Website Name
1. https://swayam.gov.in/
2. https://nptel.ac.in/
3. https://www.coursera.org/

Assessment:
Internal Assessment (IA) for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to
50% of syllabus content must be covered in First IA Test and remaining 40% to 50% of
syllabus content must be covered in Second IA Tes t
 Question paper format
 Question Paper will comprise of a total of six questions each carrying 20 marksQ.1
will be compulsory and should cover maximum contents of the syllabus
 Remaining questions will be mixed in nature (part (a) and part (b) of each quest ion
must be from different modules. For example, if Q.2 has part (a) from Module 3 then
part (b) must be from any other Module randomly selected from all the modules)
 A total of four questions need to be answered

VI System
Security IDS, Firewall Design Principles, Characteristics of
Firewalls, Types of Firewalls

Self-learning Topics: Study firewall rules table 04 CO6

Page 18

Course Code Course Name Teaching Scheme
(Contact Hours)
Credits Assigned
Theory Practical Theory Practical Total
ITC503 Entrepreneurship
and E -business 03 -- 03 -- 03

Course Code Course Name Examination Scheme
Theory
Term
Work Pract
/ Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test2 Avg.
ITC503 Entrepreneurship
and E -business 20 20 20 80 03 -- -- 100

Course Objectives:

Course Outcomes:
Sr. No. Course Outcomes Cognitive levels
of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Understand the concept of entrepreneurship and its close
relationship with enterprise and owner -management. L1,L2
2 Understand the nature of business development in the context of
existing organizations and of new business start -ups. L1,L2
3 Comprehended important factors for starting a new venture and
business development. L1,L2,L3
4 Know issues and decisions involved in financing and resourcing a
business start -up L1,L2,L3,L4
5 Describe various E -business Models L1,L2,L3,L4
6 Discuss various E -business Strategies. L1,L2,L3,L4

Prerequisite: None

Sr. No. Course Objectives
The course aims:
1 Distinguish Entrepreneur and Entrepreneurship starting and feasibility study.
2 Realize the skills required to be an entrepreneur
3 Acquaint the students with challenges of starting new ventures
4 Identify the right sources of fund for starting a new business
5 Be familiarized with concept of E -business Models.
6 Understand various E -business Strategies.

Page 19

DETAILED SYLLABUS:

Sr.
No. Module Detailed Content Hours CO Mapping
0 Prerequisite None -- --
I Introduction Concept, meaning and definition of Entrepreneur and
Entrepreneurship. Evolution of Entrepreneurship,
Role of Entrepreneurship in economic Development;
Managerial vs entrepreneurial approach;
Classification and types of Entrepreneurs.
Characteristics and qualities of successful
Entrepreneurs; Women Entrepreneurs; Corporate &
Social entrepreneurship.

Self-learning Topics: Factors impacting emergence
of entrepreneurship . 04 CO1
II Entrepreneu
rship
Developme
nt and
Leadership Entrepreneurial Motivation: motivating factors, Types
of startups; Characteristics of entrepreneurial
leadership, Components of Entrepreneurial
Leadership; Factors influencing entrepreneurial
development and motivation, Entrepreneurial
Opportunities and challenges, Entrepreneurship
process. Types of Enterprises and Ownership
Structure: small scale, medium scale and large -scale
enterprises: Meaning and definition (evolution), role
of small enterprises in economic development;
proprietorship, Policies governing SMEs, partnership,
Ltd. companies and co -operatives: their formation,
capital structure and source of finance.

Self-learnin g Topics: study the white paper
https://www.ncert.nic.in/ncerts/l/lebs213.pdf 06 CO2
III New
Venture
Planning Methods to Initiate Ventures; Acquisition -Advantages
of acquiring an ongoing venture and examination of
key issues; Developing a Marketing plan-customer
analysis, sales analysis and competition analysis,
Business Plan -benefits of drivers, perspectives in
business plan preparation, elements of a business plan;
Business plan failures.

Self-learning Topics: Refer following URL to study
various case studies
https://www.entrepreneurindia.co/case -studies 07 CO3
IV Financing &
Managing
Venture Financing Stages; Sources of Finance; Venture
Capital; Criteria for evaluating new -venture proposals
& Capital -process. Management of venture: objectives
and functions of management, scientific management,
general and strategic management; introduction to
human resource management: planning, job analysis,
training, recruitment and selection

Self-learning Topics: visit website 06 CO4

Page 20

https://www.startupindia.gov.in

V Overview of
E –
business Concept of E -business, Business Success through
adoption of technology, information management for
business Initiatives, Performance improvement
through e -business. Introduction to various
collaborative partnerships, E -commerce: Sectors of e -
commerce, B to C, B to B and C to C ecommerce, E -
commerce success factors, clicks and Bricks in
ecommerce, collaborative commerce. E -Marketplace,
M-commerce, E -Government; Various E -business
Models, Challenges of the E -Business Models,
Globalization of E -business.

Self-learning Topics: Social media applications for
E-Business, Social media analytics. 08 CO5
VI Strategic
Initiatives
for
Technology Customer Relationship Management:
The evolution of CRM, functional areas of CRM,
contemporary trends - SRM, PRM AND ERM,
Future Trends of CRM
Enterprise Resource Planning :
Core and Extended ERP; components of ERP system;
Benefits and Risks of ERP implementation
Supply Chain Management :
Meaning, definition, importance, and characteristics
of SCM, Elements of SCM, Push & Pull supply chain
model, Use of e -business to restructure supply chain,
Supply chain management implementation
Procurement :
Meaning and advantages of e –procurement,
Types& Drivers of e - procurement, Components of e -
procurement systems, Implementation of e -
procurement

Self-learning Topics: SEM and SEO E -CRM 08 CO6

Textbooks:
1 Entrepreneurship; Robert Hisrich, Michael Peters; Tata McGraw Hill Publication
2 Entrepreneurship: New venture creation by David Holt, Prentice Hall of India Pvt. Ltd.
3 E- Business & E – Commerce Management: Strategy, Implementation, Practice – Dave
Chaffey, Pearson Education
4 E-commerce – A Managerial Perspective - P. T. Joseph, Prentice Hall India Publications.
Content

References:
1 Entrepreneurship and Innovations in E -business An Integrative Perspective by Fang Zhao,
Idea Group Publications.
2 Business Driven Technology –Haag/Baltzan/Philips –Tata McGraw Hill Publication
3 1. Digital Business and E -commerce Management by Dave Chaf fey, David Edmundson -
Bird, Tanya Hemphill , Pearson Education
4

5 E-Business 2.0 Roadmap for Success by Dr. Ravi Kalakota, Marcia Robinson, Pearson
Education
Case Studies in International Entrepreneurship: Managing and Financing Ventures in the
Global Economy. By Walter Kuemmerle, Walter Kuemmerle. McGraw -Hill/Irwin, 2004.

Page 21

Note: - It is advisable that faculty should discuss case studi es in the classroom

Assessment:
Internal Assessment (IA) for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to 50% of
syllabus content must be covered in First IA Test and remaining 40% to 50% of syllabus content
must be covered in Second IA Test
 Question paper format
 Question Paper will comprise of a total of six questions each carrying 20 marksQ.1 will be
compulsory and should cover maximum contents of the syllabus
 Remaining questions will be mixed in nat ure (part (a) and part (b) of each question must
be from different modules. For example, if Q.2 has part (a) from Module 3 then part (b)
must be from any other Module randomly selected from all the modules)
 A total of four questions need to be answered

ISBN: 0072977841.

Page 22

Course Code Course Name Teaching Scheme
(Contact Hours)
Credits Assigned
Theory Practical Theory Practical Total
ITC504 Software
Engineering 03 -- 03 -- 03

Course Code Course Name Examination Scheme
Theory Term
Work Pract/
Oral Total
Internal Assessment End
Sem
Exam Exam
Duratio
n
(in Hrs)
Test1 Test 2 Avg.
ITC504 Software
Engineering 20 20 20 80 03 -- -- 100

Course Objectives:

Course Outcomes:

Sr. No. Course Outcomes Cognitive
levels of
attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Understand and use basic knowledge in software engineering. L1, L2
2 Identify requirements, analyze and prepare models. L1, L2, L3
3 Plan, schedule and track the progress of the projects. L1, L2, L3
4 Design & develop the software solutions for the growth of society L1, L2, L3
5 To demonstrate and evaluate real time projects with respect to software
engineering principles L1, L2, L3, L4
6 Apply testing and assure quality in software solution L1, L2, L3, L4

Prerequisite: Basic programming of knowledge.

Sr. No. Course Objectives
The course aims:
1 To provide the knowledge of software engineering discipline.
2 To understand Requirements and analyze it
3 To do planning and apply scheduling
4 To apply analysis, and develop software solutions
5 To demonstrate and evaluate real time projects with respect to software engineering
principles
6 Apply testing and assure quality in software solution.

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DETAILED SYLLABUS:

Sr.
No. Module Detailed Content Hours CO
Mapping
0 Prerequisite None -- --
I Introduction to
Software
Engineering
Nature of Software, Software Engineering, Software
Process, Capability Maturity Model (CMM)

Generic Process Model, Prescriptive Process Models: The
Waterfall Model, V -model, Incremental Process Models,
Evolutionary Process Models, Concurrent Models, Agile
process, Agility Principles, Extreme Programming (XP),
Scrum, Kanban model

Self-learning Topics: Personal and Team Process
Models
06 CO1,CO2
II Requirement
Analysis
Software Requirements: Functional & non -functional –
user-system requirement engineering process – feasibility
studies – elicitation – validation & management –
software prototyping – S/W documentation – Analysis
and modelling

Requirement Elicitation, Software requirement
specification (SRS),

Self-learning Topics: prioritizing requirements (Kano
diagram) - real life application case study.
07 CO1,CO2
III Software
Estimation and
Scheduling
Management Spectrum, 3Ps (people, product and process)

Process and Project metrics

Software Project Estimation: LOC, FP, Empirical
Estimation Models - COCOMO II Model, Specialized
Estimation Techniques, Object based estimation, use -case
based estimation

Project scheduling: Defining a Task Set for the Software
Project, Timeline charts, Tracking the Schedule, Earned
Value Analysis

Self-learning Topics: Cost Estimation Tools and
Techniques, Typical Problems with IT Cost Estimates. 06 CO3
IV Design
Engineering
Design Process & quality, Design Concepts, The design
Model, Pattern -based Software Design. 4.2 Architectural
Design :Design Decisions, Views, Patterns, Application
Architectures, Modeling

Component level Design: component, Designing class
based components, conducting c omponent -level design,

User Interface Design: The golden rules, Interface Design 07 CO3, CO4

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steps & Analysis, Design Evaluation

Self-learning Topics: Refinement, Aspects, Refactoring

V Software Risk,
Configuration
Management
Risk Identification, Risk Assessment, Risk Projection,
RMMM

Software Configuration management, SCM repositories,
SCM process

Software Quality Assurance Task and Plan, Metrics,
Software Reliability, Formal Technical Review (FTR),
Walkthrough

Self-learning Topics: : Configuration management for
WebApps
07 CO5
VI Software
Testing and
Maintenance
Testing: Software Quality, Testing: Strategic Approach,
Strategic Issues - Testing: Strategies for Conventional
Software, Object oriented software, Web Apps -
Validating Testing - System Testing - Art of Debugging.

Maintenance : Software Maintenance -Software
Supportability - Reengineering - Business Process
Reengineering - Software Reengineering - Reverse
Engineering - Restructuring - Forward Engineering

Self-learning Topics: Test Strategies for WebApps
06 CO6

Text Bo oks:

1 Roger S. Pressman, Software Engineering: A practitioner's approach, McGraw Hill
2 Rajib Mall, Fundamentals of Software Engineering, Prentice Hall India
3 PankajJalote, An integrated approach to Software Engineering, Springer/Narosa.
4 Ian Sommerville, Software Engineering, Addison -Wesley.

References:

1 https://nptel.ac.in/courses/106/101/106101061/
2 https://www.youtube.com/watch?v=wEr6mwquPLY
3 http://www.nptelvideos.com/video.php?id=911&c=9
4 https://onlinecourses.nptel.ac.in/noc19_cs70/unit?unit=25 &lesson=66
5 https://onlinecourses.nptel.ac.in/noc19_cs70/unit?unit=25&lesson=67
6 https://onlinecourses.nptel.ac.in/noc19_cs70/unit?unit=25&lesson=65
7 https://onlinecourses.nptel.ac.in/noc19_cs70/unit?unit=25&lesson=64
8 https://onlinecourses.nptel.a c.in/noc19_cs70/unit?unit=25&lesson=63

Preferable : Case studies can be discussed on every unit as per requirement for better
understanding, examples are given below.
Unit 1 An information system (mental health -care system), wilderness weather system.
Unit 2 Mental health care patient management system (MHC -PMS).
Unit 3 Software Tools for Estimation.

Page 25

Page 26

Course Code Course
Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Theory Practical Total
ITL501 IP Lab -- 02 -- 01 01

Course
Code Course
Name Examination Scheme
Theory Term
Work
Pract /
Oral
Total

Internal Assessment End
Sem
Exam
Exam
Duration
(in Hrs)

Test1 Test 2 Avg.
ITL501 IP Lab -- -- -- -- -- 25 25 50

Lab Objectives:

Sr. No. Lab Objectives
The Lab aims:
1 To orient students to HTML for making webpages
2 To expose students to CSS for formatting web pages
3 To expose students to developing responsive layout
4 To expose students to JavaScript to make web pages interactive
5 To orient students to React for developing front end applications
6 To orient students to Node.js for developing backend applications

Lab Outcomes:

Sr. No. Course Outcomes Cognitive levels of
attainment as per
Bloom’s Taxonomy
On successful completion, of course, learner/student will be able to:
1 Identify and apply the appropriate HTML tags to develop a webpage. L1, L2,L3,L4
2 Identify and apply the appropriate CSS tags to format data on
webpage L1, L2,L3,L4
3 Construct responsive websites using Bootstrap L1, L2,L3,L4,L5,L6
4 Use JavaScript to develop interactive web pages. L1, L2,L3,L4,L5,L6
5 Construct front end applications using React L1, L2,L3,L4,L5,L6
6 Construct back end applications using Node.js/Express L1, L2,L3,L4,L5,L6

Prerequisite: Knowledge of J ava programming and object -oriented programming.

Page 27

Hardware & Software Requirements:

Hardware Requirement:

PC i3 processor and above Software requirement:
Google Chrome Browser (latest), Java 8 or above,
NodeJS, React. Internet Connection

DETAILED SYLLABUS:

Textbooks:

1. HTML 5 Black Book (Covers CSS3, JavaScript, XML, XHTML, AJAX, PHP, jQuery) 2Ed., DT
Editorial Services
2. Learning React Functional Web Development with React and Redux, Alex Banks and Eve Porcello,
O’Reilly
3. Learning Node.js Development, Andrew Mead, Packt Publishing

References:
1. https://www.tutorialspoint.com/
2. https://reactjs.org/tutorial/tutorial.html
3. https://nodejs.dev/learn
4. https://www.youtube.com/ watch?v= -27HAh8c0YU

Term Work: Term Work shall consist of at least 12 to 15 practicals based on the above list. Also Term
work Journal must include at least 2 assignments.

Term Work Marks: 25 Marks (Total marks) = 15 Marks (Experiment) + 5 Marks (Assi gnments) + 5 Marks
(Attendance)
Sr.
No. Module Detailed Content Hours LO
Mapping
I HTML5 Elements, Attributes, Head, Body, Hyperlink, Formatting,
Images, Tables, List, Frames, Forms, Multimedia 02 LO1
II CSS3 Syntax, Inclusion, Color, Background, Fonts, Tables,
lists,CSS3 selectors, Pseudo classes, Pseudo elements 02 LO2
III Bootstrap Grid system, Forms, Button, Navbar, Breadcrumb,
Jumbotron 02 LO3
IV JavaScript Variables, Operators, Conditions, Loops, Functions,
Events, Classes and Objects, Error handling, Validations,
Arrays, String, Date 05 LO4
V React Installation and Configuration. JSX, Components, Props,
State, Forms, Events, Routers, Refs, Keys. 08 LO5
VI Node.js Installation and Configuration, Callbacks, Event loops,
Creating express app. 07 LO6

Page 28

Practical & Oral Exam: An Practical & Oral exam will be held based on the above syllabus.

Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Theory Practical Total
ITL502 Security
Lab -- 02 -- 01 01

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract
/ Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test 2 Avg.
ITL502 Security Lab -- -- -- -- -- 25 25 50

Lab Objectives:
Sr.
No. Lab Objectives
The Lab experiments aims:
1 To apply the knowledge of symmetric cryptography to implement classical ciphers.
2 To analyze and implement public key encryption algorithms, hashing and digital signature
algorithms.
3 To explore the different network reconnaissance tools to gather information about networks.
4 To explore the tools like sniffers, port scanners and other related tools for analyzing.
5 To Scan the network for vulnerabilities and simulate attacks.
6 To set up intrusion detection systems using open -source technologies
and to explore email security.

Lab Outcomes :

Sr. No. Lab Outcomes Cognitive levels of
attainment as per
Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Illustrate symmetric cryptography by implementing classical ciphers. L1,L2
2 Demonstrate Key management, distribution and user authentication. L1,L2
3 Explore the different network reconnaissance tools to gather
information about networks L1,L2, L3
4 Use tools like sniffers, port scanners and other related tools for
analyzing packets in a network. L1,L2, L3
5 Use open -source tools to scan the network for vulnerabilities and
simulate attacks. L1,L2, L3
6 Demonstrate the network security system using open source tools. L1,L2

Page 29

Prerequisite: Basic concepts of Computer Networks & Network Design, Operating System

Hardware & Software Requirements:

Hardware Requirement:

PC With following Configuration
1. Intel Core i3/i5/i7 Processor
2. 4 GB RAM
3. 500 GB Harddisk Software requirement:
1. Windows or Linux Desktop OS
2. wireshark
3. ARPWATCH
4. Kismet, NetStumbler
5. NESSU

DETAILED SYLLABUS:

Sr. No. Detailed Content Hours LO
Mapping
I Classical Encryption techniques (mono -alphabetic and poly -
alphabetic substitution techniques: Vigenere cipher, playfair
cipher) 04 LO1
II 1)Block cipher modes of operation using a)Data Encryption
Standard b)Advanced Encryption Standard (AES).
2)Public key cryptography: RSA algorithm.
3)Hashing Techniques: HMAC using SHA
4)Digital Signature Schemes – RSA, DSS. 06 LO2
III 1) Study the use of network reconnaissance tools like WHOIS,
dig, traceroute, nslookup to gather information about networks
and domain registrars.
2) Study of packet sniffer tools Wireshark, : - a. Observer
performance in promiscuous as well as non -promiscuous mode.
b. Show the packets can be traced based on different filters. 04 LO3
IV 1) Download and install nmap.
2) Use it with different options to scan open ports, perform OS
fingerprinting, ping scan, tcp port scan, udp port scan, etc. 04 LO4
V a) Keylogger attack using a keylogger tool.
b) Simulate DOS attack using Hping or other tools
c) Use the NESSUS/ISO Kali Linux tool to scan the network for
vulnerabilities. 04 LO5
VI 1) Set up IPSec under Linux.
2) Set up Snort and study the logs.
3) Explore the GPG tool to implement email security 04 LO6

Text Books

1 Build your own Security Lab, Michael Gregg, Wiley India.
2 CCNA Security, Study Guide, TIm Boyles, Sybex.
3 Hands -On Information Security Lab Manual, 4th edition, Andrew Green, Michael Whitman,

Page 30

Herbert Mattord.
4 The Network Security Test Lab: A Step -by-Step Guide Kindle Edition, Michael Gregg.

References:

1 Network Security Bible, Eric Cole, Wiley India.
2 Network Defense and Countermeasures, William (Chuck) Easttom.
3 Principles of Information Security + Hands -on Information Security Lab Manual, 4th Ed. , Michael
E. Whitman , Herbert J. Mattord.
4 IITB virtual Lab: http://cse29 -iiith.vlabs.ac.in/
5
https://www.dcode.fr/en

Sr.No Experiment Title
1. Breaking the Mono -alphabetic Substitution Cipher using
Frequency analysis method.
2. Design and Implement a product cipher using Substitution ciphers.
3. Cryptanalysis or decoding Playfair, vigenere cipher.
4. Encrypt long messages using various modes of operation using
AES or DES.
5. Cryptographic Hash Functions and Applications (HMAC): to
understand the need, design and a pplications of collision resistant
hash functions.
6. Implementation and analysis of RSA cryptosystem and Digital
signature scheme using RSA.
7. Study the use of network reconnaissance tools like WHOIS, dig,
traceroute, nslookup to gather information about networks and
domain registrars.
8. Study of packet sniffer tools wireshark: - a. Observer performance
in promiscuous as well as non -promiscuous mode. b. Show the
packets can be traced based on different filters.
9. Download, install nmap and use it with different options to scan
open ports, perform OS fingerprinting, ping scan, tcp port scan,
udp port scan, etc.
10. Study of malicious software using different tools:
a) Keylogger attack using a keylogger tool.
b) Simulate DOS attack using Hping or other tools
c) Use the NESSUS/ISO Kali Linux tool to scan the network for
vulnerabilities.
11. Study of Network security by
a) Set up IPSec under Linux.
b) Set up Snort and study the logs.
c) Explore the GPG tool to implement email security

Term Work: Term Work shall consist of at least 12 to 15 practicals based on the above list. Also Term
work Journal must include at least 2 assignments.

Term Work Marks: 25 Marks (Total marks) = 15 Marks (Experiment) + 5 Marks (Assignments) + 5 Marks
(Attendance)

Practical & Oral Exam: An Practical & Oral exam will be held based on the above syllabus.

Page 31

Course
Code Course Name Teaching
Scheme
(Contact Hours) Credits A ssigned
Theory Practical Theory Practical Total
ITL503 DevOPs Lab -- 02 -- 01 01

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract
/ Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test 2 Avg.
ITL503 DevOPs Lab -- -- -- -- -- 25 25 50

Lab Objectives:

Sr.
No. Lab Objectives
The Lab experiments aims:
1 To understand DevOps practices which aims to simplify Software Development Life Cycle
2 To be aware of different Version Control tools like GIT, CVS or Mercurial
3 To Integrate and deploy tools like Jenkins and Maven, which is used to build, test and deploy
applications in DevOps environment
4 To be familiarized with selenium tool, which is used for continuous testing of applications de ployed.
5 To use Docker to Build, ship and manage applications using containerization
6 To understand the concept of Infrastructure as a code and install and configure Ansible tool.

Lab Outcomes:

Sr.
No. Lab Outcomes Cognitive
levels of
attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 To understand the fundamentals of DevOps engineering and be fully proficient
with DevOps terminologies, concepts, benefits, and deployment options to meet
your business requirements L1,L2
2 To obtain complete knowledge of the “version control system” to effectively track
changes augmented with Git and GitHub L1,L2
3 To understand the importance of Jenkins to Build and deploy Software
Applications on server environment L1,L2
4 Understand the importance of Selenium and Jenkins to test Software Applications L1,L2

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5 To understand concept of containerization and Analyze the Containerization of
OS images and deployment of applications over Docker L1,L2,L3
6 To Synthesize software configuration and provisioning using Ansible. L1,L2,L3

Prerequisite : Operating System, Linux Administration, Java /Web Application Programming, and Software
Engineering.

Hardware & Software Requirements:

Hardware Requirements Software Requirements Other Requirements
PC With following Configuration
1. Intel i3 core or above
2. 4 GB RAM or above
3. 500 GB HDD
4. Network interface card 1. Linux / Windows Operating
system
2. VIRTUAL BOX/ VMWARE
1. Internet Connection for installing
additional packages
2. GitHub account
3. Docker hub account

DETAILED SYLLABUS:
Sr.
No. Module Detailed Content Hours LO
Mapping
0 Prerequisite Knowledge of Linux Operating system, installation and
configuration of services and command line basics,
Basics of Computer Networks and Software
Development Life cycle. 00 LO1
I Introduction to
Devops Understanding of the process to be followed during the
development of an application, from the inception of an
idea to its final deployment. Learn about the concept of
DevOps and the practices and principles followed to
implement it in any company’s softwa re development life
cycle.
Learn about the phases of Software Lifecycle. Get
familiar with the concept of Minimum Viable Product
(MVP) & Cross -functional Teams. Understand why
DevOps evolved as a prominent culture in most of the
modern -day startups to ach ieve agility in the software
development process
Self-Learning Topics: Scrum, Kanban, Agile 04 LO1
II Version Control In this module you will learn:
 GIT Installation, Version Control, Working with
remote repository
 GIT Cheat sheet
 Create and fork repositories in GitHub
 Apply branching, merging and rebasing
concepts.
 Implement different Git workflow strategies in
real-time scenarios
 Understand Git operations in IDE

Self-Learning Topics: AWS Codecommit, Mercurial,
Subversion, Bitbucket, CVS 04 LO1 &
LO2
III Continuous
Integration
using Jenkins In this module, you will know how to perform Continuous
Integration using Jenkins by building and automating test
cases using Maven / Gradle / Ant.
 Introduction to Jenkins (With Architecture)
 Introduction to Maven / Gradle / Ant. 04 LO1 &
LO3

Page 33

 Jenkins Management Adding a slave node to
Jenkins
 Build the pipeline of jobs using Maven / Gradle /
Ant in Jenkins, create a pipeline script to deploy
an application over the tomcat server
Self-Learning Topics: Travis CI, Bamboo,
GitLab, AWS CodePipeline
IV Continuous
Testing with
Selenium In this module, you will learn about selenium and how to
automate your test cases for testing web elements. You
will also get introduced to X -Path, TestNG and in tegrate
Selenium with Jenkins and Maven.
 Introduction to Selenium
 Installing Selenium
 Creating Test Cases in Selenium WebDriver
 Run Selenium Tests in Jenkins Using Maven

Self-Learning Topics: Junit, Cucumber 04 LO1 , LO3
& LO4
V Continuous
Deployment:
Containerizatio
n with Docker In this module, you will be introduced to the core
concepts and technology behind Docker. Learn in detail
about container and various operations performed on it.
 Introduction to Docker Architecture and
Container Life Cycle
 Unde rstanding images and containers
 Create and Implement docker images using
Dockerfile.
 Container Lifecycle and working with
containers.
 To Build, deploy and manage web or software
application on Docker Engine.
 Publishing image on Docker Hub.

Self-Learning Topics: Docker Compose, Docker
Swarm. 05 LO1 &
LO5
VI Continuous
Deployment:
Configuration
Management
with Puppet In this module, you will learn to Build and operate a
scalable automation system.
 Puppet Architecture
 Puppet Master Slave Communication
 Puppet Blocks
 Installation and Configuring Puppet Master and
Agent on Linux machines
 Use exported resources and forge modules to set
up Puppet modules
 Create efficient manifests to streamline your
deployments

Self-Learning Topics: Ansible, Saltstack 05 LO1 &
LO6

Text books
1. DevOps Bootcamp, Sybgen Learning
2. Karl Matthias & Sean P. Kane, Docker: Up and Running, O'Reilly Publication.
3. Len Bass,Ingo Weber,Liming Zhu,”DevOps, A Software Architects Perspective”, AddisonWesley -
Pearson Publication.
4. John Ferguson Smart,” Jenkins, The Definitive Guide”, O'Reilly Publication.
5. Mastering Puppet 5: Optimize enterprise -grade environment performance with Puppet, by Ryan Russell -

Page 34

Yates Packt Publishing (September 29, 2018)

References:
1. Sanjeev Sharma an d Bernie Coyne,” DevOps for Dummies”, Wiley Publication
2. Httermann, Michael, “DevOps for Developers”, Apress Publication.
3. Joakim Verona, “Practical DevOps”, Pack publication
4. Puppet 5 Essentials - Third Edition: A fast -paced guide to automating your infrastructure by Martin
Alfke Packt Publishing; 3rd Revised edition (September 13, 2017)

List of Experiments:

Sr.No Experiment Title
1. To understand DevOps: Principles, Practices, and DevOps
Engineer Role and Responsibilities.
2. To understand Version Control System / Source Code
Management, install git and create a GitHub account.
3. To Perform various GIT operations on local and Remote
repositories using GIT Cheat -Sheet
4. To understand Continuous Integration, install and configure
Jenkins with Maven/Ant/Gradle to setup a build Job.
5. To Build the pipeline of jobs using Maven / Gradle / Ant in
Jenkins, create a pipeline script to Test and deploy an application
over the tomcat server.
6. To understand Jenkins Master -Slave Architecture and scale your
Jenkins standalone implementation by implementing slave nodes.
7. To Setup and Run Selenium Tests in Jenkins Using Maven.
8. To understand Docker Architecture and Container Life Cycle,
install Docker and execute docker commands to manage images
and interact with containers.
9. To learn Dockerfile instructions, build an image for a sample web
application using Dockerfile.
10. To install and Configure Pull based Software Configuration
Management and provisioning tools using Puppet.
11. To learn Software Configuration Management and provisioning
using Puppet Blocks(Manifest, Modules, Classes, Function)
12 To provision a LAMP/MEAN Stack using Puppet Manifest.

Term Work: Term Work shall consist of at least 12 to 15 practicals based on the above list. Also Term
work Journal must include at least 2 assignments, one of which must include a Case study on DevOps
Implementation in real world and the other one can be based on the self -learning topics mentioned in
syllabus.

Term Work Marks: 25 Marks (Total marks) = 15 Marks (Experiment) + 5 Marks (Assignments) + 5 Marks
(Attendance)

Practical & Oral Exam: An Practical & Oral exam will be held based on the above syllab us.

.

Page 35

Course
Code Course Name Teaching
Scheme
(Contact Hours) Credits Assigned
Theory Practical Theory Practical Total
ITL504 Advance DevOps Lab -- 02 -- 01 01

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract /
Oral Total Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test 2 Avg.
ITL504 Advance DevOps
Lab -- -- -- -- -- 25 25 50

Lab Objectives:

Sr.
No. Lab Objectives
The Lab experiments aims:
1 To understand DevOps practices and cloud native environments to achieve continuous software
delivery pipelines and automated operations that address the gap between IT resources and growing
cloud complexity.
2 To Use Kubernetes services to structure N -tier applications.
3 To be familiarized wi th Infrastructure as code for provisioning, compliance, and management of
any cloud infrastructure, and service.
4 To understand that security and speed in software development are not inversely -related objectives
Internalizing the contribution of tools a nd automation in DevSecOps
5 To understand various troubleshooting techniques by monitoring your entire infrastructure and
business processes
6 To understand how software and software -defined hardware are provisioned dynamically.

Lab Outcomes :

Sr. No. Lab Outcomes Cognitive levels of
attainment as per
Bloom’s Taxonomy
On successful completion, of course, learner/student will be able to:
1 To understand the fundamentals of Cloud Computing and be fully
proficient with Cloud based DevOps solution deployment options to
meet your business requirements L1,L2
2 To deploy single and multiple container applications and manage
application deployments with rollouts in Kubernetes L1,L2,L3
3 To apply best practices for managing infrastructure as code
environments and use terraform to define and deploy cloud L1,L2,L3

Page 36

infrastructure.
4 To identify and remediate application vulnerabilities earlier and help
integrate security in the development process using SAST
Techniques. L1,L2,L3
5 To use Continuous Monitoring Tools to resolve any system errors
(low memory, unreachable server etc.) before they have any negative
impact on the business productivity L1,L2,L3
6 To engineer a composition of nano services using AWS Lambda and
Step Functions with the Serverless Framework L1,L2,L3

Prerequisite : Operating System, Linux Administration, Java /Web Application Programming,
Software Engineering, Cloud Computing and DevOps Ecosystem.

Hardware & Software Requirements:

Hardware Requirements Software Requirements Other Requirements
PC With following
Configuration
1. Intel i3 core or above
2. 4 GB RAM or above
3. 500 GB HDD
4. Network interface card 1. Linux / Windows Operating
system
2. VIRTUAL BOX/ VMWARE
1. Internet Connection for installing
additional packages
2. GitHub account
3. AWS free tier account

DETAILED SYLLABUS:

Sr. No. Module Detailed Content Hour
s LO
Mapping
0 Prerequisite Knowledge of Linux Operating system, installation
and configuration of services and command line
basics, Basics of Computer Networks, Software
Development Life cycle, Cloud Computing and
DevOps Ecosystem. 02 --
I Introduction to
Devops on
Cloud Learn about various cloud services and service
providers, also get the brief idea of how to implement
DevOps over Cloud Platforms.
 Introduction to high availability architecture
and auto -scaling
 Set up the DevOps infrastructure on the cloud
 Work and set up IDE on Cloud9
 Deploy projects on AWS using Code Build,
CodeDeploy, and CodePipeline
Self-Learning Topics: AWS Codestar 04 LO1
II Container
Orchestration
using
Kubernetes
In this module, you will learn how
Kubernetes automates many of the manual
processes involved in deploying,
managing, and scaling containerized
applications.
 Install and configure Kubernetes
 Spin Up a Kubernetes Cluster
 Check the Nodes of Your Kubernetes Cluster 04 LO1, LO2

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 Installing kubectl to manage cluster and deploy Your
First Kubernetes Application
Self-Learning Topics:
 Using Services and Ingresses to Expose
Deployments
 Perform logging, monitoring, services, and
volumes in Kubernetes.

III Infrastructure
Automation with
Terraform
In this module you will learn, Infrastructure as code
for provisioning, compliance, and management of any
cloud infrastructure, and service.
 Introduction to Infrastructure as Code
with Terraform
 Install, Build, change and Destroy
Infrastructure using Terraform.
Self-Learning Topics:
Terraform
 Create Resource Dependencies
 Provision Infrastructure
 Define Input Variables, Query Data with
output and store remote state 04 LO1, LO3
IV DevSecOps:
Static
Application
Security Testing
(SAST)
In this module, you will learn to identify
and remediate application vulnerabilities
earlier and help integrate security in the
development process using tools like
SonarQube / Gitlab /
 Perform static analysis on application source
code and binaries.
 Spot potential vulnerabilities before
deployment
 Analysis of java / web -based project
 Jenkins SonarQube / Gitlab Integration
Self-Learning Topics: Snyk, OWASP ZAP,
Analysis Core Plugin 04 LO1, LO4
V DevSecOps:
Continuous
Monitoring In this module, you will learn to detect,
report, respond to the attacks and issues
which occur within the infrastructure.
 Introduction to Continuous Monitoring
 Introduction to Nagios
 Installing Nagios
 Nagios Plugins (NRPE) and Objects Nagios
Commands and Notification
 Monitoring of different servers using Nagios
04 LO1, LO5

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Self-Learning Topics: Splunk, Snort, Tenable
VI NoOps:
Serverless
Computing In this module, you will learn serverless
computing platform like AWS Lambda,
which allows you to build your code and
deploy it without ever needing to configure
or manage underlying servers.
 AWS Lambda - Overview and Environment
Setup
 Building and Configuring the Lambda
function (NODEJS/PYTHON/JAVA)
 Creating & Deploying using AWS
Console/CLI
 Creating & Deploying using Serverless
Framework
Self-Learning Topics: AWS Lambda
 Create a REST API with the Serverless
Framework 04 LO1, LO6

Textbooks:
1. AWS Certified SysOps Administrator Official Study Guide: Associate Exam
by Stephen Cole (Author), Gareth Digby (Author), Chris Fitch (Author), Steve
Friedberg (Author), Shaun Qual
2. AWS Certified Solutions A rchitect Official Study Guide: Associate Exam by Joe
Baron
3. Terraform: Up & Running - Writing Infrastructure as Code, Second Edition
by Yevgeniy Brikman , O'Reilly
4. Kubernetes: Up and Running - Dive into the Future of Infrastructure, Second
Editionby Brendan Burns ,O'Reilly
5. Going Serverless with AWS Lambda: Leveraging the latest services from the AWS
cloud by Ajay Pherwani , Shroff/X -Team;
6. Learning Nagios, Packt Publishing.

References:
1. Learning Aws - Second Edition: Design, build, and deploy responsive applications using
AWS by Amit Shah Aurobindo Sarkar
2. Mastering Aws Lambda by Yohan Wadia Udita Gupta
List of Experiments:

Sr.
No Experiment Title
1 To understand the benefits of Cloud Infrastructure and Setup AWS Cloud9 IDE, Launch AWS
Cloud9 IDE and Perform Collaboration Demonstration .
2 To Build Your Application using AWS CodeBuild and Deploy on S3 / SEBS using AWS
CodePipeline, deplo y Sample Application on EC2 instance using AWS CodeDeploy.
3 To understand the Kubernetes Cluster Architecture, install and Spin Up a Kubernetes Cluster on
Linux Machines/Cloud Platforms.
4 To install Kubectl and execute Kubectl commands to manage the Ku bernetes cluster and deploy
Your First Kubernetes Application.

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5 To understand terraform lifecycle, core concepts/terminologies and install it on a Linux Machine.
6 To Build, change, and destroy AWS / GCP /Microsoft Azure/ DigitalOcean infrastructure Using
Terraform.
7 To understand Static Analysis SAST process and learn to integrate Jenkins SAST to
SonarQube/GitLab.
8 Create a Jenkins CICD Pipeline with SonarQube / GitLab Integration to perform a static analysis
of the code to detect bugs, code smel ls, and security vulnerabilities on a sample Web / Java /
Python application.
9 To Understand Continuous monitoring and Installation and configuration of Nagios Core,
Nagios Plugins and NRPE (Nagios Remote Plugin Executor) on Linux Machine.
10 To perform Port, Service monitoring, Windows/Linux server monitoring using Nagios.
11 To understand AWS Lambda, its workflow, various functions and create your first Lambda
functions using Python / Java / Nodejs.
12 To create a Lambda function which will log “An Image has been added” once you add an
object to a specific bucket in S3.

Term Work: Term Work shall consist of at least 12 to 15 practicals based on the above list. Also Term
work Journal must include at least 2 assignments based on the self -learning topics mentioned in
syllabus.

Term Work Marks: 25 Marks (Total marks) = 15 Marks (Experiment) + 5 Marks (Assignments) + 5 Marks
(Attendance)

Practical & Oral Exam: An Practical & Oral exam will be held based on the above syllabus.

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Course
Code Course Name Teaching scheme Credit assigned
ITL505 Professional
Communication &
Ethics -II (PCE -II) Theory Pract. Tut. Theory Pract. Tut. Total
-- 2 ⃰ + 2
Hours
(Batch -
wise) -- -- 02 -- 02
*Theory class to be conducted for full class.

Course Code Course Name Credits
ITL505 Professional Communication & Ethics -II (PCE -II) 02
Course Rationale This curriculum is designed to build up a professional and ethical approach, effective oral
and written communication with enhanced soft skills. Through practical sessions, it
augments student's interactive competence and confidence to respond appropriately and
creatively to the implied challenges of the global Industrial and Corporate requirements. It
further inculcates the social responsi bility of engineers as technical citizens.
Course Objectives  To discern and develop an effective style of writing important technical/business
documents.
 To investigate possible resources and plan a successful job campaign.
 To understand the dynamics of professional communication in the form of group
discussions, meetings, etc. required for career enhancement.
 To develop creative and impactful presentation skills.
 To analyze personal traits, interests, values, aptitudes and skills.
 To understand the impor tance of integrity and develop a personal code of ethics.
Course Outcomes Learner will be able to…
 plan and prepare effective business/ technical documents which will in turn
provide solid foundation for their future managerial roles.
 strategize their personal and professional skills to build a professional image
and meet the demands of the industry.
 emerge successful in group discussions, meetings and result -oriented agreeable
solutions in group communication situations.
 deliver persuasive and professional presentations.
 develop creative thinking and interpersonal skills required for effective professional
communication.
 apply codes of ethical conduct, personal integrity and norms of organizational
behavi our.

Course
Code Course Name Examination Scheme
Theory
Term
work Pract Oral Internal
Oral Total Internal Assessment End
sem Duration
(hrs) Test
1 Test
2 Avg
.
ITL505 Professional
Communicati
on & Ethics -II
(PCE -II) -- -- -- -- -- 25 -- -- 25 50

Page 41

Module Contents Hours
1 ADVANCED TECHNICAL WRITING :PROJECT/PROBLEM
BASED LEARNING (PBL)

1.1 Purpose and Classification of Reports:
Classification on the basis of:
 Subject Matter (Technology, Accounting, Finance, Marketing, etc.)
 Time Interval (Periodic, One -time, Special)
 Function (Informational, Analytical, etc.)
 Physical Factors (Memorandum, Letter, Short & Long)
1.2. Parts of a Long Formal Report:
 Prefatory Parts (Front Matter)
 Report Proper (Main Body)
 Appended Parts (Back Matter)
1.3. Language and Style of Reports
 Tense, Person & Voice of Reports
 Numbering Style of Chapters, Sections, Figures, Tables and
Equations
 Referencing Styles in APA & MLA Format
 Proofreading through Plagiarism Checkers
1.4. Definition, Purpose & Types of Proposals
 Solicited (in conformance with RFP) & Unsolicited Proposals
 Types (Short and Long proposals)
1.5. Parts of a Proposal
 Elements
 Scope and Limitations
 Conclusion
1.6. Technical Paper Writing
 Parts of a Technical Paper (Abstract, Introduction,
Research Methods, Findings and Analysis, Discussion, Limitations,
Future Scope and References)
 Language and Formatting
 Referencing in IEEE Format 06
2 EMPLOYMENT SKILLS
2.1. Cover Letter & Resume
 Parts and Content of a Cover Letter
 Difference between Bio-data, Resume & CV
 Essential Parts of a Resume
 Types of Resume (Chronological, Functional & Combination)
2.2 Statement of Purpose
 Importance of SOP
 Tips for Writing an Effective SOP
2.3 Verbal Aptitude Test
 Modelled on CAT, GRE, GMAT exams
2.4. Group Discussions
 Purpose of a GD
 Parameters of Evaluating a GD
 Types of GDs (Normal, Case -based & Role Plays) 06

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 GD Etiquettes
2.5. Personal Interviews
 Planning and Preparation
 Types of Questions
 Types of Interviews (Structured, Stress, Behavioural, Problem
Solving & Case -based)
 Modes of Interviews: Face -to-face (One -to one and Panel)
Telephonic, Virtual
3 BUSINESS MEETINGS
1.1. Conducting Business Meetings
 Types of Meetings
 Roles and Responsibilities of Chairperson, Secretary and Members
 Meeting Etiquette
3.2. Documentation
 Notice
 Agenda
 Minutes 02
4 TECHNICAL/ BUSINESS PRESENTATIONS
1.1 Effective Presentation Strategies
 Defining Purpose
 Analyzing Audience, Location and Event
 Gathering, Selecting &Arranging Material
 Structuring a Presentation
 Making Effective Slides
 Types of Presentations Aids
 Closing a Presentation
 Platform skills
1.2 Group Presentations
 Sharing Responsibility in a Team
 Building the contents and visuals together
 Transition Phases 02
5 INTERPERSONAL SKILLS
1.1. Interpersonal Skills
 Emotional Intelligence
 Leadership & Motivation
 Conflict Management & Negotiation
 Time Management
 Assertiveness
 Decision Making
5.2 Start -up Skills
 Financial Literacy
 Risk Assessment
 Data Analysis (e.g. Consumer Behaviour, Market Trends, etc.) 08
6 CORPORATE ETHICS
6.1Intellectual Property Rights
 Copyrights
 Trademarks
 Patents
 Industrial Designs 02

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 Geographical Indications
 Integrated Circuits
 Trade Secrets (Undisclosed Information)
6.2 Case Studies
 Cases related to Business/ Corporate Ethics

List of assignments:
(In the form of Short Notes, Questionnaire/ MCQ Test, Role Play, Case Study, Quiz, etc.)
1. Cover Letter and Resume
2. Short Proposal
3. Meeting Documentation
4. Writing a Technical Paper/ Analyzing a Published Technical Paper
5. Writing a SOP
6. IPR
7. Interpersonal Skills
8. Aptitude test (Verbal Ability)
Note:
1. The Main Body of the project/book report should contain minimum 25 pages (excluding Front and
Back matter).
2. The group size for the final report presentation should not be less than 5 students or exceed 7 students.
3. There will be an end –semester presentation based on the book report.

Assessment :

Term Work :
Term work shall consist of minimum 8 experiments.
The distribution of marks for term work shall be as follows:
Assignment : 10 Marks
Attendance : 5 Marks
Presentation slides : 5 Marks
Book Report (hard copy) : 5 Marks
The final certification and acceptance of term work ensures the satisfactory performance o f laboratory work
and minimum passing in the term work.

Internal oral:
Oral Examination will be based on a GD & the Project/Book Report presentation.
Group Discussion : 10 marks
Project Presentation : 10 Marks
Group Dynamics : 5 Marks

Books R ecommended:
Textbooks and Reference books:
1. Arms, V. M. (2005). Humanities for the engineering curriculum: With selected chapters from
Olsen/Huckin: Technical writing and professional communication, second edition . Boston, MA: McGraw -
Hill.
2. Bovée, C. L., & Thill, J. V. (2021). Business communication today . Upper Saddle River, NJ: Pearson.
3. Butterfield, J. (2017). Verbal communication: Soft skills for a digital workplace . Boston, MA: Cengage
Learning.
4. Masters, L. A., Wallace, H. R., & Harwood, L. (2011). Personal development for life and work . Mason:
South -Western Cengage Learning.
5. Robbins, S. P., Judge, T. A., & Campbell, T. T. (2017). Organizational behaviour . Harlow, England:

Page 44

Pearson.
6. Meenakshi Raman, Sangeeta Sharma (2004) Technical Communication, Prin ciples and Practice. Oxford
University Press
7. Archana Ram (2018) Place Mentor, Tests of Aptitude For Placement Readiness. Oxford University Press
Sanjay Kumar &PushpLata (2018). Communication Skills a workbook, New Delhi: Oxford University
Press.

Page 45

Course Code
Course
Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ITM5 01 Mini Project
– 2 A Web
Based
Business
Model -- 04 -- -- 02 -- 02

Course
Code
Course
Name Examination Scheme
Theory Marks
Term Work Pract. /Oral Total Internal assessment End
Sem.
Exam Test1 Test 2 Avg.
ITM5 01 Mini Project
– 2 A Web
Based
Business
Model -- -- -- -- 25 25 50

Course Objectives
1. To acquaint with the process of identifying the needs and converting it into the problem.
2. To familiarize the process of solving the problem in a group.
3. To acquaint with the process of applying basic engineering fundamentals to attempt solutions to the
problem s.
4. To inculcate the process of self -learning and research.
Course Outcome: Learner will be able to…
1. Identify problems based on societal /research needs.
2. Apply Knowledge and skill to solve societal problems in a group.
3. Develop interpersonal skills to wor k as member of a group or leader.
4. Draw the proper inferences from available results through theoretical/ experimental/simulations.
5. Analyse the impact of solutions in societal and environmental context for sustainable development.
6. Use standard norms of eng ineering practices
7. Excel in written and oral communication.
8. Demonstrate capabilities of self -learning in a group, which leads to life long learning.
9. Demonstrate project management principles during project work.

Guidelines for Mini Project
 Students sha ll form a group of 3 to 4 students, while forming a group shall not be allowed less than
three or more than four students, as it is a group activity.
 Students should do survey and identify needs, which shall be converted into problem statement for
mini pro ject in consultation with faculty supervisor/head of department/internal committee of faculties.
 Students hall submit implementation plan in the form of Gantt/PERT/CPM chart, which will cover
weekly activity of mini project.
 A log book to be prepared by each group, wherein group can record weekly work progress,
guide/supervisor can verify and record notes/comments.
 Faculty supervisor may give inputs to students during mini project activity;however, focus shall be on
self-learning.

Page 46

 Students in a group shall understand problem effectively, propose multiple solution and select best
possible solution in consultation with guide/ supervisor.
 Students shall convert the best solution into working model using various components of t heir domain
areas and demonstrate.
 The solution to be validated with proper justification and report to be compiled in standard format of
University of Mumbai.
 With the focus on the self -learning, innovation, addressing societal problems and entrepreneurs hip
quality development within the students through the Mini Projects, it is preferable that a single project
of appropriate level and quality to be carried out in two semesters by all the groups of the students. i.e.
Mini Project 1 in semester III and IV. Similarly, Mini Project 2 in semesters V and VI.
 However, based on the individual students or group capability, with the mentor’s recommendations, if
the proposed Mini Project adhering to the qualitative aspects mentioned above gets completed in odd
seme ster, then that group can be allowed to work on the extension of the Mini Project with suitable
improvements/modifications or a completely new project idea in even semester. This policy can be
adopted on case by case basis.
Guidelines for Assessment of Min i Project:
Term Work
 The review/ progress monitoring committee shall be constituted by head of departments of each
institute. The progress of mini project to be evaluated on continuous basis, minimum two reviews
in each semester.
 In continuous assessment f ocus 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 for both semesters shall be as below;
o Marks awarded by guide/supe rvisor based on log book : 10
o Marks awarded by review committee : 10
o Quality of Project report : 05

Review/progress monitoring committee may consider following points for assessment based on either
one year or half year project as mentioned in general guidelines.
One-year project:
 In first semester entire theoretical solution shall be ready, including components/system selection
and cost analysis. Two reviews will be conducted based on presentation given by students group.
 First shall be for finalisation of problem
 Second shall be on finalisation of proposed solution of problem.
 In second semester expected work shall be procurement of component’s/systems, building of
working prototype, testing and validation of results bas ed on work completed in an earlier
semester.
 First review is based on readiness of building working prototype to be conducted.
 Second review shall be based on poster presentation cum demonstration of working
model in last month of the said semester.

Half -year project:
 In this case in one semester students’ group shall complete project in all aspects including,
o Identification of need/problem
o Proposed final solution
o Procurement of components/systems
o Building prototype and testing
 Two reviews will be conducted for continuous assessment,
 First shall be for finalisation of problem and proposed solution
 Second shall be for implementation and testing of solution.

Assessment criteria of Mini Project.

Page 47

Mini Project shall be assessed based on following crite ria;
1. Quality of survey/ need identification
2. Clarity of Problem definition based on need.
3. Innovativeness in solutions
4. Feasibility of proposed problem solutions and selection of best solution
5. Cost effectiveness
6. Societal impact
7. Innovativeness
8. Cost effect iveness and Societal impact
9. Full functioning of working model as per stated requirements
10. Effective use of skill sets
11. Effective use of standard engineering norms
12. Contribution of an individual’s as member or leader
13. Clarity in written and oral communication

 In one year, project , first semester evaluation may be based on first six criteria’s and remaining
may be used for second semester evaluation of performance of students in mini project.
 In case of half year project all criteria’s in generic may be consider ed for evaluation of
performance of students in mini project.
Guidelines for Assessment of Mini Project Practical/Oral Examination:
 Report should be prepared as per the guidelines issued by the University of Mumbai.
 Mini Project shall be assessed throu gh a presentation and demonstration of working model by the
student project group to a panel of Internal and External Examiners preferably from industry or
research organisations having experience of more than five years approved by head of Institution.
 Students shall be motivated to publish a paper based on the work in Conferences/students competitions.

Mini Project shall be assessed based on following points;
1. Quality of problem and Clarity
2. Innovativeness in solutions
3. Cost effectiveness and Societal impa ct
4. Full functioning of working model as per stated requirements
5. Effective use of skill sets
6. Effective use of standard engineering norms
7. Contribution of an individual’s as member or leader
8. Clarity in written and oral communication

Page 48

Course Code Course Name Teaching Scheme
(Contact Hours)
Credits Assigned
Theory Practical Theory Practical Total
ITDO5011 Microcontroller
Embedded
Programming 03 -- 03 -- 03

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract/
Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test 2 Avg.
ITDO5011 Microcontroller
Embedded
Programming 20 20 20 80 3 -- -- 100

Course Objectives:

Course Outcomes:
Sr. No. Course Outcomes Cognitive
levels of
attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Introduce and discuss the embedded system concepts, architecture of
embedded systems and understand the embedded development environments L1, L2
2 Describe the architecture of 8051 microcontroller and write embedded
programs for 8051Microcontroller L2, L3
3 Illustrate the interfacing of peripherals with 8051 microcontroller and write
programs L2, L3
4 Understand and apply the concepts of ARM architecture L2, L3
5 Explain and Demonstrate the open source RTOS L3
6 Select the embedded platform and program it for real time application L3, L4
Sr. No. Course Objectives
The course aims:
1 Conceptualize the architecture of embedded systems.
2 Study the basics of microcontroller 8051.
3 Elaborate on the concepts of microcontroller interfacing.
4 Understand the concepts of ARM architecture
5 Study the concepts of real -time operating system
6 Learn about various embedded platforms and their programming

Page 49

Prerequisite: Computer Organization and Architecture, Operating System.

DETAILED SYLLABUS:
Sr.
No. Module Detailed Content Hours CO
Mapping
0 Prerequisite Revision of microcomputer system terminologies, High
level, difference between microprocessor and
microcontroller, basics of operating System. 02 --
I Introduction to
Embedded
systems Overview of Embedded System
Architecture, Application areas,
Categories of embedded systems, specialties of embedded
systems.
Recent trends in embedded systems.

Brief introduction to embedded
microcontroller cores CISC, RISC,
ARM, DSP and SoC.

Introduction to Embedded System Integrated
Development Environments (IDEs) with examples.
Self-learning Topics: Comparison of CISC & RISC,
Case studies of Real Time Embedded Systems. 04 CO1
II The
Microcontroller
Architecture and
Programming of
8051 Introduction to 8051 Microcontroller, Architecture, Pin
configuration, Memory
Organization, Input /Output Ports, Counter and Timers,
Serial communication, Interrupts. Addressing modes,
Instruction set
8051 developing tools,
Programming based on
Arithmetic & Logical
Operations, I/O parallel and serial ports, Timers &
Counters, and I SR.
Self-learning Topics: Writing 8051 programming in
Embedded C 10
CO2
III Interfacing with
8051Microcontr
oller Interfacing 8051 with peripherals: ADC, DAC, stepper
motor.

Interfacing 8051 with LED, LCD, keyboard, Temp
sensor, etc. using assembly language.

Self-learning Topics: Study of 8051 based GSM,
Bluetooth and RS232 communication 04 CO3
IV ARM 7
Architecture Architectural inheritance, Detailed study of Programmer’s
model,
ARM Development tools, Addressing modes, Instruction
set: Data processing, Data Transfer, Control flow.
Pipelining,

Writing simple assembly language programs.
07 CO4

Page 50

Brief introduction to exceptions and interrupts handling.
Self-learning Topics : Writing ARM programs in
Embedded C and Python for sensor application
V Open source
RTOS
Real Time
system concept
with embedded
OS Basics of RTOS: Real -time concepts, Hard Real time and
Soft Real -time, differences between general purpose OS
& RTOS,

Basic architecture of an RTOS, scheduling systems,
Inter -process -communica tion using pipes and mailboxes,
performance matrix in scheduling models, interrupt
management in RTOS environment, RTOS comparative
study.
ucos2 for real time embedded system demonstrate one
case study: Case study of automobile

RTOS issues in multitaskin g –selecting a Real Time
Operating
System

Self-learning Topics: Inter -process -communication using
semaphore, and Mutex, RTOS simple programming using
ucos2 07 CO5
VI Introduction to
Embedded
Platforms Overview of various Embedded hardware Platforms:
Architecture of Arduino,

Basic Arduino programming using Arduino IDE and
Arduino libraries for interfacing of LCD and sensors such
as Temperature (DHT11), Pressure, Humidity.

RaspberryPi (RPi -Functional Block diagram and its
operation, GPIO pins, Featur es of RaspbianOS)

Programming Arduino using python (pyserial or
pyfirmata): blink.py
Programming RaspberryPi GPIO using python: blink.py

Self-learning Topics: Study of Arduino/ RaspberryPi
using Thingspeak cloud platform and Blink app using
Mobile. 05 CO6

Textbooks:

1 M. A. Mazidi, J. G. Mazidi, R. D., McKinlay,” The 8051 microcontroller & Embedded
systems Using Assembly and C”, Pearson, 3rd edition
2 Embedded / real – time systems: concepts, design & programming, Black Book, Dr. K. V.
K. K. Prasad, Dreamtech press, Reprint edition 2013
3 Shibu K. V., “Introduction to embedded systems”, McGraw Hill

Page 51

References:

1 Steve Furber, “ARM System on chip Architecture”, Pearson, edition second
2 Laya B. Das, “Embedded systems an integrated approach”, Pearson, Third impression,
2013
3 Embedded Systems, Architecture, program and Design by Rajkamal
4 Simon Monk,” Raspberry Pi Cookbook”, O’reilly
5 Massimo Banzi, “Getting Started with Arduino: The Open Source Electronics Prototyping
Platform (Make)”, O'Reilly Media.
6 https://nptel.ac.in/courses/117/104/117104072/
7 https://www.coursera.org/learn/raspberry -pi-platform

Assessment:
Internal Assessment (IA) for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to
50% of syllabus content must be covered in First IA Test and remaining 40% to 50% of
syllabus content must be covered in Second IA Test
 Question paper format
 Question Paper will comprise of a total of six questions each carrying 20 marksQ.1
will be compulsory and should cover maximum contents of the syllabus
 Remaining questions will be mixed in nature (part (a) and part (b) of each question
must be from different modules. For example, if Q.2 has part (a) from Module 3 then
part (b) must be from any other Module randomly selected from all the modules)
 A total of four questions need to be answered .

Page 52

Course Code Course Name Teaching Scheme
(Contact Hours)
Credits Assigned
Theory Practical Theory Practical Total
ITDO5012 Advance Data
Management
Technologies 03 -- 03 -- 03

Course Code Course Name Examination Scheme
Theory
Term
Work Pract
/ Oral Total
Internal Assessment End
Sem
Exam Exam
Duratio
n
(in Hrs)
Test1 Test 2 Avg.
ITDO5012 Advance Data
Management
Technologies 20 20 20 80 3 -- -- 100

Course Objectives:

Sr. No. Course Objectives
The course aims:
1 To impart knowledge related to query processing and query optimization phases of a
database management system.
2 To learn advanced techniques for data management and to overview emerging data
models like Temporal, Mobile, and Spatial database .
3 To introduce advanced database models like distributed databases.
4 To create awareness of how enterprise can organize and analyze large amounts of data by
creating a Data Warehouse.
5 To understand the process of data extraction, transformation and loading.
6 To understand the concept of Big data and NoSQL databases..

Course Outcomes:

Sr. No. Course Outcomes: Cognitive levels
of attainment
as per bloom’s
Taxonomy
1 Measure query costs and design alternate efficient paths for query
execution . L1,L2
2 Apply sophisticated access protocols to control access to the database. L1,L2,L3
3 Implement Distributed databases. L1,L2,L3

Page 53

Prerequisite: Course on Database Management System

DETAILED SYLLABUS:

Sr.
No. Module Detailed Content Hours CO
Mapping
0 Prerequisite Reviewing basic concepts of a
Relational database, SQL concepts 02 ----
I Query
Processing
and
Optimization Overview: Introduction, Query processing in DBMS,
Steps of Query Processing, Measures of Query Cost
Selection Operation, Sorting, Join Operation, Evaluation
of Expressions.
Query Optimization Overview, Goals of Query
Optimization, Approaches of Query Optimization,
Transformations of Relational Expression, Estimating
Statistics of Expression Results Choice of Evaluation
Plans.

Self-learning Topics: Solve problems on query
optimization.
06 CO1
II Advanced
Data
Management
Techniques Advanced Database Access protocols:
Discretionary Access Control Based on Granting and
Revoking Privileges. Mandatory Access Control and Role -
Based Access Control, Remote Database access protocol.
Overview of Advanced Database Models like Mobile
databases, Temporal databases, Spatial databases .

Self-learning Topics : Learn Data Security concepts like
Authentication, Authorization and encryption. 06 CO2
III Distributed
Databases Introduction: Distributed Data Processing, Distributed
Database System: Architecture, Types, Design Issues.
Data Fragmentation , Allocation in distributed databases.

Self-learning Topics : Query Optimization in Distributed
Databases 04 CO3
IV Data
Warehous ing,
Dimensional
Modelling
and
OLAP The Need for Data Warehousing; Data Warehouse
Defined; Is data warehouse still relevant in the age of big
data, Features of a Data Warehouse; Data Warehouse
Architecture -Enterprise or centralized, federated and multi
tired architectures; Data Warehouse and Data Marts; Data
Warehousing Design Strategies, Data modeling -
Dimensional Model; The Star Schema; How Does a Query
Execute? The Snowflake Schema; Fact Tables and
Dimension Tables; Factless Fact Table;, Updates To
Dimension Tables, Primary Keys, Surrogate Keys &
Foreign Keys.
What is business intelligence, use of BI, Tools used in BI,
Need for Online Analytical Processing; OLAP Operations 09 CO4 4 Organize strategic data in an enterprise and build a data Warehouse. L1,L2,L3
5 Analyse data using OLAP operations so as to take strategic decisions. L1,L2,L3,L4
6 Design modern applications using NoSQL databases .
databases . L1,L2,L3,L4

Page 54

in a cube: Roll -up, Drill -down, Slice, Dice, Pivot; OLAP
Architectures: MOLAP, ROLAP, DOLAP and HOLAP.

Self-learning T opics: Explore life cycle of data
warehouse development

V ETL Process Challenges in ETL Functions; Data Extraction;
Identification of Data Sources; Immediate Data Extraction,
Deferred Data Extraction; Data Transformation: Tasks
Involved in Data Transformation, Techniques of Data
Loading

Self-learning Topics:
Find out various ETL tools for enterprise data
management. 05 CO5
VI Big data and
NoSQL Big data and NoSQL : Introduction, types and
characteristics of big data, What is NoSQL, CAP theorem,
BASE property,
NoSQL data architecture patterns: Key -value stores, Graph
stores, Column family stores, Document stores.

Self-learning Topics: Google’s Bigtable, Cassandra,
MongoDB, Neo4j 07 CO6

Textbooks:

1 Korth, Slberchatz,Sudarshan, :”Database System Concepts”, 6th Edition, McGraw – Hill
2 Elmasri and Navathe, “Fundamentals of Database Systems”, 6th Edition, PEARSON Education.
3 Theraja Reema, “Data Warehousing”, Oxford University Press.
4 Raghu Ramakrishnan and Johannes Gehrke, “D atabase Management Systems” 3rd Edition -
McGraw Hill

References:

1 Paulraj Ponniah, “Data Warehousing: Fundamentals for IT Professionals”, Wiley India.
2 Ralph Kimball, Margy Ross, “The Data Warehouse Toolkit: The Definitive Guide to
Dimensional Modeling”, 3rd Edition. Wiley India.
3 Han, Kamber, "Data Mining Concepts and Techniques", Morgan Kaufmann 3nd Edition.
4 Peter Rob and Carlos Coronel, “Database Systems Design, Implementation and Management”,
Thomson Learning, 9th Edition.

Page 55

Page 56

Course
Code Course Name Teaching
Scheme
(Contact Hours)
Credits Assigned
Theory Practical Theory Practical Total
ITDO5013 Computer
Graphics &
Multimedia
System 03 -- 03 -- 03

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract /
Oral Total
Internal Assessment End
Sem
Exam Exam
Duratio
n
(in Hrs)
Test1 Test 2 Avg.
ITDO5013 Computer
Graphics &
Multimedia
System 20 20 20 80 3 -- -- 100

Course Objectives:

Sr.
No. Course Objectives
The course aims:
1 To equip student with the fundamental knowledge and basic technical competence in the
field of Computer Graphics.
2 To emphasize on understanding of Computer Graphics Algorithms.
3 To prepare the student for advanced areas in the field of Computer Graphics.
4 To introduce student for professional avenues in the field of Computer Graphics
5 To introduce students about basic fundamentals and key aspects of Multimedia system.
6 To equip the students for various techniques of Multimedia.

Course Outcomes:
Sr.
No. Course Outcomes Cognitive levels
of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Describe the basic concepts of Computer Graphics. L1,L2
2 Demonstrate various algorithms for basic graphics primitives. L1,L2
3 Apply 2-D geometric transformations on graphical objects. Use various
Clipping
algorithms on graphical objects L1,L2,L3
4 Explore 3-D geometric transformations and curve representation techniques. L1,L2,L3
5 Describe the basics of Multimedia System L1,L2
6 Explore the Digital images audio & video and their related concepts.
L1,L2,L3

Page 57

Prerequisite: Basic knowledge of mathematics.

DETAILED SYLLABUS:

Sr.
No. Module Detailed Content Hours CO
Mapping
0 Prerequisite Basic knowledge of mathematics -- ---
I Introduction Definition and Representative uses of computer
graphics, Overview of coordinate system, Definition
of scan conversion, Raster scan & random scan
displays, Architecture of raster graphics system with
display processor, Architecture of random scan
systems.

Self-learning Topics: - study the working of some
Raster scan display devices

02 CO1
II Output
Primitives
Scan conversions of point, line and circle: DDA
algorithm and Brenham algorithm for line drawing,
Midpoint algorithm for circle, Aliasing, Antialiasing
techniques like Pre filtering and post filtering, super
sampling, and pixel phasing. Filled Area Primitive:
Scan line Polygon Fill algorithm, inside outside tests,
Boundary Fill and Flood fill algorithm.

Self-learning Topics: -Implementation of DDA and
Bresenhams line algorithm for dotted line, dashed line,
Dash -dot line etc.

08 CO2
III Two
Dimensional
Transformations
and Clipping Basic 2D transformations: - Translation, Scaling,
Rotation, Reflection. Matrix representation and
Homogeneous Coordinates. Composite transformation.
Viewing transformation pipeline and Window to
Viewport coordinate transformation. Clipping
operations: Point clipping, Line Clipping.
Line clipping algorithms: Cohen - Sutherland, Liang -
Barsky,
Polygon Clipping Algorithms: Sutherland - Hodgeman,
Weiler -Atherton.

Self-learning Topics: -Implementation of 2D
transformation s like translation, rotation and scaling.
Implementation of clipping algorithm.
09 CO3
IV 3D
Transformation,
curves and
fractals
3D Transformations: Translation, Rotation, Scaling.
Reflection , Composite transformations: Rotation about
an arbitrary axis.
Bezier Curve, B-Spline Curve. 06 CO4

Page 58

Fractal -Geometry: Fractal Dimension, Hilbert’s curve,
Koch Curve.

Self-learning Topics: -Implementation of 3D
transformations, Bezier curve , Koch curve.

V Introduction to
Multimedia
Overview, Objects and Elements of Multimedia,
Applications of Multimedia, Multimedia Systems
Architecture – IMA, Workstation, Network, Types of
Medium (Perception, Representation -..), Interaction
Techniques

Self-learning Topics: -Study the objects and elements
of multimedia
04 CO5
VI Digital Image,
audio & video
Digital Image Representation (2D format, resolution)
Types of Images (monochrome, gray, color),
File formats: JPG. Compression Techniques:
fundamentals (coding, inter pixel and psychovisual
redundancies). Types – lossless and lossy Compression,
Lossless Compression Algorithms – Shannon -Fano,
Lossy Compression Algorithm – JPEG
Digital Audio
Basic Sound Concepts: computer representation of
sound
File Formats – WAV
Digital Video
Digitization of Video, types of video signals
(component, composite and S - video).
File Formats: MPEG Video

Self-learning Topics: -Implementation of compression
algorithms, Analysis of Digital audio and digital video
file formats.
10 CO6

Text Books :

1 Hearn & Baker, “Computer Graphics C version”, 2nd Edition, Pearson Publication
2 James D. Foley, Andries van Dam, Steven K Feiner, John F. Hughes, “Computer Graphics
Principles and Practice in C”, 2ndEdition, Pearson Publication
3 Rajesh K. Maurya, “Computer Graphics”, Wiley India Publication.
4 Multimedia System Design, Prabhat K. Andleigh& Kiran Thakrar, PHI
5 Fundamentals of Multimedia, Ze -Nian Li & Mark S. Drew, PHI.

Page 59

Assessment:
Internal Assessment (IA)for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to
50% of syllabus content must be covered in First IA Test and remaining 40% to 50% of
syllabus content must be covered in Second IA Test
 Question paper format
 Question Paper will comprise of a total of six questions each carrying 20 marksQ.1
will be compulsory and should cover maximum contents of the syllabus
 Remaining questions will be mixed in nature (part (a) and part (b) of each question
must be from different modules. For example, if Q.2 has part (a) from Module 3 then
part (b) must be from any other Module randomly se lected from all the modules)
 A total of four questions need to be answered

References:

1 D. Rogers, “Procedural Elements for Computer Graphics”, Tata McGraw -Hill Publications.
2 Samit Bhattacharya, “Computer Graphics”, Oxford Publication
3 Multimedia Communication Systems: Techniques, Standards & Networks, K. R. Rao, Zoran S.
Bojkovic & Dragorad A. Milovanovic, TMH.
4 Multimedia Systems, K. Buford, PHI.

Sr.No Online Resources
1 https://nptel.ac.in/courses/106/106/106106090/
2 https://nptel.ac.in/courses/106/103/106103224/
3 https://nptel.ac.in/courses/106/102/106102065/
4 https://onlinecourses.swayam2.ac.in/nou21_cs04/preview
5 https://nptel.ac.in/courses/117/105/117105083/

Page 60

Course
Code Course Name Teaching
Scheme
(Contact Hours)
Credits Assigned
Theory Practical Theory Practical Total
ITDO5014 Advanced Data
structure and
Analysis 03 -- 03 -- 03

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract /
Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test 2 Avg.
ITDO5014 Advanced Data
structure and
Analysis 20 20 20 80 3 -- -- 100

Course Objectives:

Sr.
No. Course Objectives
The course aims:
1 To learn mathematical background for analysis of algorithm
2 To learn various advanced data structures.
3 To understand the different design approaches of algorithm.
4 To learn dynamic programming methods.
5 To understand the concept of pattern matching
6 To learn advanced algorithms.

Course Outcomes:

Sr.
No. Course Outcomes Cognitive levels of
attainment as per
Bloom’s Taxonomy
On successful completion, of course, learner/student will be able to:
1 Understand the different methods for analysis of algorithms. L1,L2
2 Choose an appropriate advanced data structure to solve a specific problem. L1,L2
3 Apply an appropriate algorithmic design approach for a given problem. L1,L2,L3
4 Apply the dynamic programming technique to solve a given problem. L1,L2,L3
5 Select an appropriate pattern matching algorithm for a given application. L1,L2,L3
6 Understand the concepts of Optimization, Approximation and Parallel
computing algorithms. L1,L2

Prerequisite: Data structures and Analysis, Knowledge of Any Programming Language

Page 61

DETAILED SYLLABUS:

Sr.
No Module Detailed Content Hours CO
Mapping
0 Prerequisite Basic of Data structures and analysis and programming
language. 02 -
I Introduction Fundamentals of the analysis of algorithms: Time and
Space complexity, Asymptotic analysis and notation,
average and worst -case analysis, Recurrences: The
substitution method, Recursive tree method, Masters
method.

Self-learning Topics: Analysis of Time and space
complexity of iterative and recursive algorithms
04 CO1
II Advanced Data
Structures B/B+ tree, Red -Black Trees, Heap operations,
Implementation of priority queue using heap,
Topological Sort.

Self-learning Topics: Implementation of Red -Black
Tree and Heaps.
05 CO2
III Divide and
Conquer AND
Greedy
algorithms Introduction to Divide and conquer, Analysis of Binary
Search, Merge sort and Quick sort, Finding minimum
and maximum algorithm.

Introduction to Greedy Algorithms: Knapsack Problem,
Job sequencing using deadlines, Optimal storage on
tape, Optimal Merge Pattern, Analysis of all these
algorithms and problem solving.

Self-learning Topics: Implementation of minimum and
maximum algorith m, Knapsack problem, Job
sequencing using deadlines.
08 CO3
IV Dynamic
algorithms Introduction to Dynamic Algorithms, all pair shortest
path, 0/1 knapsack, travelling salesman problem, Matrix
Chain Multiplication, Optimal binary search tree,
Analysis of All algorithms and problem solving.

Self-learning Topics: Implementation of All pair
shortest path, 0/1 Knapsack and OBST.
06 CO4
V String
Matching Introduction, the naïve string matching algorithm,
Rabin Karp algorithm, Boyer Moore algorithm, Knuth -
Morris -Pratt algorithm, Longest Common Subsequence
(LCS), Analysis of All algorithms and problem
solving.

Self-learning Topics: Implementation of Robin Karp
algorithm, KMP algorithm and LCS.

07 CO5

Page 62

VI Advanced
Algorithms
and NP
problems Optimization Algorithms: Genetic algorithm(GA),

Approximation Algorithms: Vertex -cover problem,

Parallel Computing Algorithms: Fast Fourier
Transform,

Introduction to NP -Hard and NP -Complete
Problems

Self-learning Topics: Implementation of Genetic
algorithm and Vertex -cover problem 07 CO6

Assessment:
Internal Assessment (IA) for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to
50% of syllabus content must be covered in First IA Test and remaining 40% to 50% of
syllabus content must be covered in Second IA Test
 Question paper format
 Question Paper will comprise of a total of six questions ea ch carrying 20 marks Q.1
will be compulsory and should cover maximum contents of the syllabus
 Remaining questions will be mixed in nature (part (a) and part (b) of each question
must be from different modules. For example, if Q.2 has part (a) from Module 3 then
part (b) must be from any other Module randomly selected from all the modules)
 A total of four questions need to be answered.
Textbooks:

1 Introduction to Algorithms, Cormen, Leiserson, Rivest, Stein, PHI.
2 Algorithms: Design and Analysis, Harsh Bhasin, OXFORD.
3 Fundamentals of Computer Algorithms, Horowitz, Sahani, Rajsekaran, Universities Press.
4 C and Data structures, Deshpande, Kakde, Dreamtech Press.

References:

1 Data Structures and Algorithms in C++, Goodritch, Tamassia, Mount, WILEY.
2 Data Structures using C, Reema Thareja , OXFORD.
3 Data Structures and Algorithm Analysis in C, Mark A. Weiss, Pearson.
4 Optimization Algorithms and Applications, By Rajesh Kumar Arora by Chapman and Hall

Online Resources

Sr.No Website Links
1 https://nptel.ac.in/courses/106/106/106106131/
2 https://swayam.gov.in/nd1_noc19_cs47/preview
3 https://www.coursera.org/specializations/algorithms
4 https://www.mooc -list.com/tags/algorithms

Page 63

Program Structure for Third Year Information Technology
Semester V & VI
UNIVERSITY OF MUMBAI
(With Effect from 2021 -2022 )

Semester VI

Course
Code
Course Name Teaching Scheme
(Contact Hours)
Credits Assigned
Theory Pract.
Tut. Theory Pract. Total
ITC601 Data Mining &
Business Intelligence 3 -- 3 -- 3
ITC602 Web X.0 3 -- 3 3
ITC603 Wireless Technology 3 -- 3 -- 3
ITC604 AI and DS – 1 3 -- 3 -- 3
ITDO601
X Department Optional
Course – 2 3 -- 3 -- 3
ITL601 BI Lab -- 2 -- 1 1
ITL602 Web Lab -- 2 -- 1 1
ITL603 Sensor Lab -- 2 -- 1 1
ITL604 MAD & PWA Lab -- 2 -- 1 1
ITL605 DS using Python Skill based
Lab -- 2 -- 1 1
ITM601 Mini Project – 2 B Based on
ML -- 4$ -- 2 2
Total 15 14 15 07 22

Course
Code

Course Name Examination Scheme
Theory Term
Work Prac
/oral Total

Internal Assessment End
Sem
Exam Exam.
Duration
(in Hrs)
Test1 Test2 Avg
ITC601 Data Mining &
Business Intelligence 20 20 20 80 3 -- -- 100
ITC602 Web X.0 20 20 20 80 3 -- -- 100
ITC603 Wireless Technology 20 20 20 80 3 -- -- 100
ITC604 AI and DS – 1 20 20 20 80 3 -- -- 100
ITDO601
X Department Optional
Course – 2 20 20 20 80 3 -- -- 100
ITL601 BI Lab -- -- -- -- -- 25 25 50
ITL602 Web Lab -- -- -- -- -- 25 25 50
ITL603 Sensor Lab -- -- -- -- -- 25 25 50
ITL604 MAD & PWA Lab -- -- -- -- -- 25 25 50

Page 64

ITL605 DS using Python Lab
(SBL) -- -- -- -- -- 25 25 50
ITM601 Mini Project – 2 B Based on
ML -- -- -- -- -- 25 25 50
Total -- -- 100 400 -- 150 150 800

$ indicates work load of Learner (Not Faculty), for Mini -Project. S tudents can form groups with minimum
2(Two) and not more than 4(Four). Faculty Load: 1hour per week per four groups.

ITDO601X Department Optional Course – 2
ITDO6011 Software Architecture
ITDO6012 Image Processing
ITDO6013 Green IT
ITDO6014 Ethical Hacking and Forensic

Page 65

Course
Code
Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Tutorial Theory Practical/
Oral Tutorial Total
ITC601 Data Mining &
Business Intelligence 03 -- -- 03 -- -- 03

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract /
Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test 2 Avg.

ITC601 Data Mining &
Business Intelligence 20 20 20 80 3 -- -- 100

Course Objectives:

Course Outcomes:

Sr.
No. Course Outcomes Cognitive levels
of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Demonstrate an understanding of the importance of data warehousing and data mining
and the principles of business intelligence. L1
2 Organize and prepare the data needed for data mining using pre preprocessing
techniques. L1,L2,L3
3 Perform exploratory analysis of the data to be used for mining. L1,L2,L3,L4
4 Implement the appropriate data mining methods like classification, clustering or
Frequent Pattern mining on large data sets. L1,L2,L3,L4,L5
5 Define and apply metrics to measure the performance of various data mining L1,L2,L3 Sr.
No. Course Objectives
The course aims:
1 To introduce the concept of data warehouse data Mining as an important tool for enterprise data
management and as a cutting -edge technology for building competitive advantage.
2 To enable students to effectively identify sources of data and process it for data mining.
3 To make students well versed in all data mining algorithms, methods of evaluation.
4 To impart knowledge of tools used for data mining
5 To provide knowledge on how to gather and analyze large sets of data to gain useful business
understanding.
6 To impart skills that can enable students to approach business problems analytically identifying
opportunities to derive business value f rom data.

Page 66

algorithms.

6 Apply BI to solve practical problems: Analyze the problem domain, use the data
collected in enterprise apply the appropriate data mining technique, interpret and
visualize the results and provide decision support. L1,L2,L3

Prerequisite: Database Management System

DETAILED SYLLABUS:
Sr.
No. Module Detailed Content Hours CO
Mapping
0 Prerequisite Basic Knowledge of databases 01 -
I Data Warehouse
(DWH)
Fundamentals with
Introduction to
Data Mining DWH characteristics, Dimensional modeling:
Star, Snowflakes, OLAP operation, OLTP vs
OLAP
Data Mining as a step in KDD, Kind of patterns
to be mined, Technologies used, Data Mining
applications.

Self-learning Topics : Data Marts, Major issues
in Data Mining. 04 CO1
II Data Exploration
and Data
Preprocessing
Types of Attributes, Statistical Description of
Data, Measuring Data Similarity and
Dissimilarity.
Why Preprocessing? Data Cleaning, Data
Integration, Data Reduction: Attribute Subset
Selection, Histograms, Clustering, Sampling, Data
Cube aggregation, Data transformation and Data
Discretization: Normalization, Binning,
Histogram Analysis

Self-learning Topics Data Visualization, Concept
hierar chy generation 06 CO2,
CO3
III Classification Basic Concepts; Classification methods: 1.
Decision Tree Induction: Attribute Selection
Measures, Tree pruning. 2. Bayesian
Classification: Naïve Bayes Classifier. Prediction:
Structure of regression models;
Simple linear regression, Accuracy and Error
measures, Precision, Recall, Holdout, Random
Sampling, Cross Validation, Bootstrap,
Introduction of Ensemble methods, Bagging,
Boosting, AdaBoost and Random forest.

Self-learning Topics : Multiple linear regress ion,
logistic regression, Random forest, nearest
neighbour classifier, SVM 08 CO4,
CO5
IV Clustering and
Outlier Detection Cluster Analysis: Basic Concepts; Partitioning
Methods: K -Means, K Medoids; Hierarchical
Methods: Agglomerative, Divisive, BIRCH;
Density -Based Methods: DBSCAN.
What are outliers? Types, Challenges; Outlier
Detection Methods: Supervised, Semi Supervised, 08 CO4

Page 67

Unsupervised, Proximity based, Clustering Based.

Self-learning Topics Hierarchical methods :
Chameleon, Density based methods: OPT ICS,
Grid based methods: STING, CLIQUE
V Frequent Pattern
Mining Basic Concepts : Market Basket Analysis,
Frequent Itemset, Closed Itemset, and Association
Rules; Frequent Itemset. Mining Methods: The
Apriori Algorithm: Finding Frequent Itemset
Using Candidate Generation, Generating
Association Rules from Frequent
Itemset, Improving the Efficiency of Apriori, A
pattern growth approach for mining Frequent
Itemset, Mining Frequent Itemset using vertical
data formats;
Introduction to Advance Pattern Mining: Mining
Multilevel Association Rules and
Multidimensional Association Rules.

Self-learning Topics : Association Mining to
Correlation Analysis, lift, Introduction
to Constraint -Based Association Mining 08 CO4,
CO5
VI Business
Intelligence What is BI? Business intelligence architectures;
Definition of decision support system;
Development of a business intelligence system
using Data Mining for business Applications like
Fraud Detection, Recommendation System

Self-learning Topics : Clickstream Mining,
Market Segmentation, Retail industry,
Telecommunications industry, Banking & fi nance
CRM, Epidemic prediction, Fake News Detection,
Cyberbullying, Sentiment Analysis etc. 04 CO6

Text Books:

1. Han, Kamber, "Data Mining Concepts and Techniques", Morgan Kaufmann 3nd Edition.
2. P. N. Tan, M. Steinbach, Vipin Kumar, “Introduction to Data Mining”, Pearson Education.
3. Paulraj Ponniah “Data Warehousing Fundamentals: A Comprehensive Guide for IT Professionals” Wiley
Publications
4. Business Intelligence: Data Mining and Optimization for Decision Making by Carlo Vercellis, Wiley India
Publication s.
5. G. Shmueli, N.R. Patel, P.C. Bruce, “Data Mining for Business Intelligence: Concepts, Techniques, and
Applications in Microsoft Office Excel with XLMiner”, 2nd Edition, Wiley India.

References:

1. Michael Berry and Gordon Linoff “Data Mining Techniques” , 2nd Edition Wiley Publications.
2. Michael Berry and Gordon Linoff “Mastering Data Mining - Art & science of CRM”, Wiley Student
Edition.
3. Vikram Pudi & Radha Krishna, “Data Mining”, Oxford Higher Education.
4. Data Mining https://onlinecourses.nptel.ac.in/noc21_cs06/preview

Page 68

Page 69

Course
Code
Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Tutorial Theory Practical/
Oral Tutorial Total
ITC602 Web X.0 03 -- -- 03 -- -- 03

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract /
Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test 2 Avg.
ITC602 Web X.0
20 20 20 80 3 -- -- 100

Course Objectives:

Course Outcomes:

Sr. No. Course Outcomes Cognitive
levels of
attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Understand the basic concepts related to web analytics and semantic web. L1, L2
2 Understand how TypeScript can help you eliminate bugs in your code and enable
you to scale your code. L1, L2
3 Understand AngularJS framework and build dynamic, responsive single -page
web applications. L2, L3
4 Apply MongoDB for frontend and backend connectivity using REST API. L1, L2, L3
5 Apply Flask web development framework to build web applications with less
code. L1, L2, L3 Sr. No. Course Objectives
The course aims:
1 To understand the digital evolution of web technology.
2 To learn Type Script and understand how to use it in web application.
3 To empower the use of AngularJS to create web applications that depend on the Model -View -
Controller Architecture.
4 To gain expertise in a leading document -oriented NoSQL database, designed for speed, scalability,
and developer agility using MongoDB.
5 To build web applications quickly and with less code using Flask framework.
6 To gain knowledge of Rich Internet Application Technologies.

Page 70

6 Develop Rich Internet Application using proper choice of Framework. L1, L2, L3, L4

Prerequisite: Object Oriented Programming, Python Programming, HTML and CSS.

DETAILED SYLLABUS:

Sr.
No. Module Detailed Content Hours CO
Mapping
0 Prerequisite HTML/HTML5 (Tags, Attributes and their properties),
CSS/CSS3 (Types and Properties), Basics of Java Script,
Python Programming 02 --
I Introduction to
WebX.0 Evolution of WebX.0 ; Web Analytics 2.0 : Introduction to
Web Analytics, Web Analytics 2.0, Clickstream Analysis,
Strategy to choose your web analytics tool, Measuring the
success of a website; Web3.0 and Semantic Web :
Characteristics of Semantic Web, Components of Semantic
Web, Semantic Web Sta ck, N -Triples and Turtle, Ontology,
RDF and SPARQL

Self-learning Topics : Semantic Web Vs AI, SPARQL Vs
SQL. 04 CO1
II Type Script Overview, TypeScript Internal Architecture, TypeScript
Environment Setup, TypeScript Types, variables and
operators, Decision Making and loops, TypeScript
Functions, TypeScript Classes and Objects, TypeScript
Modules

Self-learning Topics : Javascript Vs TypeScript 06 CO2
III Introduction to
AngularJS Overview of AngularJS, Need of AngularJS in real web
sites, AngularJS modules, AngularJS built -in directives,
AngularJS custom directives, AngularJS expressions,
Angular JS Data Binding, AngularJS filters, AngularJS
controllers, AngularJS scope, AngularJS dependency
injection, Angular JS Services, Form Validation, Routing
using ng -Route, ng -Repeat, ng -style, ng -view, Built -in
Helper Functions, Using Angular JS with Typescript

Self-learning Topics : MVC model, DOM model, Javascript
functions and Error Handling 08 CO3
IV MongoDB and
Building REST
API using
MongoDB MongoDB : Un derstanding MongoDB, MongoDB Data
Types, Administering User Accounts, Configuring Access
Control, Adding the MongoDB Driver to Node.js,
Connecting to MongoDB from Node.js, Accessing and
Manipulating Databases, Manipulating MongoDB
Documents from Node.js, A ccessing MongoDB from
Node.js, Using Mongoose for Structured Schema and
Validation.
REST API : Examining the rules of REST APIs, Evaluating
API patterns, Handling typical CRUD functions (create,
read, update, delete), Using Express and Mongoose to
interact with MongoDB, Testing API endpoints

Self-learning Topics : MongoDB vs SQL DB 08 CO4
V Flask Introduction, Flask Environment Setup, App Routing, URL
Building, Flask HTTP Methods, Flask Request Object,
Flask cookies, File Uploading in Flask 06 CO5

Page 71

Self-learning Topics : Flask Vs Django
VI Rich Internet
Application AJAX: Introduction and Working
Developing RIA using AJAX Techniques : CSS, HTML,
DOM, XML HTTP Request, JavaScript, PHP, AJAX as
REST Client
Introduction to Open Source Frameworks and CMS for
RIA: Django, Drupal, Joomla

Self-learning Topics : Applications of AJAX in Blogs,
Wikis and RSS Feeds 05 CO6

Text Books:

1. Boris Cherny, “ Programming TypeScript - Making Your Javascript Application Scale”, O’Reilly
Media Inc.
2. Adam Bretz and Colin J. Ihrig, “Full Stack JavaScript Development with MEAN”, SitePoint Pty. Ltd.
3. Simon Holmes Clive Harber, “Getting MEAN with Mongo, Express, Angular, and Node”, Manning
Publications.
4. Miguel Grinberg, “Flask Web Development: Developing Web Applications with Python”, O’Reilly.
5. Dr. Deven Shah, “Advanced Internet Programming”, StarEdu Solutions.

References:

1. Yakov Fain and Anton Moiseev, “TypeScript Quickly”, Manning Publications.
2. Steve Fenton, “Pro TypeScript: Application - Scale Javascript Development”, Apress
3. Brad Dayley, Brendan Dayley, Caleb Dayley, “Node.js, MongoDB and Angular Web Development:
The definitive guide to using the MEAN stack to build web applications”, 2nd Edition, Addison -
Wesley Professional

Online References:
Sr. No. Website Links
1. https://www.nptel.ac.in
2. https://swayam.gov.in
3. https://www.coursera.org
4. https://udemy.com

Assessment:
Internal Assessment (IA) for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to 50% of
syllabus content must be covered in First IA Test and remaining 40% to 50% of syllabus content
must be covered in S econd IA Test
 Question paper format
 Question Paper will comprise of a total of six questions each carrying 20 marksQ.1 will be
compulsory and should cover maximum contents of the syllabus
 Remaining questions will be mixed in nature (part (a) and part (b) of each question must be
from different modules. For example, if Q.2 has part (a) from Module 3 then part (b) must be
from any other Module randomly selected from all the modules)
 A total of four questions need to be answered

Page 72

Course
Code
Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Tutorial Theory Practical/
Oral Tutorial Total
ITC603 Wireless Technology 03 -- -- 03 -- -- 03

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract /
Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test 2 Avg.
ITC603 Wireless Technology
20 20 20 80 3 -- -- 100

Course Objectives:

Course Outcomes:

Sr.
No. Course Outcomes Cognitive levels of
attainment as per
Bloom’s Taxonomy
On successful completion, of course, learner/student will be able to:
1 Describe the basic concepts of Wireless Network and Wireless
Generations. L1,L2
2 Demonstrate and Evaluate the various Wide Area Wireless Technologies. L1,L2, L3, L4, L5
3 Analyze the prevalent IEEE standards used for implementation of WLAN
and WMAN Technologies L1,L2,L3, L4
4 Appraise the importance of WPAN, WSN and Ad -hoc Networks. L1,L2,L3,L4, L5
5 Analyze various Wireless Network Security Standards. L1,L2,L3,L4
6 Review the design considerations for deploying the Wireless Network
Infrastructure. L1,L2
Sr. No. Course Objectives
The course aims:
1 Discuss the Fundamentals of Wireless Communication.
2 Comprehend the Fundamental Principles of Wide Area Wireless Networking Technologies and
their Applications.
3 Explain Wireless Metropolitan and Local Area Networks.
4 Describe Wireless Personal Area Networks and Ad hoc Networks
5 Learn and Analyze Wireless Network Security Standards.
6 Study the Design Considerations for Wireless Networks.

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Prerequisite: Principle of Communication, Computer Network and Network Design, Computer Network
Security.

DETAILED SYLLABUS:

Sr.
No. Module Detailed Content Hours CO
Mapping
0 Prerequisite Digital Modulation Techniques – ASK, FSK, BPSK,
QPSK; Electromagnetic Spectrum; Multiplexing
Techniques – FDM, TDM, OFDM ; OSI and TCP/IP
Model ; Need for Security, Types of Security Threats
and Attacks. 02 --
I Fundamentals of
Wireless
Communication Introduction to Wireless Communication -
Advantages, Disadvantages and Applications;
Multiple Access Techniques - FDMA, TDMA,
CDMA, OFDMA; Spread Spectrum Techniques –
DSSS, FHSS ; Evolution of wireless generations –
1G to 5G (Based on technological differences and
advancements); 5G – Key requirements and drivers
of 5G systems, Use cases, Massive MIMO.

Self-learning Topics : Modulation Techniques -
QAM, MSK, GMSK 07 CO1
II Wide Area
Wireless Networks Principle of Cellular Communication – Frequency
Reuse concept, cluster size and system capacity, co -
channel interference and signal quality; GSM –
System Architecture, GSM Radio Subsystem, Frame
Structure; GPRS and EDGE – System Architecture;
UMTS – Network Architecture; CDMA 2000 –
Network Architecture; LTE – Network Architecture;
Overview of LoRa & LoRaWAN.
Self-learning Topics :- IS-95 09 CO2
III Wireless
Metropolitan and
Local Area
Networks IEEE 802.16 (WiMax) – Mesh mode, Physical and
MAC layer; IEEE 802.11(Wi -Fi) – Architecture,
Protocol Stack, Enhancements and Applications.

Self-learning Topics :- WLL(Wireless Local Loop). 06 CO3
IV Wireless Personal
Area Networks and
Ad hoc Networks IEEE 802.15.1 (Bluetooth) – Piconet, Scatter net,
Protocol Stack; IEEE 802.15.4 (ZigBee) – LR-
WPAN Device Architecture, Protocol Stack;
Wireless Sensor Network – Design Considerations,
Issues and Challenges, WSN Architecture,
Applications; Introduction of Ad h oc Networks –
MANET and VANET – Characteristics,
Applications, Advantages and Limitations; Over
view of E -VANET( Electrical Vehicular AdHoc
Networks).

Self-learning Topics :- HR–WPAN (UWB) 08 CO4
V Wireless Network
Security
Security in GSM ; UMTS Security ; Bluetooth
Security ; WEP ; WPA2.

Self-learning Topics :- Study of Wireless Security
Tools . 04 CO5

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VI Wireless Network
Design
Considerations Cisco Unified Wireless Network ; D esigning
Wireless Networks with Lightweight Access Points
and Wireless LAN Controllers.
Self-learning Topics :- Cisco Unified Wireless
Network Mobility Services. 03 CO6

Text Books:

1. Wireless Communications, T.L. Singal, McGraw Hill Education.
2. Wireless Communications and Networking, Vijay Garg, Morgan Kaufmann Publishers.
3. Wireless Mobile Internet Security, 2nd Edition, Man Young Rhee, A John Wiley & Sons, Ltd.,
Publication.
4. 5G Outlook –Innovations and Applications,Ramjee Prasad,River Publishers Series in Communications.
5. Designing for Cisco Internetwork Solu tions, 2nd Edition, CCDA, Diane Teare, Cisco Press.

Reference Books:

1. Cellular Communications: A Comprehensive and Practical Guide, Nishith Tripathi, Jeffery H Reed, Wiley.
2. Wireless Communications - Principles & Practice,Theodore S. Rappaport, Prentice Hall Series.
3. Wireless Communications and Networks", William Stallings, Pearson / Prentice Hall.
4. Adhoc & Sensor Networks Theory and Applications, Carlos de Morais Cordeiro, Dharma Prakash Agrawal,
World Scientific,2nd Edition.
5. Wireless Netw orks, Nicopolitidia, M S Obaidat, GI Papadimitriou, Wiley India (Student Edition, 2010).

Online References:
Sr. No. Website/Reference link
1. www.swayam.gov.in
2. www.coursera.org
3. https://doi.org/10.1007/978 -3-642-17878 -8_63
4. https://doi.org/10.1007/978 -3-642-54525 -2_44
5. https://lora -alliance.org/resource_hub/what -is-lorawan/
6. https://doi.org/10.1007/s42835 -021-00687 -8

Assessment:
Internal Assessment (IA) for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to 50% of
syllabus content must be covered in First IA Test and remaining 40% to 50% of syllabus content
must be covered in Second IA Tes t
 Question paper format
 Question Paper will comprise of a total of six questions each carrying 20 marksQ.1 will be
compulsory and should cover maximum contents of the syllabus
 Remaining questions will be mixed in nature (part (a) and part (b) of each quest ion must be
from different modules. For example, if Q.2 has part (a) from Module 3 then part (b) must be
from any other Module randomly selected from all the modules)
 A total of four questions need to be answered

Page 75

Course
Code
Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Tutorial Theory Practical/
Oral Tutorial Total
ITC604 AI and DS - 1 03 -- -- 03 -- -- 03

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract /
Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test 2 Avg.
ITC604 AI and DS - 1
20 20 20 80 3 -- -- 100

Course Objectives:
Course Outcomes:
Sr.
No. Course Outcomes Cognitive levels of
attainment as per
Bloom’s Taxonomy
On successful completion, of course, learner/student will be able to:
1 Develop a basic understanding of the building blocks of AI as presented in terms
of intelligent agents. L1
2 Apply an appropriate problem -solving method and knowledge -representation
scheme. L1,L2, L3
3 Develop an ability to analyze and formalize the problem (as a state space, graph,
etc.). They will be able to evaluate and select the appropriate search method. L1,L2,L3, L4
4 Apply problem solving concepts with data science and will be able to tackle them
from a statistical perspective. L1,L2, L3 Sr. No. Course Objectives
The course aims:
1 To introduce the students’ with different issues involved in trying to define and simulate
intelligence.
2 To familiarize the students’ with specific, well known Artificial Intelligence methods,
algorithms and knowledge representation schemes.
3 To introduce students’ different techniques which will help them build simple intelligent
systems based on AI/IA concepts.
4 To introduce students to data science and problem solving with data science and statistics.
5 To enable students to choose appropriately from a wider range of exploratory and inferential
methods for analyzing data, and interpret the results contextually.
6 To enable students to apply types of machine learning methods for real world problems.

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5 Choose and apply appropriately from a wider range of exploratory and inferential
methods for analyzing data and will be able to evaluate and interpret the results
contextually. L1,L2, L3
6 Understand and apply types of machine learning methods for real world
problems. L1,L2, L3

Prerequisite:
1. Engineering Mathematics III (ITC301)
2. Data Structures and Analysis (ITC302)
3. Engineering Mathematics IV (ITC401)
DETAILED SYLLABUS:

Sr. No. Module Detailed Content Hours CO
Mapping
0 Prerequisite Nil -- --
I Introduction to AI Introduction: Introduction to AI, AI techniques,
Problem Formulation. Intelligent Agents: Structure
of Intelligent agents, Types of Agents, Agent
Environments PEAS representation for an Agent.

Self-Learning Topics : Identify application areas of
AI 04 CO1
II Search Techniques Uninformed Search Techniques: Uniform cost search,
Depth Limited Search, Iterative Deepening,
Bidirectional search. Informed Search Methods:
Heuristic functions, Best First Search, A*, Hill
Climbing, Simulated Annealing. Constraint
Satisfaction Problem Solving: Crypto -Arithmetic
Proble m, Water Jug, Graph Coloring. Adversarial
Search: Game Playing, Min -Max Search, Alpha Beta
Pruning. Comparing Different Techniques.

Self-Learning Topics : IDA*, SMA* 09
CO2
III Knowledge
Representation
using First Order
Logic Knowledge and Reasoning: A Knowledge Based
Agent, WUMPUS WORLD Environment,
Propositional Logic, First Order Predicate Logic,
Forward and Backward Chaining, Resolution.
Planning as an application of a knowledge based
agent. Concepts of Partial Order planning,
Hierarchical Planning a nd Conditional Planning.

Self-Learning Topics: Representing real world
problems as planning problems. 06
CO3
IV Introduction to DS Introduction and Evolution of Data Science, Data
Science Vs. Business Analytics Vs. Big Data, Data
Analytics, Lifecycle, Roles in Data Science Projects.

Self-Learning Topics : Applications and Case
Studies of Data Science in various Industries 04 CO4
V Exploratory Data
Analysis Introduction to exploratory data analysis, Typical data
formats. Types of EDA, Graphical/Non graphical
Methods, Univariate/multivariate methods
Correlation and covariance, Degree of freedom 08
CO5

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Statistical Methods for Evaluation inclu ding
ANOVA.

Self-Learning Topics: Implementation of graphical
EDA methods.
VI Introduction to ML Introduction to Machine Learning, Types of Machine
Learning: Supervised (Logistic Regression, Decision
Tree, Support Vector Machine) and Unsupervised (K
Means Clustering, Hierarchical Clustering,
Association Rules) Issues in Machine learning,
Application of Machine Learning Steps in developing
a Machine Learning Application.

Self-Learning Topics : Real world case studies on
machine learning 08 CO6

Text Books:

1. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 2nd Edition, Pearson
Education.
2. Elaine Rich, Kevin Knight, Shivshankar B Nair, Artificial Intelligence, McGraw H ill, 3rd Edition.
3. Howard J. Seltman, Experimental Design and Analysis, Carnegie Mellon University, 2012/1.
4. Ethem Alpaydın, “Introduction to Machine Learning”, MIT Press

References:

1. Deepak Khemani, A First Course in Artificial Intelligence, McGraw Hill Publication
2. George Lugar, AI -Structures and Strategies for Complex Problem Solving., 4/e, 2002, Pearson Education.
3. Data Science & Big Data Analytics, 1st Edition, 2015, EMC Education Services, Wiley. ISBN: 978 -
1118876138
4. Tom M.Mitchell “Machine Learning” M cGraw Hill
5. Richard I. Levin, David S. Rubin “Statistics for Management” Pearson
6. Vivek Belhekar, “Statistics for Psychology using R” SAGE
Online References:

Sr. No. Website/Reference link
1. https://nptel.ac.in/noc/courses/noc19/SEM2/noc19 -cs83/
2. https://nptel.ac.in/courses/106/105/106105077/
3. https://www.coursera.org/specializations/jhu -data-science
4. https://www.coursera.org/learn/machine -learning
5. https://www.udemy.com/course/statistics -for-data-science -and-business -analysis/

Assessment:
Internal Assessment (IA) for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to 50% of
syllabus content must be covered in First IA Test and remaining 40% to 50% of syllabus content
must be covered in Second IA Test
 Question paper format
 Question Paper will comprise of a total of six questions each carrying 20 marksQ.1 will be
compulsory and should cover maximum contents of the syllabus

Page 78

 Remaining questions will be mixed in nature (part (a) and part (b) of each question must be
from different modules. For example, if Q.2 has par t (a) from Module 3 then part (b) must be
from any other Module randomly selected from all the modules)
 A total of four questions need to be answered
Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Theory Practical Total
ITL601 Business
Intelligence
Lab -- 02 -- 01 01

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract
/ Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test
2 Avg.
ITL601 Business
Intelligence Lab -- -- -- -- -- 25 25 50

Lab Objectives:

Sr. No. Lab Objectives
The Lab experiments aims:
1 To introduce the concept of data Mining as an important tool for enterprise data management and
as a cutting -edge technology for building competitive advantage
2 To enable students to effectively identify sources of data and process it for data mining
3 To make students well versed in all data mining algorithms, methods, and tools.
4 To learn how to gather and analyze large sets of data to gain useful business understanding.
5 To impart skills that can enable students to approach business problems analytically by
identifying opportunities to derive business value from data.
6 To identify and compare the performance of business.

Lab Outcomes:
Sr.
No. Lab Outcomes Cognitive
levels of
attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Identify sources of Data for mining and perform data exploration L2
2 Organize and prepare the data needed for data mining algorithms in terms of
attributes and class inputs, training, validating, and testing files L2
3 Implement the appropriate data mining methods like classification, clustering or
association mining on large data sets using open -source tools like WEKA L3

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4 Implement various data mining algorithms from scratch using languages like
Python/ Java etc. L3
5 Evaluate and compare performance of some available BI packages L3, L4
6 Apply BI to solve practical problems: Analyze the problem domain, use the data
collected in enterprise apply the appropriate data mining technique, interpret and
visualize the results and provide decision support L3, L4

Prerequisite: Object oriented Concept, Java programming language, Python.

Hardware & Software Requirements :

Hardware Requirements Software Requirements
PC i3 processor and above Open source data mining and BI tools like
WEKA, Rapid Miner, Pentaho

DETAILED SYLLABUS :

Sr.
No. Module Detailed Content Hours LO
Mapping
0 Prerequisite -- ---- --
I I Tutorial on
a) Design Star and Snowflake Schema 02 LO 1

II II Implement using tools or languages like
JAVA/ python/R
a) Data Exploration
b) Data preprocessing 04 LO 2
III III Implement and evaluate using languages like
JAVA/ python/R
a) Classification Algorithms
b) Clustering Algorithms
c) Frequent Pattern Mining Algorithms 06 LO4
IV IV Perform and evaluate using any open -source
tools
a) Classification Algorithms
b) Clustering Algorithms
c) Frequent Pattern Mining Algorithms 04 LO3
V V Detailed case study of any one BI tool such as
Pentaho, Tableau and QlikView 04 LO5
VI VI Business Intelligence Mini Project: Each
group assigned one new case study for this
A BI report must be prepared outlining the
following steps:
a) Problem definition, identifying which data
mining task is needed
b) Identify and use a standard data mining
dataset available for the problem. Some
links for data mining datasets are: WEKA,
Kaggle, KDD cup, Data Mining Cup, UCI
Machine Learning Repository etc.
c) Implement appropr iate data mining
algorithm
d) Interpret and visualize the results 06 LO6

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e) Provide clearly the BI decision that is to
be taken as a result of mining

Text Books:

1. Han, Kamber, "Data Mining Concepts and Techniques", Morgan Kaufmann 3nd Edition.
2. G. Shmueli, N.R. Patel, P.C. Bruce, “Data Mining for Business Intelligence: Concepts, Techniques, and
Applications in Microsoft Office Excel with XLMiner”, 1st Edition, Wiley India.
3. Paulraj Ponniah “Data Warehousing Fundamentals: A Comprehensive Gu ide for IT Professionals” Wiley
Publications

References:

1. P. N. Tan, M. Steinbach, Vipin Kumar, “Introduction to Data Mining”, Pearson Education
2. WEKA, RapidMiner Pentaho resources from the Web.
3. https://www.kaggle.com/learn/overview
4. Python for Data Science https://onlinecourses.nptel.ac.in/noc21_cs33/preview

Term Work: Term W ork shall consist of at least 10 racticals based on the above list. Also Term work
Journal must include at least 2 assignments.

Term Work Marks: 25 Marks (Total marks) = 10 Marks (Experiment) + 10 Marks (Mini Project) + 5 Marks
(Attendance)

Oral Exam: An Oral exam will be held based on the above syllabus.

Page 81

Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Theory Practical Total
ITL602 Web Lab
-- 02 -- 01 01

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract
/ Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test 2 Avg.
ITL602 Web Lab
-- -- -- -- -- 25 25 50

Lab Objectives:

Sr. No. Lab Objectives
The Lab experiments aims:
1 Open Source Tools for Web Analytics and Semantic Web.
2 Programming in TypeScript for designing Web Applications.
3 AngularJS Framework for Single Page Web Applications.
4 AJAX for Rich Internet Applications.
5 REST API and MongoDB for Frontend and Backend Connectivity.
6 Flask Framework for building web applications.

Lab Outcomes:

Sr. No. Lab Outcomes Cognitive
Levels of
Attainment as
per Bloom’s
Taxanomy
On successful completion, of course, learner/student will be able to:
1 Understand open source tools for web analytics and semantic web apps
development and deployment. L1, L2
2 Understand the basic concepts of TypeScript for designing web applications. L1, L2, L3
3 Implement Single Page Applications using AngularJS Framework. L1, L2, L3

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4 Develop Rich Internet Applications using AJAX. L1, L2, L3
5 Create REST Web services using MongoDB. L1, L2, L3, L4
6 Design web applications using Flask. L1, L2, L3, L4

Prerequisite: HTML/HTML5, CSS/CSS3, JavaScript, Python

Hardware & Software requirements:

Hardware Specifications Software Specifications
PC with following Configuration
1. Intel Core i3/i5/i7
2. 4 GB RAM
3. 500 GB Hard disk Angular IDE, Visual Studio Code, Notepad++,
Python Editors, MySQL, XAMPP, MongoDB,
JDK

DETAILED SYLLABUS:
Sr.
No. Module Detailed Content Hours LO
Mapping
I Web Analytics &
Semantic Web Study Any 1 tool in each
1. Study web analytics using open source
tools like Matomo, Open Web Analytics,
AWStats, Countly, Plausible.
2. Study Semantic Web Open Source Tools
like Apache TinkerPop, RDFLib, Apache
Jena, Protégé, Sesame.
02 LO1
II TypeScript Perform Any 3 from the following
1. Small code snippets for programs like
Hello World, Calculator using
TypeScript.
2. Inheritance example using TypeScript
3. Access Modifiers example using
TypeScript
4. Building a Simple Website with
TypeScript 04 LO2
III AngularJS Perform Any 2 from the following
1. Create a simple HTML “Hello World”
Project using AngularJS Framework and
apply ng -controller, ng -model and
expressions.
2. Events and Validations in AngularJS.
(Create functions and add events, adding
HTML validators, using $valid property
of Angular, etc.)
3. Create an application for like Students
Record using AngularJS
06 LO3
IV Rich Internet
Application using
AJAX Perform Any 3 from the following
1. Write a JavaScript program for a AJAX.
2. Write a program to use AJAX for user
validation using and to show the result on
the same page below the submit button. 06 LO4

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3. Design and develop small web application
using AJAX, HTML and JSP.

V MongoDB and
Building REST API
using MongoDB Perform Any 1 from the following
1. Build a RESTful API using MongoDB.
2. Build a TypeScript REST API using
MongoDB.
04 LO5
VI Flask Perform Any 3 from the following
1. Design Feedback Form using Flask.
2. Design Weather App using Flask.
3. Design Portfolio Website using Flask.
4. Create a complete Machine learning web
application using React and Flask.
04 LO6

Text Books:

1. John Hebeler, Matthew Fisher, Ryan Blace, Andrew Perez -Lopez, “Semantic Web Programming”,
Wiley Publishing, Inc, 1st Edition, 2009.
2. Boris Cherny, “ Programming TypeScript - Making Your Javascript Application Scale”, O’Reilly
Media Inc., 2019 Edition.
3. Adam Bretz and Colin J. Ihrig, “Full Stack JavaScript Development with MEAN”, SitePoint Pty. Ltd.,
2015 Edition.
4. Simon Holmes Clive Harber, “Getting MEAN with Mongo, Express, Angular, and Node”, Manning
Publications, 2019 Edition.
5. Dr. Deven Shah, “Advanced Internet Programming”, StarEdu Solutions, 2019 Edition.
6. Miguel Grinberg, “Flask Web Development: Developing Web Applications with Python”, O’Reilly,
2018 Edition.

References:

1. John Davies, Rudi Studer and Paul Warren, “Semantic Web Technologies Trends and Research in
Ontology -based Systems”, Wiley, 2006 Edition.
2. Yakov Fain and Anton Moiseev, “TypeScript Quickly”, Manning Publications, 2020 Edition.
3. Steve Fenton, “Pro TypeScript: Application - Scale Javascript Developme nt”, Apress, 2014 Edition.
4. Brad Dayley, Brendan Dayley, Caleb Dayley, “Node.js, MongoDB and Angular Web Development:
The definitive guide to using the MEAN stack to build web applications”, 2nd Edition, Addison -
Wesley Professional, 2018 Edition.

Term Work :
Term Work shall consist of at least 10 to 12 practical’s based on the above list. Also Term Work Journal must
include at least 2 assignments.
Term Work Marks:
25 Marks (Total marks) = 15 Marks (Experiment) + 5 Marks (Assignments) + 5 Marks (Attendan ce)
Oral Exam: An Oral exam will be held based on the above syllabus.

Page 84

Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Theory Practical Total
ITL603 Sensor Lab
-- 02 -- 01 01

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract
/ Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test 2 Avg.
ITL603 Sensor Lab
-- -- -- -- -- 25 25 50

Lab Objectives:

Sr. No. Lab Objectives
The Lab experiments aims:
1 Learn various communication technologies, Microcontroller boards and sensors.
2 Design the problem solution as per the requirement analysis done using sensors and technologies.
3 Study the basic concepts of programming/sensors/ emulators.
4 Design and implement the mini project intended solution for project based earning.
5 Build, test and report the mini project successfully.
6 Improve the team building, communication and management skills of the students.

Lab Outcomes:
Sr. No. Lab Outcomes Cognitive Levels of
Attainment as per
Bloom’s Taxanomy
On successful completion, of course, learner/student will be able to:
1 Differentiate between various wireless communication technologies based on
the range of communication, cost, propagation delay, power and throughput. L1,L2

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2 Conduct a literature survey of sensors used in real world wireless
applications. L1,L2
3 Demonstrate the simulation of WSN using the Network Simulators (Contiki/
Tinker CAD/ Cup carbon etc). L1,L2,L3
4 Demonstrate and build the project successfully by hardware/sensor
requirements, coding, emulating and testing L1,L2,L3
5 Report and present the findings of the study conducted in the preferred
domain. L1,L2,L3
6 Demonstrate the ability to work in teams and manage the conduct of the
research study. L1,L2,L3
Prerequisite: Computer Networks, Microprocessor Lab.

Hardware & Software requirements:

Hardware Specifications: Software Specifications:
1.Laptop/ PC with minimum 2GB RAM and 500 GB Hard
disk drive.
2. Sensors –DHT11/22, PIR, MQ2/MQ3, HC -SR04,
Moisture sensor , Arduino Uno/Mega board, RPi Board
3. Wireless Radio Modules - Zigbee RF module, Bluetooth
Module (HC -05), Mobile Phone with Bluetooth antenna
4. Others -Breadboard, wires, power supplies, USB cables,
buzzers, LEDs, LCDs. 1. Windows or Linux Desktop OS
Arduino IDE
2.XCTU configuration and test utility
software
3. CupCa rbon IOT simulator
4. Tinkercad Simulation Software
5. Contiki/Cooja
6. Internet connection

Guidelines
A. Students should perform the following experiments:

DETAILED SYLLABUS:

Sr. No. Module Detailed Content Hours LO
Mapping
0 Prerequisite Introduction to 8086, 8051 and Python
programming 02 --
I Review of Wireless
Communication
Technologies Study of various wireless communication
technologies like IEEE 802.15.1, IEEE 802.15.4
and IEEE 802.11.

Mini Project: Allocation of the groups 02 LO1
II Sensors and their
Interfacing Study of various types of sensors and display
devices (eg. DHT -11/22, HC -SR04, MFRC 522,
PIR Sensor) and demonstration of their interfacing
using Arduino/ Raspberry pi.

Mini Project: Topic selection 02 LO2
III Wireless
Communication tools Installation and testing the simulation tools (eg.
TinkerCad/Cupcarbon/ContikiCooja).

Mini Project: Topic validation and finalizing
software and Hardware requirement.
02 LO3
IV Implementation of
Wireless Technologies Study of interfacing of Arduino/ Raspberry pi with
Wireless Technologies (eg. HC -05, XBee S2C by 02 LO4

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Digi, ESP controller).

Mini Project: Hardware procurement
V Remote Access Study of interface using Mobile/Web to publish or
remotely access the data on the Internet.
Mini Project: Study of remote access technologies
with respect to the selected project. 02 LO4
VI Mini Project Implementation of the Mini Project:
1. Design, configure, testing the Mini Project.
2. Report submission as per the guidelines. 14 LO4,LO5
,LO6
B. Mini project

1. Students should carry out hardware based mini -project in a group of three/four students with a subject In
charge/ mini project mentor associated with each group.
2. The group should meet with the concerned faculty during laboratory hours and the progress of work
discussed must be documented.
3. Each group should perform a detailed literature survey and formulate a problem statement.
4. Each group will identify the hardware and software requirement for their defined mini project problem
statement.
5. Design, configure and test their own circuit board.
5. Interface using Mobile/Web to publish or remotely access the data on the Internet.
6. A detailed report is to be prepared as per guidelines.
7. Each group may present their work in various pr oject competitions and paper presentations

C. Documentation of the Mini Project

The Mini Project Report can be made on following lines:
1. Abstract
2. Contents
3. List of figures and tables
4. Chapter -1 (Introduction, Literature survey, Problem definition, Objectives, Proposed Solution, Wireless
Technology used)
5. Chapter -2 (System design/Block diagram, Flow chart, Circuit/Interfacing diagram, Hardware and Software
requirements, cost estimation)
6. Chapter -3 (Implementation snapshots/figures with explanation, co de, future directions)
7. Chapter -4 (Conclusion)
8. References

Text Books:
1. Fundamentals of Sensor Network Programming: Applications and Technology, S.
Sitharama Iyengar, Nandan Parameshwaran, Vir V. Phoha, N. Balakrishnan, Chuka D. Okoye, Wiley
Publications.
2. ContikiCooja User Guide.
3. Building Wireless Sensor Networks, Robert Faludi, O’Reilly Publications.

Page 87

Reference Books :
1. Internet of Things (A Hands -on-Approach) , Vijay Madisetti , ArshdeepBahga.
2. A comparative review of wireless sensor netwo rk mote technologies, IEEE paper 2009.
3. Wireless Sensor Networks -Technology, Protocols and Applications, KazemSohraby, Daniel
Minoli and TaiebZnati, Wiley Publications.
4. Adhoc& Sensor Networks Theory and Applications, Carlos de MoraisCordeiro, Dharma Prakash Agrawal,
World Scientific,2nd Edition.
Online References:
Sr.
No. Website/Reference link
1. https://www.digi.com/resources/documentation/digidocs/90001526/tasks/t_download_and_install_xct
u.htm
2. https://www.arduino.cc/en/software

3. http://cupcarbon.com/

Term Work:
Term Work shall consist of Mini Project on above guidelines/syllabus. Also Term work must include at
least 2 assignments and mini project report.
Term Work Marks: 25 Marks (Total marks) = 15 Marks (Mini Project) + 5 Marks ( Assignments) + 5 Marks
(Attendance)
Oral Exam: An Oral exam will be held based on the Mini Project a nd Presentation.

Page 88

Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Theory Practical Total
ITL604 MAD & PWA
Lab -- 02 -- 01 01

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract
/ Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test 2 Avg.
ITL604 MAD & PWA
Lab -- -- -- -- -- 25 25 50

Lab Objectives:
Sr. No. Lab Objectives
The Lab experiments aims:
1 Learn the basics of the Flutter framework.
2 Develop the App UI by incorporating widgets, layouts, gestures and animation
3 Create a production ready Flutter App by including files and firebase backend service.
4 Learn the Essential technologies, and Concepts of PWAs to get started as quickly and efficiently as
possible
5 Develop responsive web applications by combining AJAX development techniques with the jQuery
JavaScript library.
6 Understand how service workers operate and also learn to Test and Deploy PWA.

Page 89

Lab Outcomes:

Sr. No. Lab Outcomes Cognitive levels of
attainment as per
Bloom’s
Taxonomy
On Completion of the course the learner/student should be able to:
1 Understand cross platform mobile application development using Flutter
framework L1, L2
2 Design and Develop interactive Flutter App by using widgets, layou ts, gestures
and animation L3
3 Analyze and Build production ready Flutter App by incorporating backend
services and deploying on Android / iOS L3, L4
4 Understand various PWA frameworks and their requirements L1, L2
5 Design and Develop a responsive User Interface by applying PWA Design
techniques L3
6 Develop and Analyse PWA Features and deploy it over app hosting solutions L3, L4
Prerequisite: HTML/HTML5, CSS3, Javascript

Hardware & Software Requirements:
Hardware
Requirement:

PC i3 processor and
above Software requirement:

JDK 8 and above, Android studio, Flutter SDK, AngularJs,
React, Vue, PWA Builder, Google Chrome Browser, Github
account.
Internet Connection

DETAILED SYLLABUS:

Sr.
No. Module Detailed Content Hours LO
Mapping

Page 90

I Basics of Flutter
Programming Introduction of Flutter, Understanding Widget
Lifecycle Events,Dart Basics, Widget Tree and
Element Tree, Basics of Flutter installation,
Flutter Hello World App. 02 LO1
II Developing Flutter
UI:Widgets,
Layouts, Gestures,
Animation USING COMMON WIDGETS: SafeArea,
Appbar, Column, Row, Container, Buttons,
Text , Richtext,Form ,Images and Icon.
BUILDING LAYOUTS : high level view of
layouts, Creating the layout, Types of layout
widgets
APPLYING GESTURES : Setting Up
GestureDetector, Implementing the Draggable
and Drag target Widgets,Using the
GestureDetector for Moving and Scaling
ADDING ANIMATION TO AN APP :Using
Animated Container,Using Animated
CrossFade,Using Animated Opacity,Using
Animation Controller, Using Staggered
Animation
CREATING AN APP’S NAVIGATION:
Usin g the Navigator,Using the Named
Navigator Route,Using the Bottom
NavigationBar,Using the TabBar and
TabBarView 06 LO2
III Creating
Production Ready
Apps Working with files : Including libraries in
your Flutter app, Including a file with your app,
Reading/Writing to files, Using JSON.
Using Firebase with Flutte r: Adding the
Firebase and Firestore Backend ,Configuring
the Firebase Project,Adding a Cloud Firestore
Database and Implementing Securit y
Testing and Deploying of Flutter
Application: Widget testing, Deploying Flutter
Apps on Android / iOS 04 LO3
IV
Introduction to
Progressive Web
App Introduction to
Progressive Web App
● Why Progressive Web App
● Characteristics of PWA
● PWAs and Hybrid Apps vs. Mobile
Apps
● PWA Requirements: HTTPS, Service
Workers, and Web App Manifest
● PWA framework tools
● Use cases 02 LO4

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V Creating
Responsive UI Creating Responsive UI
using JQuery Mobile /
Material UI / Angular UI
/ React UI
● Understanding the concept of
responsive web design
● Comparing responsive, fluid, and
adaptive web
● keys to great Progressive Web App UX
● Responsive Design – The Technicalities
● Flexible grid -based layout
● Flexible images and video
● Smart use of CSS splitting the website
behavior (media queries) 06 LO5
VI Web App Manifest
& Service Workers Web App Manifest:
Understand the basic
format and workings of the
Web App Manifest file.
● Using an App Manifest to Make your App
Installable
● Understanding App Manifest Properties
● Simulating the Web App on an Emulator
● Installing the Web App - Prerequisites
● Understanding manifest.json
Service Workers: Making
PWAs work offline with
Service workers
● Introduction to Service Workers
● Servic e Workers Lifecycle (Registration,
Installation and Activation)
● Implement Service Workers Features
(Events)
● Handling cached content
● Enabling offline functionality
● Serving push notifications
● Loading cached content for new users
● Background synchronization
● Using IndexedDB in the Service Worker
● Geo-fencing
Deploy a PWA to GitHub Pages as a free
SSL enabled static app hosting solution.
● Initialising the PWA as a Git repo
● Testing with Lighthouse
● Deploying via GitHub Pages 06 LO6
Text Books:
1. Beginning Flutter a Hands -on Guide to App Development, Marco L. Napoli, Wiley, 2020.
2. Beginning App Development with Flutter: Create Cross -Platform Mobile Apps, By Rap Payne, 201 9
3. Progressive Web Application Development by Example: Develop fast, reliable, and engaging user
experiences for the web, Packt Publishing Limited ,2018
4. Building Progressive Web Apps,O’Reilly 2017

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5. Progressive Web Apps with Angular: Create Responsive, Fast and Reliable PWAs Using Angular,
Apress; 1st ed. edition (28 May 2019)
References:
1. Flutter in Action by Eric Windmill, MANING, 2019
2. Google Flutter Mobile Development Quick Start Guide.Packt,2019
3. Learning Progressive Web Apps: Building Modern Web Apps Using Service Workers ,Addison -
Wesley Professional, 2020

Online References:
Sr. No. Website/Reference link
1. https://flutter.dev/docs/reference/tutorials
2. https://www.tutorialspoint.com/flutter/index.htm
3. https://www.javatpoint.com/flutter
4. https://www.tutorialspoint.com/jquery_mobile/jqm_panel_responsive.htm
5. https://www.w3schools.com/css/css_rwd_intro.asp
6 https://developers.google.com/web/updates/2015/12/getting -started -pwa
7 https://www.w3schools.com/react/
8 https://angular.io/docs
9 https://flaviocopes.com/service -workers/
10 https://blog.logrocket.com/how -to-build -a-progressive -web-app-pwa-with-node -js/

List of Experiments.
1. To install and configure Flutter Environment.
2. To design Flutter UI by including common widgets.
3. To create an interactive Form using form widget
4. To design a layout of Flutter App using layout widgets
5. To include icons, images, charts in Flutter app
6. To apply navigation, routing and gestures in Flutter App
7. To Connect Flutter UI with fireBase database
8. To test and deploy production ready Flutter App on And roid platform
9. To create a responsive User Interface using jQuery Mobile/ Material UI/ Angular UI/ React UI for
Ecommerce application.
10. To write meta data of your Ecommerce PWA in a Web app manifest file to enable “add to homescreen
feature”.
11. To code and reg ister a service worker, and complete the install and activation process for a new service
worker for the E -commerce PWA.
12. To implement Service worker events like fetch, sync and push for E -commerce PWA.
13. To study and implement deployment of Ecommerce PWA to GitHub Pages.
14. To use google Lighthouse PWA Analysis Tool to test the PWA functioning.
15. To deploy an Ecommerce PWA using SSL enabled static hosting solution.
Assignment 1: MAD (Any one)
1. To Study basics of Dart language and design basic Flutter App
2. To include Files and JSON data in App
3. To build interactive App by including Flutter Gestures and Animations
Assignment 2: PWA (Any one)

Page 93

1. To study the requirement for progressive web application for Ecommerce using the concept of
service worker, Webapp Manifest and framework tools
2. To Design a wireframe for simple PWA for E -commerce website
3. Case study for successful real life implementation of PWA.
Term Work:
Term Wor k shall consist of at least 10 to 12 practical’s based on the above list. Also Term Work Journal must
include at least 2 assignments as mentioned in above syllabus.
Term Work Marks: 25 Marks (Total marks) = 15 Marks (Experiment) + 5 Marks (Assignments) + 5 Marks
(Attendance)
Practical & Oral Exam: An Practical & Oral exam will be held based on the above syllabus.

Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Theory Practical Total

ITL605 DS using
Python Lab -- 02 -- 01 01

Course
Code Course Name Examination Scheme
Theory
Term
Work Pract
/ Oral Total
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test1 Test 2 Avg.

ITL605 DS using Python
Lab -- -- -- -- -- 25 25 50

Lab Objectives:

Sr. No. Lab Objectives
The Lab experiments aims:
1 To know the fundamental concepts of data science and analytics
2 To learn data collection, preprocessing and visualization techniques for data science
3 To Understand and practice analytical methods for solving real life problems based on Statistical
analysis
4 To learn various machine learning techniques to solve complex real -world problems
5 To learn streaming and batch data processing using Apache Spark
6 To map the elements of data science to perceive information

Lab Outcomes:

Sr.
No. Lab Outcomes Cognitive levels of
attainment as per
Bloom’s
Taxonomy

Page 94

On successful completion, of course, learner/student will be able to:
1 Understand the concept of Data science process and associated terminologies
to solve real -world problems L1
2 Analyze the data using different statistical techniques and visualize the
outcome using different types of plots. L1, L2, L3, L4
3 Analyze and apply the supervised machine learning techniques like
Classification, Regression or Support Vector Machine on data for building the
models of data and solve the problems. L1,L2, L3, L4
4 Apply the different unsupervised machine learning algorithms like Clustering,
Decision Trees, Random Forests or Association to solve the p roblems. L1, L2,L3
5 Design and Build an application that performs exploratory data analysis using
Apache Spark L1,L2,L3,L4,L5, L6
6 Design and develop a data science application that can have data acquisition,
processing, visualization and statistical analysis methods with supported
machine learning technique to solve the real -world problem L1,L2,L3,L4,L5, L6

Prerequisite: Basics of Python programming and Database management system.

DETAILED SYLLABUS:

Sr.
No. Module Detailed Content Hours LO
Mappin
g
I Introduction to
Data Science and
Data Processing
using Pandas i. Introduction, Benefits and uses of data science
ii. Data Science tasks
iii. Introduction to Pandas
iv. Data preparation: Data cleansing, Data
transformation, Combine/Merge /Join data, Data
loading & preprocessing with pandas
v. Data aggregation
vi. Querying data in Pandas
vii. Statistics with Pandas Data Frames
viii. Working with categorical and text data
ix. Data Indexing and Selection
x. Handling Missing Data 04 LO1
II Data Visualization
and Statistics i. Visualization with Matplotlib and Seaborn
ii. Plotting Line Plots, Bar Plots, Histograms Density
Plots, Paths, 3Dplot, Stream plot, Logarithmic plots,
Pie chart, Scatter Plots and Image visualization using
Matplotlib
iii. Plotting scatt er plot, box plot, Violin plot, swarm
plot, Heatmap, Bar Plot using seaborn
iv. Introduction to scikit -learn and SciPy
v. Statistics using python: Linear algebra, Eigen value,
Eigen Vector, Determinant, Singular Value
Decomposition, Integration, Correlation, Central
Tendency, Variability, Hypothesis testing, Anova, z -
test, t -test and chi -square test.
04 LO2
III Machine Learning i. What is Machine Learning?
ii. Applications of Machine Learning;
iii. Introduction to Supervised Learning
iv. Overview of Regression
v. Support Vector Machine
vi. Classification algorithms 05 LO3

Page 95

IV Unsupervised
Learning i. Introduction to Unsupervised Learning
ii. Overview of Clustering
iii. Decision Trees
iv. Random Forests
v. Association 05 LO4
V Data analytics
using Apache
Spark i. Introduction to Apache Spark
ii. Architecture of Apache Spark
iii. Modes and components
iv. Basics of PySpark 04 LO5
VI Case Studies i. Understanding the different data science phases used
in selected case study
ii. Implementation of Machine learning algorithm for
selected case study 04 LO1,
LO6

Text Books:
1. Jake VanderPlas, “Python Data Science Handbook”, O’Reilly publication
2. Frank Kane, “Hands -On Data Science and Python Machine Learning”, packt publication
3. M.T. Savaliya, R.K. Maurya, G.M.Magar, “Programming with Python”, 2nd Edition, Sybgen
Learning.

References:
1. Armando Fandango, “Python Data Analysis”, Second Edition, Packt publication.
2. Alberto Boschetti, Luca Massaron, “Python Data Science Essentials Second Edition”, Packt Publishing
3. Davy Cielen, Arno D. B. Meysman, Mohamed Ali, “Introducing Data Science”, Manning Publications.

Online References:
Sr. No. Website/Reference link
1. https://www.w3schools.com/python/pandas/default.asp
2. https://matplotlib.org/stable/gallery/index.html
3. . https://seaborn.pydata.org/examples/index.html
4. . https://docs.scipy.org/doc/scipy/reference/linalg.html#module -scipy.linalg
5. https://scikit -learn.org/stable/auto_examples/index.html
6 https://www.tutorialspoint.com/scipy/scipy_integrate.htm \
7 https://machinelearningmastery.com/statistical -hypothesis -tests-in-python -cheat -
sheet/
8 https://data -flair.training/blogs/data -science -project -ideas/

Suggested List of Experiments
For the following Experiments, use any available data set or download it from Kaggle/UCI or other repositories
and use Python to solve each problem.
1. Data preparation using NumPy and Pandas
a. Derive an index field and add it to the data set.
b. Find out the missing values.
c. Obtain a listing of all records that are outliers according to the any field. Print out a
listing of the 10 largest values for that fiel d.
d. Do the following for the any field.
i. Standardize the variable.
ii. Identify how many outliers there are and identify the most extreme outlier.

Page 96

2. Data Visualization / Exploratory Data Analysis for the selected data set using Matplotlib and Seaborn
a. Create a bar graph, contingency table using any 2 variables.
b. Create normalized histogram.
c. Describe what this graphs and tables indicates?
3. Data Modeling
a. Partition the data set, for example 75% of the records are included in the training data set and
25% are included in the test data set. Use a bar graph to confirm your proportions.
b. Identify the total number of records in the training data set.
c. Validate your partition by performing a two‐sample Z‐test.
4. Implementation of Statistical Hypothesis Test usi ng Scipy and Sci -kit learn [Any one]

1. Normality Tests
1. Shapiro -Wilk Test
2. D’Agostino’s K^2 Test
3. Anderson -Darling Test
2. Correlation Tests
1. Pearson’s Correlation Coefficient
2. Spearman’s Rank Correlation
3. Kendall’s Rank Correlation
4. Chi-Squared Test
3. Stationary Tests
1. Augmented Dickey -Fuller
2. Kwiatkowski -Phillips -Schmidt -Shin
4. Parametric Statistical Hypothesis Tests
1. Student’s t -test
2. Paired Student’s t -test
3. Analysis of Variance Test (ANOVA)
4. Repeated Measures ANOVA Test
5. Nonparametric Statistical Hypothesis Tests
1. Mann -Whitn ey U Test
2. Wilcoxon Signed -Rank Test
3. Kruskal -Wallis H Test
4. Friedman Test

5. Regression Analysis
a. Perform Logistic Regression to find out relation between variables.
b. Apply regression Model techniques to predict the data on above dataset

6. Classification modelling
a. Choose classifier for classification problem.
b. Evaluate the performance of classifier.
7. Clustering
a. Clustering algorithms for unsupervised classification.
b. Plot the cluster data.
8. Using any machine learning techniques using available data set to develop a recommendation
system.

9. Exploratory data analysis using Apache Spark and Pandas
10. Batch and Streamed Data Analysis using Spark
11. Implementat ion of Mini project based on following case study using Data science and Machine learning
[Any one]

Page 97

List of Case Studies
Fake News Detection Road Lane Line Detection Sentiment Analysis
Detecting Parkinson’s Disease Brain Tumor Detection with
Data Science Leaf Disease Detection
Speech Emotion Recognition Gender Detection and Age
prediction Diabetic Retinopathy
Uber Data Analysis Driver Drowsiness detection Chatbot Project
Credit Card Fraud Detection Movie/ Web Show
Recommendation System Customer Segmentation
Cancer Classification Traffic Signs Recognition Exploratory Data Analysis for
Housing price prediction
Coronavirus visualizations Visualizing climate change Predictive policing
Uber’s pickup analysis Earth Surface Temperature
Visualization Web traffic forecasting using
time series
Pokemon Data Exploration
Impact of Climate Change on
Global Food Supply Used Car Price Estimator
Skin Cancer Image Detection World University Rankings and so on ….

Assignments:

1) Recent trends in Data science
2) Comparative analysis between Batch and Streamed data processing tools like Map -reduce, Apache spark,
Apache Flink, Apache Samza, Apache Kafka and Apache Storm.

Term Work:

• Term work shall consist of at least 10 experiments and a case study.
• Journal must include 2 assignments.
• The final certification and acceptance of term work indicates that performance in laboratory work is
satisfactory and minimum passing marks may be given in term work.
• The distribution of marks for term work shal l be as follows:
• Laboratory work (Experiments) ………… (15) Marks.
• Mini project (Implementation) ………… . (05) Marks.
• Attendance………………………………. (05) Marks
TOTAL:………………………………….(25) Marks.
Oral examination will be based on Lab oratory work, mini project and abo ve syllabus.

Page 98

Course Code
Course
Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ITM6 01 Mini Project
– 2 B Web
Based on
ML -- 04 -- -- 02 -- 02

Course
Code
Course
Name Examination Scheme
Theory Marks
Term Work Pract. /Oral Total Internal assessment End
Sem.
Exam Test1 Test 2 Avg.
ITM6 01 Mini Project
– 2 B Based
on ML -- -- -- -- 25 25 50

Course Objectives
5. To acquaint with the process of identifying the needs and converting it into the problem.
6. To familiarize the process of solving the problem in a group.
7. To acquaint with the process of applying basic engineering fundamentals to attempt solutions to the
problems.
8. To inc ulcate the process of self -learning and research.
Course Outcome: Learner will be able to…
10. Identify problems based on societal /research needs.
11. Apply Knowledge and skill to solve societal problems in a group.
12. Develop interpersonal skills to work as memb er of a group or leader.
13. Draw the proper inferences from available results through theoretical/ experimental/simulations.
14. Analyse the impact of solutions in societal and environmental context for sustainable development.
15. Use standard norms of engineering practices
16. Excel in written and oral communication.
17. Demonstrate capabilities of self -learning in a group, which leads to life long learning.
18. Demonstrate project management principles during project work.

Page 99

Guidelines for Mini Project
 Students shall form a group of 3 to 4 students, while forming a group shall not be allowed less than
three or more than four students, as it is a group activity.
 Students should do survey and identify needs, which shall be converted into problem statement for
mini project in c onsultation with faculty supervisor/head of department/internal committee of faculties.
 Students hall submit implementation plan in the form of Gantt/PERT/CPM chart, which will cover
weekly activity of mini project.
 A log book to be prepared by each group, wherein group can record weekly work progress,
guide/supervisor can verify and record notes/comments.
 Faculty supervisor may give inputs to students during mini project activity;however, focus shall be on
self-learning.
 Students in a group shall understand problem effectively, propose multiple solution and select best
possible solution in consultation with guide/ supervisor.
 Students shall convert the best solution into working model using various components of t heir domain
areas and demonstrate.
 The solution to be validated with proper justification and report to be compiled in standard format of
University of Mumbai.
 With the focus on the self -learning, innovation, addressing societal problems and entrepreneurs hip
quality development within the students through the Mini Projects, it is preferable that a single project
of appropriate level and quality to be carried out in two semesters by all the groups of the students. i.e.
Mini Project 1 in semester III and IV. Similarly, Mini Project 2 in semesters V and VI.
 However, based on the individual students or group capability, with the mentor’s recommendations, if
the proposed Mini Project adhering to the qualitative aspects mentioned above gets completed in odd
seme ster, then that group can be allowed to work on the extension of the Mini Project with suitable
improvements/modifications or a completely new project idea in even semester. This policy can be
adopted on case by case basis.
Guidelines for Assessment of Min i Project:
Term Work
 The review/ progress monitoring committee shall be constituted by head of departments of each
institute. The progress of mini project to be evaluated on continuous basis, minimum two reviews
in each semester.
 In continuous assessment f ocus 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 for both semesters shall be as below;
o Marks awarded by guide/supe rvisor based on log book : 10
o Marks awarded by review committee : 10
o Quality of Project report : 05

Review/progress monitoring committee may consider following points for assessment based on either
one year or half year project as mentioned in general guidelines.
One-year project:
 In first semester entire theoretical solution shall be ready, including components/system selection
and cost analysis. Two reviews will be conducted based on presentation given by students group.
 First shall be for finalisation of problem
 Second shall be on finalisation of proposed solution of problem.
 In second semester expected work shall be procurement of component’s/systems, building of
working prototype, testing and validation of results bas ed on work completed in an earlier
semester.
 First review is based on readiness of building working prototype to be conducted.
 Second review shall be based on poster presentation cum demonstration of working
model in last month of the said semester.

Page 100

Half -year project:
 In this case in one semester students’ group shall complete project in all aspects including,
o Identification of need/problem
o Proposed final solution
o Procurement of components/systems
o Building prototype and testing
 Two reviews will be conducted for continuous assessment,
 First shall be for finalisation of problem and proposed solution
 Second shall be for implementation and testing of solution.

Assessment criteria of Mini Project.

Mini Project shall be assessed based on following crite ria;
14. Quality of survey/ need identification
15. Clarity of Problem definition based on need.
16. Innovativeness in solutions
17. Feasibility of proposed problem solutions and selection of best solution
18. Cost effectiveness
19. Societal impact
20. Innovativeness
21. Cost effect iveness and Societal impact
22. Full functioning of working model as per stated requirements
23. Effective use of skill sets
24. Effective use of standard engineering norms
25. Contribution of an individual’s as member or leader
26. Clarity in written and oral communication

 In one year, project , first semester evaluation may be based on first six criteria’s and remaining
may be used for second semester evaluation of performance of students in mini project.
 In case of half year project all criteria’s in generic may be considered for evaluation of
performance of students in mini project.
Guidelines for Assessment of Mini Project Practical/Oral Examination:
 Report should be prepared as per the guidelines issued by the University of Mum bai.
 Mini Project shall be assessed through a presentation and demonstration of working model by the
student project group to a panel of Internal and External Examiners preferably from industry or
research organisations having experience of more than five years approved by head of Institution.
 Students shall be motivated to publish a paper based on the work in Conferences/students competitions.

Mini Project shall be assessed based on following points;
9. Quality of problem and Clarity
10. Innovativeness in solut ions
11. Cost effectiveness and Societal impact
12. Full functioning of working model as per stated requirements
13. Effective use of skill sets
14. Effective use of standard engineering norms
15. Contribution of an individual’s as member or leader
16. Clarity in written and oral communication

Page 101

Course Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Theory Practical Total
ITDO6011 Software
Architecture 03 -- 03 -- 03

Course
Code Course
Name Examination Scheme
Theory Marks
Term
Work Practical Oral Total Internal assessment End
Sem.
Exam Test
1 Test 2 Avg. of 2
Tests
ITDO601
1 Software
Architecture 20 20 20 80 -- -- -- 100

Course Objectives:

Course Outcomes:
Sr.
No. Course Outcomes Cognitive levels
of attainment as
per Bloom’s
Taxonomy Sr. No. Course Objectives
The course aims:
1 To understand the importance of architecture in building effective, efficient, competitive
software products.
2 To understand the need, design approaches for software architecture to bridge the dynamic
requirements and implementation
3 To learn the design principles and to apply for large scale systems including distributed,
network and heterogeneous systems
4 To understand principal design decisions governing the system.
5 To understand different notations used for capturing design decisions.
6 To understand different functional and non -functional properties of complex software systems.

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On successful completion, of course, learner/student will be able to:
1 Understand the need of software architecture for sustainable dynamic systems. L1
2 Have a sound knowledge on design principles and to apply for large scale systems. L2
3 Apply functional and non -functional requirements L1,L2, L3
4 Design architectures for distributed, network and heterogeneous systems L1,L2, L3
5 Have good knowledge on service oriented and model driven architectures and the
aspect -oriented architecture. L1,L2, L3
6 Have a working knowledge to develop appropriate architectures through various
case studies. L1,L2, L3

Prerequisite : Software Engineering , Any Programming Language

DETAILED SYLLABUS:

Sr. No. Module Detailed Content Hours CO
Mapping
0 Prerequisite Software Engineering Concepts, Knowledge of Any
programming Language 02 CO1
I Basic Concepts
and
Architectures
Design Terminology, Models, Processes, Stakeholders, Design
Process, Architectural Conceptions, Styles and
architectural Patterns, Architectural conceptions in
absences of experience, connectors, 4+1 view model of
Architecture

Self Learning Topics : Technical Paper
“What_is_included_in_software_architectur” 07 CO1
II Architectural
Modeling and
Analysis Modeling Concepts, Ambiguity, Accuracy and Precisions,
Complex Modeling, Evaluati ng Modeling Techniques,
Specific Modeling Techniques, Analysis Goals, Scope of
Analysis, Formality of Architectural Models, Types of
Analysis, Level of Automation, System Stakeholders,
Analysis Techniques

Self Learning Topics: Technical Paper “Specificat ion of
Requirements and Software Architecture for the
Customisation of Enterprise Software” 09 CO1, CO2
III Implementation,
Deployment and
Mobility Implementation Concepts, Existing Frameworks, Overview
of Deployment and Mobility Challenges, Software
Architecture and Deployment, Software Architecture and
Mobility

Self Learning Topics: Technical Paper”Application of
Distributed System in Neuroscience: A Case Study of BCI
Framework” 06 CO1, CO2
IV Applied
Architectures
and Styles Distributed and Network Architectures, Architectures for
Network Based Applications, Decentralized Architectures,
Service oriented Architectures and Web Services.

Self Learning Topics: Technical Paper “Analysing the
Behaviour of Distributed Software Architectures: a Case
Study” 06 CO1,
CO2,
CO3

Page 103

V Designing for
Non-Functional
Properties Efficiency, Complexity, Scalability and Heterogeneity,
Adaptability, Dependability

Self Learning Topics: Technical Paper “Threat -
Modeling -in-Agile -Software -Development” 04 CO1,CO2,
CO4,
CO6
VI Domain -
Specific
Software
Engineering Domain -Specific Software Engineering, Domain - Specific
Architecture, Software Architects Roles

Self Learning Topics : Research Paper “A Case Study of
the Variability Consequences of the CQRS” 05 CO1,CO2,
CO3

Text Books:

1. Software Architecture, Foundations, Theory, and Practise, Richard Taylor, Nenad Medvidovic, Eric M
Dashofy, Wiley Student Edition.
2. The Art of Software Architecture: Design Methods and Techniques, Stephen T.Albin, Wiley India Private
Limited.
3. Software Architecture in Practice by Len Bass, Paul Clements, Rick Kazman, Pearson

References:

1. DevOps A Software Architect’s Perspective, Len Bass, Ingo Weber, Liming Zhu, Addison Wesley
2. Essentials of Software Architecture, Ion Gorton, Second Edition, Springer -verlag, 2011

Online Resources :

1. ArchStudio Software
2. https://www.coursera.org/learn/software -architecture
3. https://www.coursera.org/specializations/sof tware -design -architecture
4. https://resources.sei.cmu.edu/library/asset -view.cfm?assetid=509483
5. http://infolab.stanford.edu/~backrub/google.html
6. https://web.njit.edu/~alexg/courses/cs345/OLD/F15/solutions/f3345f15.pdf
Assessment:
Internal Assessment (IA) for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to 50% of
syllabus content must be covered in First IA Test and remaining 40% to 50% of syllabus content
must be covered in Second IA Tes t
 Question paper format
 Question Paper will comprise of a total of six questions each carrying 20 marksQ.1 will be
compulsory and should cover maximum contents of the syllabus
 Remaining questions will be mixed in nature (part (a) and part (b) of each question must be
from different modules. For example, if Q.2 has part (a) from Module 3 then part (b) must be
from any other Module randomly selected from all the modules)
 A total of four questions need to be answered

Page 104

Course Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Theory Practical Total
ITDO6012 Image
Processing 03 -- 03 -- 03

Course
Code Course
Name Examination Scheme
Theory Marks
Term
Work Practical Oral Total
Internal assessment End
Sem.
Exam
Test1 Test 2 Avg.
ITDO6012 Image
Processing 20 20 20 80 -- -- -- 100

Course Objectives:

Course Outcomes: Sr. No. Course Objectives
The course aims:
1 Define image and its formation and debate about the roles of image processing in today's world
and also introduce students to the major research domains in the field of image processing.
2 Describe point, mask and histogram processing units of image enhancements that can be
applied on a given image for im proving the quality of digital image required for an application.
3 Explain the forward and reverse discrete image transforms and discuss the selection of the
image transform used for enhancement, compression, or representation and description.
4 Make students understand the impacts and effects of image compression techniques over a
given bandwidth to learn how effectively storage and retrieval can be achieved using lossy and
lossless compression methods.
5 Describe and demonstrate the proper procedure for segmenting images, and demonstrate how
the image object can be described using image representation techniques.
6 Illustrate how to shape and reshape a given object in an image using morphological techniques
over binary and gray scale images.

Page 105

Sr. No. Course Outcomes Cognitive
levels of
attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Define image and explain formation of image and recall its types and calculate
image parameters by reading images using a programming language. L1
2 Apply and differentiate point, mask and histogram processing techniques
suitable for enhancing images required for an application. L1,L2, L3
3 List and calculate discrete image transform coefficients and use it for
enhancement, compression and representation. L1,L2, L3
4 Compute compression ratio and fidelity criteria to evaluate and compare
method efficiency and classify compression techniques into lossless and lossy
methods. L1,L2, L3, L4
5 Apply the segmentation techniques to highlight and select the region of
interest and determine and describe using chain code, shape number and
moments for representing objects in an image. L1,L2, L3
6 Choose structuring elements and apply morphological operations to find a
suitable shape for an object in the image. L1,L2, L3
Prerequisite: Digital Signal Processing.

DETAILED SYLLABUS:

Sr. No. Module Detailed Content Hours CO
Mapping
0 Prerequisite Digital Signal Processing, Matrix Multiplication. 01
I Introduction to
Image Processing Image Fundamentals: Image Definition, Steps and
Components of Image Processing, Image Sensing and
Acquisition, Image Sampling and Quantization.
Relationship Between Pixels: Adjacency, Connectivity
and Distance.

Self-Learning Topics: Different Image File Formats and
Types of noise in image. 04 CO1
II Image
Enhancement Point Processing Techniques : Image Negative, Bit Plane
Slicing, Gray Level Slicing, Contrast Stretching, Clipping,
Thresholding, Dynamic Range Compression.
Mask Processing Techniques: Filtering in Spatial
Domain, Average Filter, Weighted Average Filter, Order
Statistic Filter: Min, Max, Median Filter.
Histogram Processing: Histogram Equalization and
Specifica tion.

Self-Learning Topics: Application of Image Enhancement
in Spatial Domain. 08 CO2
III Image
Transforms Discrete Fourier Transform: Transform Pair, Transform
Matrix, Properties, Filtering in Frequency Domain.
Other Discrete Transforms: Discrete Cosine Transform,
Discrete Hadamard Transform, Discrete Walsh,
Transform, Discrete Haar Transform. 07 CO3

Page 106

Self-Learning Topics: Application of Transforms in
Steganography and CBIR.
IV Image
Compression Entropy, Redundancy and Types, Compression Ratio,
Compression Methods.
Lossless Compression: Run-Length Encoding, Huffman
Coding, Arithmetic Coding, LZW Coding, Lossless
Predictive coding.
Lossy Compression : Fidelity Criterion, Improved Gray
scale Quantiz ation , Symbol -Based Coding, Bit -Plane
Coding, Vector Quantization.

Self-Learning Topics: DPCM, Block Transform Coding,
JPEG compression. 07 CO4
V Image
Segmentation and
Representation Image Segmentation: Point, Line and Edge Detections
Methods, Hough Transform, Graph Theoretic Method,
Region Based Segmentation.
Image Representation: Chain Codes, Shape Number,
Polygon Approximation, Statistical Moments.

Self-Learning Topics: Fourier Descriptors, Otsu
Thresholding, Application in Number Plate Recognition. 07 CO5
VI Morphological
Image Processing Basic Morphological Methods: Erosion, Dilation,
Opening, Closing, Hit -or-Miss Transformation.
Advanced Morphological Methods: Skeletonization,
Thinning, Thickening, Pruning, Boundary Extraction.

Self-Learning Topics: Gray Scale Morphology: Erosion
and Dilation. 05 CO6
Text Books:

1. Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Addison - Wesley Publishing
Company, 3e, 2007.
2. William K. Pratt, “Digital Image Processing”, John Wiley, 4e, 2007.
3. S. Jayaraman, S. Esakkirajan and T. Veerakumar, “Digital Image Processing”, MGH Publication,
2016.
References:

1. Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing using MATLAB,” Pearson
Education.
2. J. G. Proakis and D. G. Manolakis, “Digital Signal processing Principles, Algorithms and
Applications,” PHI Publications, 3e.
3. Anil K. Jain, “Fundamentals of Digital Image Processing,” PHI, 1995.
4. Milan Sonka, “D igital Image Processing and Computer Vision,” Thomson publication, Second
Edition.2007.
5. Kenneth R. Castleman, “Digital Image Processing,” PHI, 1996.
6. S. Sridhar, “Digital Image Processing,” Oxford University Press, 2e, 2016.
Assessment:
Internal Assessment (IA) for 20 marks:

Page 107

 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to 50% of
syllabus content must be covered in First IA Test and remaining 40% to 50% of syllabus content
must be covered in Second IA Test
 Question paper for mat
 Question Paper will comprise of a total of six questions each carrying 20 marksQ.1 will be
compulsory and should cover maximum contents of the syllabus
 Remaining questions will be mixed in nature (part (a) and part (b) of each question must be
from dif ferent modules. For example, if Q.2 has part (a) from Module 3 then part (b) must be
from any other Module randomly selected from all the modules)
 A total of four questions need to be answered

Course Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Theory Practical Total
ITDO6013 Green IT 03 -- 03 -- 03

Course
Code Course
Name Examination Scheme
Theory Marks
Term
Work Practical Oral Total Internal assessment End
Sem.
Exam Test1 Test 2 Avg.
ITDO6013 Green IT 20 20 20 80 -- -- -- 100

Course Objectives:

Course Outcomes:

Sr.
No. Course Outcomes Cognitive levels of
attainment as per
Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to: Sr. No. Course Objectives
The course aims:
1 To understand what Green IT is and How it can help improve environmental Sustainability
2 To understand the principles and practices of Green IT.
3 To understand how Green IT is adopted or deployed in enterprises.
4 To understand how data centres, cloud computing, storage systems, software and networks can
be made greener.
5 To measure the Maturity of Sustainable ICT world.
6 To implement the concept of Green IT in Information Assurance in Communication and Social
Media and all other commercial field.

Page 108

1 Describe awareness among stakeholders and promote green agenda and green
initiatives in their working environments leading to green movement L1
2 Identify IT Infrastructure Management and Green Data Centre Metrics for software
development L1,L2
3 Recognize Objectives of Green Network Protocols for Data communication. L1,L2
4 Use Green IT Strategies and metrics for ICT development. L1,L2, L3
5 Illustrate various green IT services and its roles. L1,L2
6 Use new career opportunities available in IT profession, audits and others with
special skills such as energy efficiency, ethical IT assets disposal, carbon footprint
estimation, reporting and development of green products, applications and
services. L1,L2, L3

Prerequisite: Environmental Studies

DETAILED SYLLABUS:

Sr.
No. Module Detailed Content Hours CO
Mapping
0 Prerequisite Environmental Studies 2
I Introduction Environmental Impacts of IT, Holistic Approach to
Greening IT, Green IT Standards and Eco-Labeling,
Enterprise Green IT Strategy
Hardware: Life Cycle of a Device or Hardware, Reuse,
Recycle and Dispose
Software: Introduction, Energy -Saving Software
Techniques

Self learning Topics: Evaluating and Measuring Software
Impact to Platform Power 7 CO 1
II Software
development and
data centers Sustainable Software, Software Sustainability Attributes,
Software Sustainability Metrics
Data Centres and Associated Energy Challenges, Data
Centre IT Infrastructure, Data Centre Facility
Infrastructure: Im plications for Energy Efficiency, Green
Data Centre Metrics

Self-learning Topics: Sustainable Software: A Case
Study, Data Centre Management Strategies: A Case Study 7 CO 1
CO 2
III Data storage and
communication Storage Media Power Characteristics, Energy
Management Techniques for Hard Disks
Objectives of Green Network Protocols, Green Network
Protocols and Standards

Self learning Topics: System -Level Energy Management 6 CO 1
CO 3
IV Information
systems, green it
strategy and
metrics Approaching Green IT Strategies, Business Drivers of
Green IT Strategy
Multilevel Sustainable Information, 6 CO 1
CO 4

Page 109

Sustainability Hierarchy Models, Product Level
Information, Individual Level Information, Functional
Level Information, Measuring the Maturity of Sustainable
ICT: A Capability Maturity Framework for SICT,
Defining the Scope and Goal, Capability Maturity Levels

Self learning Topics: Business Dimensions for Green IT
Transformation
V Green IT services
and roles Factors Driving the Development of Sustainable IT,
Sustainable IT Services (SITS), SITS Strategic Framework
Organizational and Enterprise Greening, Information
Systems in Greening Enterprises, Greening the Enterprise:
IT Usage and Hardware

Self learning Topics: Inter -organizational Enterpr ise
Activities and Green Issues, Enablers and Making the Case
for IT and the Green Enterprise 6 CO 1
CO 4
CO 5
VI Managing and
regulating green
IT Strategizing Green Initiatives, Implementation of Green
IT, Communication and Social Media
The Regulatory Environment and IT Manufacturers,
Nonregulatory Government Initiatives, Industry
Associations and Standards Bodies, Green Building
Standards, Social Movements and Greenpeace.

Self learning Topics: Information Assurance, Green Data
Centers, Case Study: Man aging Green IT 5 CO 1
CO 5
CO 6

Text Books:

1. San Murugesan, G. R. Gangadharan, Harnessing Green IT, WILEY 1st Edition -2013
2. Mohammad Dastbaz Colin Pattinson Babak Akhgar, Green Information Technology A Sustainable
Approach, Elsevier 2015
3. Reinhold, Carol Baroudi, and Jeffrey HillGreen IT for Dummies, Wiley 2009

References:

1. Mark O'Neil, Green IT for Sustainable Business Practice: An ISEB Foundation Guide, BCS
2. Jae H. Kim, Myung J. Lee Green IT: Technologies and Applications, Springer, ISBN: 978 -3-642-22178 -1
3. Elizabeth Rogers, Thomas M. Kostigen The Green Book: The Everyday Guide to Saving the Planet One
Simple Step at a Time, Springer

Assessment:
Internal Assessment (IA) for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to 50% of
syllabus content must be covered in First IA Test and remaining 40% to 50% of syllabus content
must be covered in Second IA Test
 Question paper format
 Question Paper will comprise of a total of six questions each carrying 20 mar ksQ.1 will be
compulsory and should cover maximum contents of the syllabus
 Remaining questions will be mixed in nature (part (a) and part (b) of each question must be
from different modules. For example, if Q.2 has part (a) from Module 3 then part (b) must be
from any other Module randomly selected from all the modules)
 A total of four questions need to be answered

Page 110

Course Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Theory Practical Total
ITDO6014 Ethical
Hacking and
Forensics 03 -- 03 -- 03

Course
Code Course
Name Examination Scheme
Theory Marks
Term
Work Practical Oral Total Internal assessment End
Sem.
Exam Test1 Test 2 Avg.
ITDO6014 Ethical
Hacking and
Forensics 20 20 20 80 -- -- -- 100

Course Objectives:

Course Outcomes: Sr. No. Course Objectives
The course aims:
1 To understand the concept of cybercrime and principles behind ethical hacking.
2 To explore the fundamentals of digital forensics, digital evidence and incident response.
3 To learn the tools and techniques required for computer forensics.
4 To understand the network attacks and tools and techniques required to perform network
forensics.
5 To learn how to investigate attacks on mobile platforms.
6 To generate a forensics report after investigation.

Page 111

Sr. No. Course Outcomes Cognitive levels of
attainment as per
Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Define the concept of ethical hacking. L1
2 Recognize the need of digital forensics and define the concept of digital
evidence and incident response. L1,L2
3 Apply the knowledge of computer forensics using different tools and
techniques. L1,L2, L3
4 Detect the network attacks and analyze the evidence. L1, L2,L3, L4
5 Apply the knowledge of computer forensics using different tools and
techniques. L1,L2, L3
6 List the method to generate legal evidence and supporting investigation
reports L1,L2

Prerequisite: Computer Networks, Computer Network Security

DETAILED SYLLABUS:

Sr.
No. Module Detailed Content Hours CO
Mapping
0 Prerequisite Computer Networks, Computer Network Security 01 --
I Cybercrime and
Ethical Hacking Introduction to Cybercrime, Types of Cybercrime,
Classification of Cybercriminals, Role of computer
in Cybercrime, Prevention of Cybercrime.
Ethical Hacking, Goals of Ethical Hacking, Phases
of Ethical Hacking, Difference between Hackers,
Crackers and Phreakers, Rules of Ethical Hacking.
Self Learning T opics : exploring various online
hacking tools for Reconnaissance and scanning
Phase. 06 CO1
II Digital Forensics
Fundamentals Introduction to Digital Forensics, Need and
Objectives of Digital Forensics, Types of Digital
Forensics, Process of Digital Forensics, Benefits of
Digital Forensics, Chain of Custody, Anti
Forensics.
Digital Evidence and its Types, Rules of Digital
Evidences.
Incident Response, Methodology of Incident
Response, Roles of CSIRT in handling incident.
Self Learning Topics: Pre Inci dent preparation and
Incident Response process 06 CO2
III Computer
Forensics Introduction to Computer Forensics, Evidence
collection (Disk, Memory, Registry, Logs etc),
Evidence Acquisition, Analysis and
Examination(Window, Linux, Email, Web,
Malware) , C hallenges in Computer Forensics,
Tools used in Computer Forensics. 08 CO3

Page 112

Self Learning Topics: Open source tool for Data
collection & analysis in windows or Unix
IV Network
Forensics Introduction, Evidence Collection and Acquisition
(Wired and Wireless), Analysis of network
evidences(IDS, Router,), Challenges in network
forensics, Tools used in network forensics.
Self Learning Topics: IDS types and role of IDS
in attack prevention 08 CO4
V Mobile Forensics Introduction, Evidence Collection and Acquisition,
Analysis of Evidences, Challenges in mobile
forensics, Tools used in mobile forensics
Self Learning Topics : Tools / Techniques used in
mobile forensics 06 CO5
VI Report
Generation Goals of Report, Layout of an Investigative Report,
Guidelines for Writing a Report, sample for writing
a forensic report.
Self Learning Topics : For an incident write a
forensic report. 04 CO6

Text Books:

1. John Sammons, “The Basics of Digital Forensics: The Premier for Getting Started in Digital Forensics”, 2nd
Edition, Syngress, 2015.
2. Nilakshi Jain, Dhananjay Kalbande, “Digital Forensic: The fascinating world of Digital Evidences” Wiley
India Pvt Ltd 2017.
3. Jason Luttgens, Matthew Pepe, Kevin Mandia, “Incident Response and computer forensics”,3rd Edition Tata
McGraw Hill, 2014.

References:

1. Sangita Chaudhuri, Madhumita Chatterjee, “Digital Forensics”, Staredu, 2019.
2. Bill Nelson,Amelia Phillips,Christopher Steuart, “Guide to Computer Forensics and Investigations”
Cengage Learning, 2014.
3. Debra Littlejohn Shinder Michael Cross “Scene of the Cybercrime: Computer Forensics Handbook”, 2nd
Edition Syngress Publishing, Inc.2008.

Assessment:
Internal Assessment (IA) for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximat ely 40% to 50% of
syllabus content must be covered in First IA Test and remaining 40% to 50% of syllabus content
must be covered in Second IA Test
 Question paper format
 Question Paper will comprise of a total of six questions each carrying 20 marksQ.1 will be
compulsory and should cover maximum contents of the syllabus
 Remaining questions will be mixed in nature (part (a) and part (b) of each question must be
from different modules. For example, if Q.2 has part (a) from Module 3 then part (b) must be
from a ny other Module randomly selected from all the modules)
 A total of four questions need to be answered