MU FYBSC CS SYLLABUS 2021 1 Syllabus Mumbai University

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Page 2 of 50
UNIVERSITY OF MUMBAI
Syllabus
For the
Program: F.Y.B.Sc. Sem -I &II CBCS
Course: Computer Science
(Choice Based and Credit System with effect from the
academic year 2021 -22) AC – 29/06/2021
Item No: 6.38

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Page 3 of 50 AC – 29/06/2021
Item No: 6.38

UNIVERSITY OF MUMBAI



Syllabus for Approval
Sr. No. Heading Particulars
1. Title of the Course F.Y.B.Sc. Sem. I & II
(Computer Science)
2. Eligibility for
Admission Ordinance no. O.5719
Circular no. UG/284 of 2007 dated 16th
June 2007
3. Passing Marks 40%
4. Ordinances /
Regulations (if, any) As applicable for all B.Sc. Courses
5. Number of years /
Semesters Three years – Six Semesters
6. Level P.G./ U.G. / Diploma / Certificate
(Strike out which is not applicable)
7. Pattern Yearly / Semester, Choice Based
(Strike out which is not applicable)
8. Status New /Revised
9. To be implemented
from Academic year From the Academic Year 2021 – 2022

Date: 28/06/2021
Dr. Jagdish Bakal Dr. Anuradha Majumdar
BoS Chairperson in Computer Science Dean, Science and Technology

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Page 4 of 50 Preamble

The rise of Information and Communication Technology (ICT) has profoundly affected modern society.
Increasing applications of computers in almost all areas of human endeavor has led to vibrant industries
with concurrent rapid change in technology.
As the computing field advances at a rapid pace, the students must possess a solid foundation that allows
and encourages them to maintain relevant skills as the field evolves. Specific languages and technology
platforms change over time. Thus students must continue to learn and adapt their skills throughout their
careers. To develop this ability, students will be exposed to multiple programming languages, tools,
paradigms and technologies as well as the fundamental underlying principles throughout this prog ramme.
The programme offers required courses such as programming languages, data structures, computer
architecture and organization, algorithms, database systems, operating systems, and software engineering;
as well as specialized courses in artificial int elligence, computer -based communication networks,
distributed computing, information security, graphics, human -computer interaction, multimedia, scientific
computing, web technology, and other current topics in computer science.
The core philosophy of this programme is to –
Form strong foundations of Computer Science
Nurture programming, analytical & design skills for the real world problems.
Introduce emerging trends to the students in gradual way.
Groom the students for the challenges of ICT indus try
The students these days not only aspire for a career in the industry but also look for research
opportunities. The main aim of this programme is to deliver a modern curriculum that will equip
graduates with strong theoretical and practical backgrou nds to enable them to excel in the workplace and
to be lifelong learners. Not only does it prepare the students for a career in Software industry, it also
motivates them towards further studies and research opportunities. Graduating students, can thus tak e up
postgraduate programmes in CS leading to research as well as R&D, can be employable at IT industries,
or can adopt a business management career.
In the first year i.e. for semester I & II, basic foundation of important skills required for software
development is laid. The syllabus proposes to have four core subjects of Computer science and two core
courses of Mathematics -Statistics. All core subjects are proposed to have theory as well as practical
tracks. While the Computer Science courses will f orm fundamental skills for solving computational
problems, the Mathematics & Statistics course will inculcate research -oriented acumen. Ability
Enhancement Courses on Soft Skill Development will ensure an overall and holistic development of the
students. T he syllabus design for further semesters encompasses more advanced and specialized courses
of Computer Science.
We sincerely believe that any student taking this programme will get very strong foundation and exposure
to basics, advanced and emerging trend s of the subject. We hope that the students‟ community and
teachers‟ fraternity will appreciate the treatment given to the courses in the syllabus.
We wholeheartedly thank all experts who shared their valuable feedbacks and suggestions in order to
improvise the contents; we have sincerely attempted to incorporate each of them. We further thank
Chairperson and members of Board of Studies for their confidence in us.
Special thanks to Department of Computer Science and colleagues from various colleges , who
volunteered or have indirectly, helped designing certain specialized courses and the syllabus as a whole.

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Page 5 of 50 Programme Structure for B.Sc. Computer Science


Programme Duration 06 Semesters
spread across 3 years
Total Credits required for successful completion of the Course 120
Credits required from the Core Courses 76
Credits required for the Ability Enhancement Courses 04
Credits required for Skills Enhancement Courses 32
Credits for General Elective Courses 08
Minimum Attendance per Semester 75%



Progamme Objectives

The objectives of the 3 year B.Sc. Computer Science programme are as follows:

To develop an understanding and knowledge of the basic theory of Computer Science
with good foundation on theory, systems and applications.

To fosternecessary skills and analytical abilities for developing computer based solutions
of real -life problems.

To provide training in emergent computing technologies which lead to innovative
solutions for industry and academia.

To develop the necessary study skills and knowledge to pursue further post -graduate
study in computer science or other related fields.

To develop the professional skillset required for a career in an information technology
oriented business or industry.
To enable students to work independently and collaboratively, communicate effectively,
and become responsible, competent, confident, insightful, and creative users of
computing technology

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Page 6 of 50 Progamme Learning Outcomes

At the end of three year Bachelo r of Computer Science the students will be able:

To formulate, to model, to design solutions, procedure and to use software tools to solve
real world problems.

To design and develop computer programs/computer -based systems in the areas such as
networking, web design, security, cloud computing, IoT, data science and other emerging
technologies.

To familiarize with the modern -day trends in industry and research b ased settings and
thereby innovate novel solutions to existing problems.

To apply concepts, principles, and theories relating to computer science to new situations.

To use current techniques, skills, and tools necessary for computing practice

To a pply standard Software Engineering practices and strategies in real -time software
project development

To pursue higher studies of specialization and to take up technical employment.

To work independently or collaboratively as an effective tame member on a substantial
software project.

To communicate and present their work effectively and coherently.

To display ethical code of conduct in usage of Internet and Cyber systems.

To e ngage in independent and life -long learning in the background of rapid changing IT
industry.

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Page 7 of 50




Academic year 2021 -2022


Semester – I
Course
Code Course Type Course Title Credits Lectures/Week
USCS101 Core Subject Digital Systems & Architecture 2 3
USCSP101 Core Subject
Practical Digital Systems & Architecture – Practical 1 3
USCS102 Core Subject Introduction to Programming with Python 2 3
USCSP102 Core Subject
Practical Introduction to Programming with Python –
Practical 1 3
USCS103 Core Subject LINUX Operating System 2 3
USCSP103 Core Subject
Practical LINUX Operating System – Practical 1 3
USCS104 Core Subject Open Source Technologies 2 3
USCSP104 Core Subject
Practical Open Source Technologies – Practical 1 3
USCS105 Core Subject Discrete Mathematics 2 3
USCSP105 Core Subject
Practical Discrete Mathematics – Practical 1 3
USCS106 Core Subject Descriptive Statistics 2 3
USCSP106 Core Subject
Practical Descriptive Statistics – Practical 1 3

USCS107 Ability
Enhancement
Course
Soft Skills
2
3

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Page 8 of 50 F.Y.B.Sc. Computer Science Syllabus
Choice Based Credit System (CBCS)
with effect from

Academic year 2021 -2022

Semester – II
Course
Code Course Type Course Title Credits Lectures/Week
USCS201 Core Subject Design & Analysis of Algorithms 2 3
USCSP201 Core Subject
Practical Design & Analysis of Algorithms –
Practical 1 3
USCS202 Core Subject Advanced Python Programming 2 3
USCSP202 Core Subject
Practical Advanced Python Programming –
Practical 1 3
USCS203 Core Subject Introduction to OOPs using C++ 2 3
USCSP203 Core Subject
Practical Introduction to OOPs using C++ –
Practical 1 3
USCS204 Core Subject Database Systems 2 3
USCSP204 Core Subject
Practical Database Systems – Practical 1 3
USCS205 Core Subject Calculus 2 3
USCSP205 Core Subject
Practical Calculus – Practical 1 3
USCS206 Core Subject Statistical Methods 2 3
USCSP206 Core Subject
Practical Statistical Methods – Practical 1 3

USCS207 Ability
Enhancement
Course
E-Commerce & Digital Marketing
2
3

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Page 8 of 50 Semester I

Course Code Course Title Credits Lectures
/Week
USCS101 Digital Systems & Architecture 2 3

About the Course:
This course introduces the principles of computer organization and the basic architecture concepts.The
course emphasizes performance and cost analysis, instruction set design, pipelining, memory
technology, memory hierarchy, virtual memory management, and I/O systems.
Course Objectives:
To have an understanding of Digital systems and operation of a digital computer.
To learn different architectures & organizations of memory systems, processor organization and
control unit.
To understand the working principles of multiprocessor and parallel organization‟s as advanced
computer architectures
Learning Outcomes:
After successful completion of this course, students would be able to
To learn about how computer systems work and underlying principles
To understand the basics of digital electronics needed for computers
To understand the basics of instruction set architecture for reduced and complex instruction sets
To understand the basics of processor structure and operation
To understand how data i s transferred between the processor and I/O devices

Unit Topics No of
Lectures




I Fundamentals of Digital Logic: Boolean algebra, Logic Gates,
Simplification of Logic Circuits: Algebraic Simplification, Karnaugh Maps.
Combinational Circuits: Adders, Mux, De -Mux, Sequential Circuits: Flip -
Flops (SR, JK & D), Counters: synchronous and asynchronous Counter

Computer System: Comparison of Computer Organization &
Architecture, Computer Components and Functions, Interconnection
Structures. Bus Interconnections, Input / Output: I/O Module, Programmed
I/O, Interrupt Driven I/O, Direct Memory Access



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II Memory System Organization: Classification and design parameters,
Memory Hierarchy, Internal Memory: RAM, SRAM and DRAM,
Interleaved and Associative Memory. Cache Memory: Design Principles,
Memory mappings, Replacement Algorithms, Cache performance, Cache
Coherence. Virtual Memory, External Memory: Magnetic Discs, Optical
Memory, Flash Memories, RAID Levels

Processor Organization: Instruction Formats, Instruction Sets, Addressing
Modes, Addressing Modes Examples with Assembly Language [8085/8086
CPU], Processor Organization, Structure and Function. Register



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Page 9 of 50 Organization, Basic Microprocessor operations: Data Transfer (Register /
Memory) Operations, Arithmetic & Logical Operations,
Instruction Cycle, Instruction Pipelining. Introduction to RISC and CISC
Architecture, Instruction Level Parallelism and Superscalar Processors:
Design Issues



III Control Unit: Micro -Operations, Functional Requirements, Processor
Control, Hardwired Implementation, Micro -programmed Control .
Fundamentals of Advanced Computer Architecture: Parallel
Architecture: Classification of Parallel Systems, Flynn‟s Taxonomy, Array
Processors, Clusters, and NUMA Computers. Multiprocessor Systems:
Structure & Interconnection Networks, Multi -Core Computers:
Introduction, Organization and Performance.


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Textbooks:
1. M. Mano, Computer System Architecture 3rd edition, Pearson
2. Carl Hamacher et al., Computer Organization and Embedded Systems, 6 ed., McGraw -Hill
2012
3. R P Jain, Modern Digital Electronics, Tata McGraw Hill Education Pvt. Ltd. , 4th Edition, 2010
Additional References :
1. William Stallings (2010), Computer Organization and Architecture - designing for
performance,8th edition, Prentice Hall, New Jersy.
2. Anrew S. Tanenbaum (2006), Structured Computer Organization, 5th edition, PearsonEducation
Inc,
3. John P. Hayes (1998), Computer Architecture and Organization, 3rd edition, Tata McGrawHill

Course Code Course Title Credits Lectures
/Week
USCSP101 Digital Systems & Architecture – Practical 1 3

1 Study and verify the truth table of various logic gates (NOT, AND, OR, NAND, NOR,
EX-OR, and EX -NOR).
2 Simplify given Boolean expression and realize it.
3 Design and verify a half/full adder
4 Design and verify half/full subtractor
5 Design a 4 bit magnitude comparator using combinational circuits.
6 Design and verify the operation of flip -flops using logic gates.
7 Verify the operation of a counter.
8 Verify the operation of a 4 bit shift register
9 Design and implement expression using multiplexers / demultiplexers.
10 Design and implement 3 -bit binary ripple counter using JK flip flops.
11 Simple microprocessor programs for data transfer operations
12 Simple microprocessor programs for arithmetic & logical transfer operations

Note Practical 1 – 10 can be performed using any open source simulator (like Logisim)
(Download it from https://sourceforge.net/projects/circuit/)
Practical 11 – 12 can be performed on any simulation software like Jubin‟s 8085 simulator

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Page 10 of 50 Course Code Course Title Credits Lectures
/Week
USCS102 Introduction to Programming with Python 2 3

About the Course:
This course is aims at introducing one of the fastest growing programming language of current time and
enables learners to understand the fundamentals of programming with Python. Learners will be able to
write programs to solve real -world problems, and produce quality code. It will help to develop strong
skills of programming for implem enting applications for emerging fields including data science and
machine learning.
Course Objectives:
To learn how to design and program Python applications.
To explore the innards of Python Programming and understand components of Python Program
To define the structure and components of a Python program.
To learn how to write loops and decision statements in Python
To learn about inbuilt input/output operations and compound data types in Python
Learning Outcomes:
After successful completion o f this course, students would be able to:
Ability to store, manipulate and access data in Python
Ability to implement basic Input / Output operations in Python
Ability to define the structure and components of a Python program.
Ability to learn how to write loops and decision statements in Python.
Ability to learn how to write functions and pass arguments in Python.
Ability to create and use Compound data types in Python

Unit Topics No of
Lectures








I Overview of Python: History & Versions, Features of Python, Execution
of a Python Program, Flavours of Python, Innards of Python, Python
Interpreter, Memory Management in Python, Garbage Collection in Python,
Comparison of Python with C and Java, Installing Python, Writing and
Execut ing First Python Program, Getting Help, IDLE

Data Types, Variables and Other Basic Elements: Comments,
Docstrings, Data types - Numeric Data type, Compound Data Type,
Boolean Data type, Dictionary, Sets, Mapping, Basic Elements of Python,
Variables

Input and Output Operations: Input Function, Output Statements, The
print() function, The print(“string”) function, The print(variables list)
function, , The print(object) function, The print(formatted string) function,
Command Line Arguments

Control Stat ements: The if statement, The if … else Statement, The „if …







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Page 11 of 50 elif … else ‟ Statement, Loop Statement - while loop, for loop, Infinite loop,
Nested loop, The else suite, break statement, continue statement, pass
statement, assert statement, return statement











II Operators: Arithmetic operators, Assignment operators, Unary minus
operator, Relational operators, Logical operators, Bitwise operators,
Membership operators, Identity operators, Precedence of Operators,
Associativity of Operators

Arrays: Creating Arrays, Indexing and Slicing of Arrays, Basic Array
Operations, Arrays Processing, Mathematical Operations on Array,
Aliasing Arrays, Slicing and Indexing in NumPy Arrays, Basic slicing,
Advanced Indexing, Dimensions of Arrays, Attributes of an Array, The
ndim Attribute, The shape Attribute, The size Attribute, The itemsize
Attribute

Functions: Function definition and call, Returning Results, Returning
Multiple Values from a Function, Built -in Functions, Difference between a
Function and a Metho d, Pass Value by Object Reference, Parameters and
Arguments, Formal and Actual Arguments, Positional Arguments,
Keyword Arguments, Default Arguments, Arbitrary Arguments, Recursive
Functions, Anonymous or Lambda Functions, Using Lambda with the
filter() Function, Using Lambda with the map() Function, Using Lambda
with the reduce() Function

Modules :Introduction to Modules in Python










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III Strings: Creating Strings, Functions of Strings, Working with Strings,
Length of a String, Indexing and Slicing, Repeating and Concatenating
Strings, Checking Membership, Comparing Strings, Removing Spaces,
Finding Substrings, Counting Substrings, Immutability, Spl itting and
Joining Strings, Changing Case, Checking Starting and Ending of a String,
Sorting Strings, Searching in the Strings, Testing Methods, Formatting
Strings, Finding the Number of Characters and Words, Inserting Substrings
into a String

List and Tuples: Lists, List Functions and Methods, List Operations, List
Slices, Nested Lists, Tuples, Functions in Tuple

Dictionaries: Creating a Dictionary, Operators in Dictionary, Dictionary
Methods, Using for Loop with Dictionaries, Operations on Di ctionaries,
Converting Lists into Dictionary, Converting Strings into Dictionary,
Passing Dictionaries to Functions, Sorting the Elements of a Dictionary
using Lambda, Ordered Dictionaries







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Textbooks:
1. Practical Programming: An Introduction to Computer Science Using Python 3, Paul Gries ,
Jennifer Campbell, Jason Montojo, Pragmatic Bookshelf, 2nd Edition, 2014
2. Programming through Python, M. T Savaliya, R. K. Maurya& G M Magar, Sybgen Learning
India, 2020

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Page 12 of 50

Course Code Course Title Credits Lectures
/Week
USCSP102 Introduction to Programming with Python – Practical 1 3

1 Write a program to design and develop python program to implement various control
statement using suitable examples
2 Write program in Python to define and call functions for suitable problem.
3 Write Python program to demonstrate different types of function arguments.
4 Write a Python program to demonstrate the precedence and associativity of operators.
5 Write suitable Python program to implement recursion for problems such as Fibonacci
series, Factorial, Tower of Hanoi etc.
6 Write Python program to implement and use lambda function in python
7 Write a python program to create and manipulate arrays in Python. Also demonstrate
use of slicing and indexing for accessing elements from the array.
8 Write a program to implement list in Python for suitable problem. Demonstrate various
operations on it.
9 Write a program to implement tuple in Python for suitable problem. Demonstrate
various operations on it.
10 Write a program to implement dictionary in Python for suitable problem. Demonstrate
various operations on it. Additional References :
1. Python: The Complete Reference, Martin C. Brown, McGraw Hill, 2018
2. Beginning Python: From Novice to Professional, Magnus Lie Hetland, Apress, 2017
3. Programming in Python 3, Mark Summerfield, Pearson Education, 2nd Ed, 2018
4. Python Programming: Using Problem Solving Approach, ReemaThareja, Oxford Univeristy
Press, 2017
5. Let Us Python, Yashwant. B. Kanetkar, BPB Publication, 2019

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Page 13 of 50 Course Code Course Title Credits Lectures
/Week
USCS103 LINUX Operating System 2 3

About the Course:
This syllabus will help to train students in fundamental skills and build -up sustainable interest in Linux
Operating System. It will improve necessary knowledge base to understand Linux Operating System
and its practical implementation, it will also help to develop Linux based solutions for real life
problems.
Course Objectives:
To learn basic concepts of Linux in terms of operating system
To learn use of various shell commands with regular expressions
To set Linux Environment variables and learn setting file permissions to maintain Linux
security implementation
To learn various editors available in Linux OS
To learn shell scripting.
To learn installation of compilers and programming using C and Python languages on Linux
platform
Learning Outcomes:
After successful completion of this course, students would be able to
Work with Linux file system structure, Linux Environment
Handle shell commands for scripting, with features of reg ular expressions, redirections
Implement file security permissions
Work with vi, sed and awk editors for shell scripting using various control structures
Install softwares like compilers and develop programs in C and Python programming languages
on Linux Platform

Unit Topics No of
Lectures







I Linux operating system and Basics : History, GNU Info and Utilities,
Various Linux Distributions, The Unix/Linux architecture, Features of
Unix/Linux, Starting the shell, Shell prompt, Command structure, File
Systems and Directory Structure, man pages, more documentation pages
Basic Bash shell commands: General purpose utility Commands, basic
commands, Various file types, attributes and File handling Commands,
Handling Ordinary Files. More file attributes
Advanced Bash shell commands: Simple Filters, Filters using regular
expressions.
The Linux environment variable: Setting, Locating and removing
environment variables like PATH etc, Default shell environment variables,
Using command aliases.






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Page 14 of 50



II Understanding Linux file permission: Linux security, Using Linux
groups, Decoding file permissions, Changing security setting, Sharing files.
Linux Security: Understanding Linux Security, uses of root, sudo
command, working with passwords, Understanding ssh.
Networking: TCP/IP Basics, TCP/IP Model, Resolving IP addresses,
Applications, ping, telnet, ftp, DNS
Working withEditors: awk, sed and Introduction to vi



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III Basic script building: Using multiple commands, Creating script files,
Displaying messages, Using variables, Redirecting Input and Output, Pipes
performing math, Exiting the script.
Using structured commands: Working with if -then, if -then-else and
nested if statements, test command, Compound condition testing, while
command, until command, case command.
Script and Process control : Handling signals, Running scripts in
background mode, Running scripts without a console, Job control, Job
scheduling commands: ps, nice, renice , at, batch, cron table, Running the
script at boot





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Textbooks:
1. “Linux Command line and Shell Scripting Bible”, Richard Blum, Wiley India.
2. “Unix: Concepts and Applications”, Sumitabha Das, 4th Edition, McGraw Hill.
3. “Official Ubuntu Book”, Matthew Helmke& Elizabeth K. Joseph with Jose Antonio Rey and
Philips Ballew, 8th Ed.
Additional References :
1. “Linux Administration: A Beginner's Guide”, Fifth Edition, Wale Soyinka, Tata McGraw -Hill,
2008.
2. “Linux: Complete Reference ”, Richard Petersen, 6th Edition, Tata McGraw -Hill
3. “Beginning Linux Programming”, Neil Mathew, 4th Edition, Wiley Publishing, 2008.

Course Code Course Title Credits Lectures
/Week
USCSP103 LINUX Operating System – Practical 1 3



1 Installation of Ubuntu Linux operating system.
a) Booting and Installing from ( USB/DVD)
b) Using Ubuntu Software center / Using Synaptic
c) Explore useful software packages.


2 Becoming an Ubuntu power user
a) Administering system and User setting
b) Learning Unity keyboard
c) Using the Terminal
d) Working with windows programs

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Page 15 of 50


3 File System Commands: touch, help, man, more, less, pwd, cd, mkdir, rmdir, ls, find,
ls, etc

File handling Commands: cat, cp, rm, mv, more, file, wc, od, cmp, diff, comm,
chmod, chown, chgrp, gzip and gunzip, zip and unzip, tar, ln, umask,, chmod, chgrp,
chown, etc



4 General purpose utility Commands: cal, date, echo, man, printf, passwd, script, who,
uname, tty, stty, etc

Simple Filters and I/O redirection: head, tail, cut paste, sort, grep family, tee, uniq, tr,
etc.

Networking Commands: who, whoami, ping, telnet, ftp, ssh, etc
5 Editors: vi, sed, awk
6 Working and Managing with processes - sh, ps, kill, nice, at and batch etc.

7 Shell scripting I: Defining variables, reading user input, exit and exit status
commands, , expr, test, [], if conditional, logical operators

8 Shell scripting II: Conditions (for loop, until loop and while loop) arithmetic
operations, examples

9 Shell scripting III: Redirecting Input / Output in scripts, creating your own
Redirection

10 Installation of C/C++/Java/Python Compiler and creating an environment for
app development. Basic programming using C and Python Languages.

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Page 16 of 50 Course Code Course Title Credits Lectures
/Week
USCS104 Open Source Technologies 2 3

About the Course:
Open Source Software is becoming an important resource for development, especially in developing
countries. A working understanding of the economic and technical background of the Free / Open
Source Software movement (FOSS) is essential for its effective use. The course takes students through
the history and current status of the FOSS world, and starts them exploring it, by connecting their
personal experiences with corresponding FOSS projects. Students will experience finding and using
Open Source Softwar e projects.
Course Objectives:
Understand the difference between open -source software and commercial software.
Understand the policies, licensing procedures and ethics of FOSS.
Understand open -source philosophy, methodology and ecosystem.
Awarenes s with Open -Source Technologies.
Learning Outcomes:
Differentiate between Open Source and Proprietary software and Licensing.
Recognize the applications, benefits and features of Open -Source Technologies
Gain knowledge to start, manage open -source projects.

Unit Topics No of
Lectures







I Introduction to Open -Source: Open Source, Need and Principles of OSS,
Open -Source Standards, Requirements for Software, OSS success, Free
Software, Examples, Licensing, Free Vs. Proprietary Software, Free
Software Vs. Open -Source Software, Public Domain. History of free
software, Prop rietary Vs Open -Source Licensing Model, use of Open -
Source Software, FOSS does not mean no cost. History: BSD, The Free
Software Foundation and the GNU Project.

Open -Source Principles and Methodology: Open -Source History, Open -
Source Initiatives, Open Standards Principles, Methodologies, Philosophy,
Software freedom, Open -Source Software Development, Licenses,
Copyright vs. Copy left, Patents, Zero marginal cost, Income -generation
Opportunities, Internationalization.

Licensing: What Is A License, How t o create your own Licenses,
Important FOSS Licenses (Apache, BSD, PL, LGPL), copyrights and copy
lefts, Patent.






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II Open -Source projects: Starting and maintaining own Open -Source
Project, Open -Source Hardware, Open -Source Design, Open -source
Teaching, Open -source media.

Collaboration: Community and Communication, Contributing to Open -
Source Projects Introduction to GitHub, interacting with the community on
GitHub, Communication and etiquette, testing open -source code, reporting


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Page 19

Page 17 of 50 issues, contributing code. Introduction to Wikipedia, contributing to
Wikipedia or contributing to any prominent open -source project of student ‟s
choice.

Open -Source Ethics and Social Impact: Open source vs. closed source,
Open -source Government, Ethics of Open -source, Social and Financial
impacts of open -source technology, Shared software, Shared source, Open
Source as a Business Strategy





III Understanding Open -Source Ecosystem : Open -Source Operating
Systems: GNU/Linux, Android, Free BSD, Open Solaris. Open -Source
Hardware, Virtualization Technologies, Containerization Technologies:
Docker, Development tools, IDEs, Debuggers, Programming languages,
LAMP, Open -Source Database technologies

Case Studies: Example Projects: Apache Web server, BSD, GNU/Linux,
Android, Mozilla (Firefox), Wikipedia, Drupal, WordPress, Git, GCC,
GDB, GitHub, Open Office, LibreOffice
Study: Understanding the developmental models, licensing, mode of
funding, commercial/non -commercial use.




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Textbooks:
1. “Open -Source Technology”, Kailash Vadera&Bhavyesh Gandhi, University Science Press,
Laxmi Publications, 2009
2. “Open -Source Technology and Policy”, Fadi P. Deek and James A. M. McHugh, Cambridge
University Press, 2008.
Additional References :
1. “Perspectives on Free and Open -Source Software”, Clay Shirky and Michael Cusumano, MIT
press.
2. “Understanding Open Source and Free Software Licensing”, Andrew M. St. Laurent, O‟Reilly
Media.
3. “Open Source for the Enterprise”, Dan Wo ods, GautamGuliani, O‟Reilly Media
4. Linux kernel Home: http://kernel.org4
5. Open -Source Initiative: https://opensource.org/5
6. The Linux Foundation: http://www.linuxfoundation.org/
7. The Linux Documentation Project: http://www.tldp.org/2
8. Docker Project Home: http://www.dock er.com3.
9. Linux Documentation Project: http://www.tldp.org/6
10. Wikipedia:
https://en.wikipedia.org/7.https://en.wikipedia.org/wiki/Wikipedia:Contributing_to_Wikipedia8
11. GitHub: https://help.github.com/9.
12. The Linux Foundation: http://www.linuxfoundation.org/

Page 20

Page 18 of 50 Course Code Course Title Credits Lectures
/Week
USCSP104 Open Source Technologies – Practical 1 3



1 Open Source Operating Systems
Learn the following open source operating system of your choice: Linux,
Android, FreeBSD, Open Solaris etc.
Learn the installation.
Identify the unique features of these OS.

2 Hands on with LibreOffice
Learn it from practical view -point
Give a brief presentation about it to the class

3 Hands on with GIMP Photo Editing Tool
Learn it from practical view -point
Give a brief presentation about it to the class

4 Hands on with Shotcut Video Editing Tool
Learn it from practical view -point
Give a brief presentation about it to the class

5 Hands on with Blender Graphics and Animation Tool
Learn it from practical view -point
Give a brief presentation about it to the class

6 Hands on with Apache Web Server
Learn it from practical view -point
Give a brief presentation about it to the class

7 Hands on with WordPress CMS
Learn it from practical view -point
Give a brief presentation about it to the class


8 Contributing to Wikipedia :
Introduction to wikipedia: operating model, license, how to contribute?
Create your user account on wikipedia
c. Identify any topic of your choice and contribute the missing information





9 Github
Create and publish your own open source project: Write any simple program
using your choice of programming language.
Create a repository on github and save versions of your project. You‟ll learn
about the staging area, committing your code, branching, and merging,
Using GitHub to Collaborate: Get practice using GitHub or other remote
repositorie s to share your changes with others and collaborate on multi -
developer projects. You‟ll learn how to make and review a pull request on
GitHub.
d. Contribute to a Live Project: Students will publish a repository containing their
reflections from the course and submit a pull request.

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Page 19 of 50
10 Virtualization: Open Source virtualization technologies:
Install and configure the following: VirtualBox, Zen, KVM
Create and use virtual machines


11 Containerization:
Install and configure the following containerization technologies: docker,
rocket, LXD
Create and use containers using it

Page 22

Page 20 of 50 Course Code Course Title Credits Lectures
/Week
USCS105 Discrete Mathematics 2 3

About the Course:
Discrete Mathematics provides an essential foundation for virtually every area of Computer Science.
The problem -solving techniques honed in Discrete Mathematics are necessary for writing complicated
software. Discrete mathematics also builds the gateway to advance d courses in Mathematical Sciences,
Data Science, Machine Learning, Software Engineering, etc.
Course Objectives:
The purpose of the course is to familiarize the prospective learners with mathematical structures
that are fundamentally discrete.
This course will enhance prospective learners to reason and ability to articulate mathematical
problems.
This course will introduce functions, forming and solving recurrence relations and different
counting principles. These concepts will be useful to study o r describe objects or problems in
computer algorithms and programming languages and these concepts can be used effectively in
other courses.
Learning Outcomes:
After successful completion of this course, learners would be able to:
Define mathematical structures (relations, functions, graphs) and use them to model real life
situations.
Understand, construct and solve simple mathematical problems.
Solve puzzles based on counting principles.
Provide basic knowledge about models of automata theory and the corresponding formal
languages.
Develop an attitude to solve problems based on graphs and trees, which are widely used in
software.

Unit Topics No of
Lectures






I Functions:
Definition of function; Domain, co-domain, range of a function; Examples
of standard functions such as identity and constant functions, absolute value
function, logarithmic and exponential functions, flooring and ceiling
functions; Injective, surjective and bijective functions; Composit e and
inverse functions.

Relations:
Definition and examples of relation; Properties of relations, Representation
of relations using diagraphs and matrices; Equivalence relation; Partial
Order relation, Hasse Diagrams, maximal, minimal, greatest, least ele ment,
Lattices.





15

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Page 21 of 50
Recurrence Relations:
Definition and Formulation of recurrence relations; Solution of a recurrence
relation; Solving recurrence relations - Back tracking method, Linear
homogeneous recurrence relations with constant coefficients;
Homogeneous solution of linear homogeneous recu rrence relation with
constant coefficients; Particular solution of non -linear homogeneous
recurrence relation with constant coefficients; General solution of non -
linear homogeneous recurrence relation with constant coefficients;
Applications - Formulate an d solve recurrence relation for Fibonacci
numbers, Tower of Hanoi, Intersection of lines in a plane, Sorting
Algorithms.








II Counting Principles:
Basic Counting Principles (Sum and Product Rule); Pigeonhole Principle
(without proof) - Simple examples; Inclusion Exclusion Principle (Sieve
formula) (without proof); Counting using Tree diagrams.

Permutations and Combinations:
Permutation without and with repetition; Combination without and with
repetition; Binomial numbers and identities: Pascal Identity,
Vandermonde‟s Identity, Pascal triangle, Binomial theorem (without proof)
and applications; Multionomial numbers, Multinomia l theorem (without
proof) and applications.

Languages, Grammars and Machines:
Languages and Grammars – Introduction, Phase structure grammar, Types
of grammar, derivation trees; Finite -State Machines with Output; Finite -
State Machines with No Output; Re gular Expression and Regular
Language.







15







III Graphs:
Graphs and Graph Models; Graph terminologies and Special types of
graphs; Definition and elementary results; Representing graphs, Linked
representation of a graph; Graph Isomorphism; Connectivity in graphs –
path, trail, walk; Euler and Hamilton paths; Planar graphs, Graph coloring
and chromatic number.

Trees:
Definition, Tree terminologies and elementary results; Linked
representation of binary trees; Ordered rooted tree, Binary tre es, Complete
and extended binary trees, Expression trees, Binary Search tree, Algorithms
for searching and inserting in binary search trees, Algorithms for deleting in
a binary search tree; Traversing binary trees






15
Textbooks:
1. Discrete Mathematics and Its Applications, Seventh Edition by Kenneth H. Rosen, McGraw
Hill Education (India) Private Limited. (2011)
2. Discrete Mathematics: SemyourLipschutz, Marc Lipson, Schaum‟s out lines, McGraw- Hill Inc.

Page 24

Page 22 of 50



Course Code
Course Title
Credits Lectures
/Week
USCSP105 Discrete Mathematics – Practical 1 3




1 Functions –
a. Identify if the given mapping is a function
b. Finding domain and range of a given function
c. Check if the given function is injective/surjective/bijective
d. Find the inverse of a given function
e. Operations on functions
f. Graphs of functions using any online tool



2 Relations –
a. Representation of relations
b. Determine if the given relation satisfies equivalence relation/partial order
relation
c. Draw Hasse diagrams
d. Find maximal, minimal, greatest, least element in a poset
e. Determine if a given poset is a lattice


3 Recurrence Relation –
a. Solve recurrence relation using backtracking method
b. Solve linear homogeneous recurrence relations with constant coefficients
c. Find homogeneous, particular, general solution of a recurrence relation
d. Formulate and solving recurrence relation


4 Counting Principles –
a. Sum and product rule
b. Pigeonhole Principle
c. Inclusion Exclusion Principle
d. Counting using Tree diagrams


5 Permutations and Combinations –
a. Permutations
b. Permutations with repetitions
c. Combinations
d. Combinations with repetitions
e. Binomial numbers and Identities 3rd Edition
3. Data Structures Seymour Lipschutz, Schaum‟s out lines, McGraw- Hill Inc. 2017
4. Norman L. Biggs, Discrete Mathematics, Revised Edition, Clarendon Press, Oxford 1989.
Additional References :
1. Elements of Discrete Mathematics: C.L. Liu, Tata McGraw - Hill Edition.
2. Concrete Mathematics (Foundation for Computer Science): Graham, Knuth, Patashnik Second
Edition, Pearson Education.
3. Discrete Mathematics: SemyourLipschutz, Marc Lipson, Schaum‟s out lines, McGraw- Hill Inc.
4. Foundations in Discrete Mathematics: K.D. Joshi, New Age Publication, New Delhi.

Page 25

Page 23 of 50 f. Applications on Binomial theorem
g. Applications on Multinomial theorem


6 Languages and Grammars –
a. Find the language generated by given grammar
b. Check if a given string belongs or not to a given language/grammar
c. Operations on languages
d. Identify the type of grammar

7 Finite State Machines –
a. Check if a given string is accepted or rejected by FSM without output
b. Find the output for a FSM with output
c. Describe a machine (diagram/table)

8 Regular Expression and Regular Language –
a. Describe the regular expressions represented by given language
b. Describe the language represented by given regular expression




9 Graphs –
a. Types of graph
b. Properties of graph
c. Representation of graph
d. Graph Isomorphism
e. Connectivity in graphs – path, trail, walk
f. Euler and Hamilton graphs
g. Planar graphs
h. Graph coloring and chromatic number




10 Trees –
a. Tree terminologies
b. Types of tree
c. Properties of tree
d. Representation of tree
e. Expression tree
f. Binary Search tree
g. Tree traversal

Page 26

Page 24 of 50 Course Code Course Title Credits Lectures
/Week
USCS106 Descriptive Statistics 2 3

About the Course:
This course is designed to provide learners with an understanding of the data and to develop an
understanding of the quantitative techniques from Statistics. It also provides the knowledge of different
statistical tools used for primary statistical analysis of data.
Course Objectives:
1. To develop the learners ability to deal with different types of data.
2. To enable the use of different measures of central tendency and dispersion wherever relevant.
3. To make learner aware about the techniques to check the Skewness and Kurtosis of data.
4. To make learner enable to find the correlation between different variables and further apply the
regression analysis to find the exact relation between them.
5. To develop ability to analyze statistical data through R software.
Learning Outcomes:
After successful completion of this course, learners would be able to
1. Organize, manage and present data.
2. Analyze Statistical data using measures of central tendency and dispersion.
3. Analyze Statistical data using basics techniques of R.
4. Study the relationship between variables using techniques of correlation and regression.

Unit Topics No of
Lectures









I Data Types and Data Presentation: Data types: Attribute, Variable,
Discrete and Continuous variable, Univariate and Bivariate distribution.
Types of Characteristics, Different types of scales: nominal, ordinal,
interval and ratio.

Data presentation: Frequency distribution, Histogram, Ogive curves.

Introduction to R: Data input, Arithmetic Operators, Vector Operations,
Matrix Operations, Data Frames, Built -in Functions. Frequency
Distribution, Grouped Frequency Distribution, Diagrams and Graphs,
Summary statistics for raw data and grouped frequency distribution.

Measures of Central tendency: Concept of average/central tendency,
characteristics of good measure of central tendency. Arithmetic Mean
(A.M.), Median, Mode - Definition, examples for ungrouped and grouped
data, effect of shift of origin and change of scale, merits and demerits.
Combined arithmetic mean. Partition Values: Quartiles, Deciles and
Percentiles - examples for ungrouped and grouped data








15

Page 27

Page 25 of 50





II Measures dispersion: Concept of dispersion, Absolute and Relative
measure of dispersion, characteristics of good measure of dispersion.
Range, Semi -interquartile range, Quartile deviation, Standard deviation -
Definition, examples for ungrouped and grouped data, effect of shi ft of
origin and change of scale, merits and demerits. Combined standard
deviation, Variance. Coefficient of range, Coefficient of quartile deviation
and Coefficient of variation (C.V.)

Moments: Concept of Moments, Raw moments, Central moments,
Relation between raw and central moments.

Measures of Skewness and Kurtosis: Concept of Skewness and
Kurtosis, measures based on moments, quartiles.





15








III Correlation: Concept of correlation, Types and interpretation,
Measure of Correlation: Scatter diagram and interpretation; Karl
Pearson‟s coefficient of correlation (r): Definition, examples for
ungrouped and grouped data, effect of shift of origin and change of
scale, properties; Spearman‟s rank correlation coefficient: Definition,
examples of with and without repetition. Concept of Multiple
correlation.

Regression: Concept of dependent (response) and independent
(predictor) variables, concept of regression, Types and prediction,
difference between correlation and regression, Relation between
correlation and regression. Linear Regression - Definition, examples
using l east square method and regression coefficient, coefficient of
determination, properties. Concept of Multiple regression and
Logistic regression.







15
Textbooks:
1. Goon, A. M., Gupta, M. K. and Dasgupta, B. (1983). Fundamentals of Statistics, Vol. 1, Sixth
Revised Edition, The World Press Pvt. Ltd., Calcutta.
2. Gupta, S.C. and Kapoor, V.K. (1987): Fundamentals of Mathematical Statistics, S. Chand and
Sons, New Delhi
Additional References :
1. Sarma, K. V. S. (2001). Statistics Made it Simple: Do it yourself on PC. Prentce Hall of India,
NewDelhi.
2. Agarwal, B. L. (2003). Programmed Statistics, Second Edition, New Age International
Publishers, NewDelhi.
3. Purohit, S. G., Gore S. D., Deshmukh S. R. (2008). Statistics Using R, Narosa Publishing
House, NewDelhi.
4. Schau m‟s Outline Of Theory And Problems Of Beginning Statistics, Larry J. Stephens,
Schau m‟s Outline Series Mcgraw-Hill

Page 28

Page 26 of 50 Course Code Course Title Credits Lectures
/Week
USCSP106 Descriptive Statistics – Practical 1 3
Problem solving and implementation using R programming


1 Basics of R -
a. Data input, Arithmetic Operators
b. Vector Operations, Matrix Operations
c. Data Frames, Built -in Functions
d. Frequency Distribution, Grouped Frequency Distribution
e. Diagrams and Graphs

2 Frequency distribution and data presentation -
a. Frequency Distribution (Univariate data/ Bivariate data)
b. Diagrams
c. Graphs


3 Measures of Central Tendency -
a. Arithmetic Mean
b. Median
c. Mode
d. Partition Values

4 Measures dispersion -
a. Range and Coefficient of range
b. Quartile deviation and Coefficient of quartile deviation
c. Standard deviation, Variance and Coefficient of variation (C.V.)

5 Moments -
a. Raw moments
b. Central moments

6 Measures of Skewness -
a. Karl Pearson‟s measure of Skewness
b. Bowley‟s measure of Skewness
c. Moment coefficient of Skewness

7 Measures of Kurtosis -
a. Moment coefficient of Kurtosis (Absolute measure)
b. Moment coefficient of Kurtosis (Relative measure)

8 Correlation -
a. Karl Pearson‟s correlation coefficient
b. Spearman‟s Rank correlation

9 Regression -
a. Method of least squares
b. Using regression coefficients
c. Properties of regression lines & regression coefficients

10 Summary Statistics using R -
a. Summary statistics for raw data
b. Summary statistics for grouped frequency distribution
c. Simple Correlation & Regression using R

Page 29

Page 27 of 50 Course Code Course Title Credits Lectures
/Week
USCS107 Soft Skills 2 3

About the Course:
To help learners develop their soft skills and develop their personality along with technical skills. Focus
on various communication enhancement along with academic and professional ethics.
Course Objectives:
Understand the significance and essence of a wide range of soft skills.
Learn how to apply soft skills in a wide range of routine social and professional settings
Learn how to employ soft skills to improve interpersonal relationships
Learn how to employ soft skills to enhance employability and ensure workplace and career
success
Learning Outcomes:
Learners will be able to understand the importance and types soft skills
Learners will develop skills for Academic and Professional Presentations.
Learners will able to understand Leadership Qualities and Ethics.
Ability to understand the importance of stress management in their academic & professional
life.

Unit Topics No of
Lectures











I Introduction to Soft Skills
Soft Skills: An Introduction – Definition and Significance of Soft Skills;
Process, Importance and Measurement of Soft Skill Development.

Personality Development: Knowing Yourself, Positive Thinking, Johari ‟s
Window, Physical Fitness

Emotional Intelligence: Meaning and Definition, Need for Emotional
Intelligence, Intelligence Quotient versus Emotional Intelligence Quotient,
Components of Emotional Intelligence, Com petencies of Emotional
Intelligence, Skills to Develop Emotional Intelligence

Positivity and Motivation: Developing Positive Thinking and Attitude;
Driving out Negativity; Meaning and Theories of Motivation; Enhancing
Motivation Levels

Etiquette and Mannerism: Introduction, Professional Etiquette,
Technology Etiquette

Ethical Values: Ethics and Society, Theories of Ethics, Correlation
between Values and Behavior, Nurturing Ethics, Importance of Work
Ethics, Problems in the Absence of Work Ethics










15

Page 30

Page 28 of 50










II Basic Skills in Communication:
Components of effective communication: Communication process and
handling them, Composing effective messages, Non – Verbal
Communication: its importance and nuances: Facial Expression, Posture,
Gesture, Eye contact, appearance (dress code).

Communication Skills: Spoken English, Phonetics, Accent, Intonation

Employment Communication: Introduction, Resume, Curriculum Vitae,
Scannable Resume, Developing an Impressive Resume, Formats of
Resume, Job Application or Cover Letter

Job Interviews: Introduction, Importance of Resume, Definition of
Interview, Background Information, Types of Interviews, Preparatory Steps
for Job Interviews, I nterview Skill Tips, Changes in the Interview Process,
FAQ During Interviews

Group Discussion: Introduction, Ambience/Seating Arrangement for
Group Discussion, Importance of Group Discussions, Difference between
Group Discussion, Panel Discussion and Deb ate, Traits, Types of Group
Discussions, topic based and Case based Group Discussion, Individual
Traits










15









III Academic and Professional Skills:
Professional Presentation: Nature of Oral Presentation, planning a
Presentation, Preparing the Presentation, Delivering the Presentation

Creativity at Workplace: Introduction, Current Workplaces, Creativity,
Motivation, Nurturing Hobbies at Work, The Six Thinking Hat Method.

Capacity Building: Learn, Unlearn and Relearn : Capacity Building,
Elements of Capacity Building, Zones of Learning, Ideas for Learning,
Strategies for Capacity Building

Leadership and Team Building: Leader and Leadership, Leadership
Traits, Culture and Leadership, Leadership Styles and Trends, Team
Building, Types of Teams.
Decision Making and Negotiation: Introduction to Decision Making,
Steps for Decision Making, Decision Making Techniques, Negotiation
Fundamentals, Negotiation Styles, Major Negotiation Concepts
Stress and Time Management: Stress, Sources of Stress , Ways to Cope
with Stress








15
Textbooks:
1. Managing Soft Skills for Personality Development – edited by B.N.Ghosh, McGraw Hill India,
2017.
2. Soft Skills: An Integrated Approach to Maximize Personality, Gajendra S. Chauhan, Sangeeta
Sharma, Wiley India
Additional References :
1. Personality Development and Soft Skills, Barun K. Mitra, Oxford Press
2. Business Communication, ShaliniKalia, Shailja Agrawal, Wiley India
3. Cornerstone: Developing Soft Skills, Sherfield, Pearson India

Page 31

Page 29 of 50 Semester II

Course Code Course Title Credits Lectures
/Week
USCS201 Design & Analysis of Algorithms 2 3

About the Course:
The course covers the concepts of - (i) calculating complexity of algorithms, (ii) the essential
operations like searching, sorting, selection, pattern matching & recursion, and (iii) various algorithmic
strategies like greedy, divide -n-conquer, dynamic programming, backtracking and imp lementations of
all these on basic data structures like array, list and stack.
Course Objectives:
The objectives of this course are:
To make students understand the basic principles of algorithm design
To give idea to students about the theoretical b ackground of the basic data structures
To familiarize the students with fundamental problem -solving strategies like searching, sorting,
selection, recursion and help them to evaluate efficiencies of various algorithms.
To teach students the important algorithm design paradigms and how they can be used to solve
various real world problems.
Learning Outcomes:
After successful completion of this course, students would be able to
Students should be able to understand and evaluate efficiency of the progr ams that they write
based on performance of the algorithms used.
Students should be able to appreciate the use of various data structures as per need
To select, decide and apply appropriate design principle by understanding the requirements of
any real life problems


Unit Topics No of
Lectures






I Introduction to algorithms - What is algorithm, analysis of algorithm,
Types of complexity, Running time analysis, How to Compare Algorithms,
Rate of Growth, Types of Analysis, Asymptotic Notation, Big-O Notation,
Omega -Ω Notation, Theta -Θ Notation, Asymptotic Analysis, Performance
characteristics of algorithms, Estimating running time / number of steps of
executions on paper, Idea of Computability

Introduction to Data Structures - What is data stru cture, types,
Introduction to Array(1 -d & 2 -d), Stack and List data structures, operations
on these data structures, advantages disadvantages and applications of these
data structures like solving linear equations, Polynomial Representation,
Infix -to-Postf ix conversion





15
II Recursion - What is recursion, Recursion vs Iteration, recursion
applications like Factorial of a number, Fibonacci series & their 15

Page 32

Page 30 of 50 comparative analysis with respect to iterative version, Tower of hanoi
problem

Basic Sorting Techniques - Bubble, Selection and Insertion Sort & their
comparative analysis

Searching Techniques - Linear Search and its types, Binary Search and
their comparative analysis

Selection Techniques - Selection by Sorting, Partition -based Selection
Algorithm, Finding the Kth Smallest Elements in Sorted Order & their
comparative analysis

String Algorithms - Pattern matching in strings, Brute Force Method &
their comparative analysis








III Algorithm Design Techniques - Introduction to various types of
classifications/design criteria and design techniques

Greedy Technique - Concept, Advantages & Disadvantages, Applications,
Implementation using problems like - file merging problem

Divide -n-Conquer - Concept, Advantages & Disadvantages, Applications,
Implementation using problems like - merge sort, Strassen's Matrix
Multiplication

Dynamic Programming - Concept, Advantages & Disadvantages,
Applications, Implementation using problems like - Fibonacci series,
Factorial of a number, Longest Common subsequence

Backtracking Programming - Concept, Advantages & Disadvantages,
Applications, Implementation using problems like N -Queen Problem







15
Textbooks:
1. “Data Structure and Algorithm Using Python”, Rance D. Necaise, Wiley India Edition, 2016.
2. “Data Structures and Algorithms Made Easy”, NarasimhaKarumanchi, CareerMonk
Publications, 2016.
3. “Introduction to Algorithms”, Thomas H. Cormen, 3rd Edition, PHI.
Additional References :
1. “Introduction to the Design and Analysis of Algorithms”, Anany Levitin, Pearson, 3rd Edition,
2011.
2. “Design and Analysis of Algorithms”, S. Sridhar, Oxford University Press, 2014.

Page 33

Page 31 of 50 Course Code Course Title Credits Lectures
/Week
USCSP201 Design & Analysis of Algorithms – Practical 1 3



1 Programs on 1 -d arrays like - sum of elements of array, searching an element in
array, finding minimum and maximum element in array, count the number of
even and odd numbers in array. For all such programs, also find the time
complexity, compare if there are multiple methods

2 Programs on 2 -d arrays like row -sum, column -sum, sum of diagonal elements,
addition of two matrices , multiplication of two matrices. For all such programs,
also find the ti me complexity, compare if there are multiple methods
3 Program to create a list -based stack and perform various stack operations.

4 Program to perform linear search and binary search on list of elements.
Compare the algorithms by calculating time required in milliseconds using
readymade libraries.

5 Programs to sort elements of list by using various algorithms like bubble,
selection sort, and insertion sort. Compare the efficiency of algorithms.

6 Programs to select the Nth Max/Min element in a list by using various
algorithms. Compare the efficiency of algorithms.

7 Programs to find a pattern in a given string - general way and brute force
technique. Compare the efficiency of algorithms.

8 Programs on recursion like factorial, fibonacci, tower of hanoi. Compare
algorithms to find factorial/fibonacci using iterative and recursive approaches.

9 Program to implement file merging, coin change problems using Greedy
Algorithm and to understand time complexity.

10 Program to implement merge sort, Straseen‟s Matrix Multiplication using D-n-C
Algorithm and to understand time complexity.

11 Program to implement fibonacci series, Longest Common Subsequence using
dynamic programming and to understand time complexity. Compare it with the
general recursive algorithm.

12 Program to implement N -Queen Problem, Binary String generation using
Backtracking Strategy and to underst and time complexity.

Page 34

Page 32 of 50 Course Code Course Title Credits Lectures
/Week
USCS202 Advanced Python Programming 2 3

About the Course:
This course aims to explore and enable learners to master the skills of advanced topics in Python
Programming. It helps learners develops advanced skills such as working with databases, matching
patterns, implementing threads and exception handling and GUI in Python. It also highlights and why
Python is a useful scripting language for all developers.
Course Objectives:
To learn how to design object‐oriented programs with Python c lasses.
To learn about reading, writing and implementing other operation on files in Python.
To implement threading concept and multithreading on Python
To design GUI Programs and implement database interaction using Python.
To know about use of regular expression and handling exceptions for writing robust python
programs.
Learning Outcomes:
After successful completion of this course, students would be able to
Ability to implement OOP concepts in Python including Inheritance and Polymorphism
Ability to work with files and perform operations on it using Python.
Ability to implement regular expression and concept of threads for developing efficient
program
Ability to implement exception handling in Python applications for error handling.
Knowledge of working with databases, designing GUI in Python and implement networking in
Python

Unit Topics No of
Lectures








I Working with files: Files, opening and closing a file, working with text
files containing strings, knowing whether a file exists or not, working with
binary files, the „with‟ statement, the seek() and tell() methods, random
accessing of binary files, zipping and unzipping fil es, working with
directories, running other programs from python program

Regular expressions: What is a regular expression?, sequence characters in
regular expressions, quantifiers in regular expressions, special characters in
regular expressions, using r egular expression on files, retrieving
information from an html file,

Threads in python: Difference between process and thread, types of
threads, benefits of threads, creating threads, single tasking and
multitasking, thread synchronization, deadlock in threads, daemon threads

Date and time in python: Date and time now, combining date and time,







15

Page 35

Page 33 of 50 formatting dates and times, finding durations using “time delta”, comparing
two dates, sorting dates, stopping execution temporarily, knowing the time
taken by a program, calendar module








II Database in python: Using SQL with python, retrieving rows from a table,
inserting rows into a table, deleting rows from a table, updating rows in a
table, creating database tables through python, Exception handling in
databases.

Exceptions in python: Errors in a python program, compile & run -time
errors, logical error, exceptions -exception handling, types of exceptions, the
except block, the assert statement, user -defined exceptions, logging the
exceptions

Networking: Protocols,server -client architecture, tcp/ip and udp
communication

Graphical user interface: Creating a GUI in python, Widget classes,
Working with Fonts and Colours, working with Frames, Layout manager,
Event handling







15








III OOPs in python: Features of Object Oriented Programming system (oops) -
classes and objects, encapsulation, abstraction, inheritance, polymorphism,
constructors and destructors

Classes and objects: Creating a class, the self -variable, types of variables,
namespaces, types of methods, instance methods, class methods, static
methods, passing members of one class to another class, inner classes
Inheritance and polymorphism: Inheritance in python, types of
inheritance - single inheritance, multilevel inheritance, hierarchical
inheritance, multiple inheritance, constructors in inheritance, overriding
super class constructors and methods, the super() method, method
resolution order (mro), polymorphism, duck typing, operator overloading,
method overloading, method overriding ,

Abstract classes and interfaces: Abstract class, abstract method, interfaces
in python, abstract classes vs. Interfaces







15
Textbooks:
1. Paul Gries , Jennifer Campbell, Jason Montojo, Practical Programming: An Introduction to
Computer Science Using Python 3, Pragmatic Bookshelf, 3rd Edition, 2018
2. Programming through Python, M. T Savaliya, R. K. Maurya, G M Magar, Revised Edition,
Sybgen Learning India, 2020
Additional Reference s:
1. Advanced Python Programming, Dr. Gabriele Lanaro, Quan Nguyen, SakisKasampalis, Packt
Publishing, 2019
2. Programming in Python 3, Mark Summerfield, Pearson Education, 2nd Ed, 2018
3. Python: The Complete Reference, Martin C. Brown, McGraw Hill, 2018
4. Beginning Python: From Novice to Professional, Magnus Lie Hetland, Apress, 2017
5. Programming in Python 3, Mark Summerfield, Pearson Education, 2nd Ed, 2018

Page 36

Page 34 of 50
Course Code Course Title Credits Lectures
/Week
USCSP202 Advanced Python Programming – Practical 1 3

1 Write a program to Python program to implement various file operations.
2 Write a program to Python program to demonstrate use of regular expression for
suitable application.
3 Write a Program to demonstrate concept of threading and multitasking in Python.


4 Write a Python Program to work with databases in Python to perform operations such
as
a. Connecting to database
b. Creating and dropping tables
c. Inserting and updating into tables.
5 Write a Python Program to demonstrate different types of exception handing.


6 Write a GUI Program in Python to design application that demonstrates
a. Different fonts and colors
b. Different Layout Managers
c. Event Handling
7 Write Python Program to create application which uses date and time in Python.
8 Write a Python program to create server -client and exchange basic information


9 Write a program to Python program to implement concepts of OOP such as
a. Types of Methods
b. Inheritance
c. Polymorphism

10 Write a program to Python program to implement concepts of OOP such as
a. Abstract methods and classes
b. Interfaces

Page 37

Page 35 of 50 Course Code Course Title Credits Lectures
/Week
USCS203 Introduction to OOPs using C++ 2 3

About the Course:
The course aims to introduce a new programming paradigm called Object Oriented Programming. This
will be covered using C++ programming language. C++ is a versatile programming language, which
supports a variety of programming styles, including procedural, object -oriented, and functional
programming. This makes C++ powerful as well as flexible. It can be used to develop software such as
operating systems, databases, and compilers.
Course Objectives:
Learning Outcomes:
After successful completion of this course, students would be able to
Work with numeric, character and textual data and arrays.
Understand the importance of OOP approach over procedural language.
Understand how to model classes and relationships using UML.
Apply the concepts of OOPS like encapsulation, inheritance and polymorphism.
Handle basic file operations.

Unit Topics No of
Lectures











I Introduction to Programming Concepts: Object oriented programming
paradigm, basic concepts of object oriented programming, benefits of
object oriented programming, object oriented languages, applications of
object oriented programming.

Tokens -keywords, identifiers, constants -integer, real, character and string
constants, backslash constants, features of C++ and its basic structure,
simpl e C++ program without class, compiling and running C++ program.

Data Types, Data Input Output and Operators: Basic data types,
variables, rules for naming variables, programming constants, the type cast
operator, implicit and explicit type casting, cout and cin statements,
operators, precedence of operators.

Decision Making, Loops, Arrays and Strings: Conditional statements -if,
if...else, switch loops - while, do...while, for, types of arrays and string and
string manipulations

Unified Modeling Language (UML): Introduction to UML & class
diagrams.

Classes, Abstraction & Encapsulation: Classes and objects, Dot
Operator, data members, member functions, passing data to functions,
scope and visibility of variables in function.










15

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II Constructors and Destructors: Default constructor, parameterized
constructor, copy constructor, private constructor, destructors.

Working with objects: Accessor - mutator methods, static data and static
function, access specifiers, array of objects.

Polymorphism - Binding -static binding & overloading, constructor
overloading function overloading, operator overloading, overloading unary
and binary operators.

Modelling Relationships in Class Diagrams: Association, Aggrega tion-
Composition and examples covering these principles




15









III Inheritance: Defining base class and its derived class, access specifiers,
types of inheritance -single, multiple, hierarchical, multilevel, hybrid
inheritance, friend function and friend class, constructors in derived classes.

Modelling Relationships : Generalization -Specialization and examples
covering these principles

Run time Polymorphism - Dynamic Binding, Function overriding, virtual
function, pure virtual function, virtual base class, abstract class.

Pointers: Introduction to pointers, * and & operators, assigning addresses
to pointer variables, accessing values using pointers, pointers to objects &
this pointer, pointers to derived classes

File Handling: File Str eam classes, opening and closing file -file opening
modes, text file handling, binary file handling.

Applying OOP to solve real life applications: To cover case studies like
library management, order management etc. to design classes covering all
relations hips








15
Textbooks:
1. Object Oriented Programming with C++, Balagurusamy E., 8th Edition, McGraw Hill
Education India.
2. UML & C++: A Practical Guide to Object Oriented Development, Lee/Tepfenhart, Pearson
Education, 2nd Edition2015
Additional References :
1. Mastering C++ by Venugopal, Publisher: McGraw -Hill Education, 2017
2. Let Us C++ by KanetkarYashwant, Publisher: BPB Publications, 2020
3. Object Oriented Analysis and Design by Timothy Budd TMH, 2001

Page 39

Page 37 of 50 Course Code Course Title Credits Lectures
/Week
USCSP203 Introduction to OOPs using C++ - Practical 1 3

1 Program to demonstrate use of data members & member functions.
2 Programs based on branching and looping statements using classes.
3 Program to demonstrate one and two dimensional arrays using classes

4 Program to use scope resolution operator. Display the various values of the same
variables declared at different scope levels.
5 Programs to demonstrate various types of constructors and destructors.
6 Programs to demonstrate use of public, protected & private scope specifiers.
7 Programs to demonstrate single and multilevel inheritance
8 Programs to demonstrate multiple inheritance and hierarchical inheritance
9 Programs to demonstrate inheritance and derived class constructors
10 Programs to demonstrate friend function, inline function, this pointer
11 Programs to demonstrate function overloading and overriding.
12 Programs to demonstrate use of pointers
13 Programs to demonstrate text and binary file handling

Page 40

Page 38 of 50 Course Code Course Title Credits Lectures
/Week
USCS204 Database Systems 2 3

About the Course:
The course introduces the core principles and techniques required in the design and implementation of
database systems. It includes ER Model, Normalization, Relational Model, and Relational Algebra. It
also provides students with theoretical knowledge and practical skills of creating and manipulating data
with an interactive query language (MySQL). It also provide student knowledge and importance of data
protection.
Course Objectives:
To make students aware fundamentals of database system.
To give idea how ERD components helpful in database design and implementation.
To experience the students working with database using MySQL.
To familiarize the student with normalization, database protection and different DCL
Statements.
To make students aware about importance of protecting data from unauthorized users.
To make students aware of granting and revoking rights of data manipulation.
Learning Outcomes:
After successful completion of this cours e, students would be able to
To appreciate the importance of database design.
Analyze database requirements and determine the entities involved in the system and their
relationship to one another.
Write simple queries to MySQL related to String, Maths and Date Functions.
Create tables and insert/update/delete data, and query data in a relational DBMS using MySQL
commands.
Understand the normalization and its role in the database design process.
Handle data permissions.
Create indexes and u nderstands the role of Indexes in optimization search.

Unit Topics No of
Lectures






I Introduction to DBMS – Database, DBMS – Definition, Overview of
DBMS, Advantages of DBMS, Levels of abstraction, Data independence,
DBMS Architecture

Data models - Client/Server Architecture, Object Based Logical Model,
Record Based Logical Model (relational, hierarchical, network)

Entity Relationship Model - Entities, attributes, entity sets, relations,
relationship sets, Additional constraints (key c onstraints, participation
constraints, weak entities, aggregation / generalization, Conceptual Design
using ER (entities VS attributes, Entity Vs relationship, binary Vs ternary,
constraints beyond ER)





15

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ER to Table - Entity to Table, Relationship to tables with and without key
constraints.

DDL Statements - Creating Databases, Using Databases, datatypes,
Creating Tables (with integrity constraints – primary key, default, check,
not null), Altering Tables, Renaming Tables, Dropp ing Tables, Truncating
Tables

DML Statements – Viewing the structure of a table insert, update, delete,
Select all columns, specific columns, unique records, conditional select, in
clause, between clause, limit, aggregate functions (count, min, max, avg,
sum), group by clause, having clause









II Relational data model – Domains, attributes, Tuples and Relations,
Relational Model Notation, Characteristics of Relations, Relational
Constraints - primary key, referential integrity, unique constraint, Null
constraint, Check constraint

Relational Algebra operations (selection, projection, set operations union,
intersection, difference, cross product, Joins –conditional, equi join and
natural joins, division)

Functions – String Functions (concat, instr, left, right, mid, length,
lcase/lower, ucase/upper, replace, strcmp, trim, ltrim, rtrim), Math
Functions (abs, ceil, floor, mod, pow, sqrt, round, truncate) Date Functions
(adddate, datediff, day, month, year, hour, min, sec, now, reverse)

Joining Table s – inner join, outer join (left outer, right outer, full outer)

Subqueries – subqueries with IN, EXISTS, subqueries restrictions, Nested
subqueries, ANY/ALL clause, correlated subqueries








15








III Schema refinement and Normal forms: Functional dependencies, first,
second, third, and BCNF normal forms based on primary keys, lossless join
decomposition.

Database Protection: Security Issues, Threats to Databases, Security
Mechanisms, Role of DBA, Discretionary Access Control, Backing Up and
Restoring databases

Views (creating, altering dropping, renaming and manipulating views)

DCL Statements (creating/dropping users, privileges introduction,
granting/revoking privileges, viewing privileges), Transaction control
commands – Commit, Roll back

Index Structures of Files: Introduction, Primary index, Clustering Index,
Multilevel indexes







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Course Code Course Title Credits Lectures
/Week
USCSP204 Database Systems – Practical 1 3

1. Conceptual Designing using ER Diagrams (Identifying entities, attributes, keys
and relationships between entities, cardinalities, generalization, specialization
etc.)
2. Perform the following:
Viewing all databases
Creating a Database
Viewing all Tables in a Database
Creating Tables (With and Without Constraints)
Inserting/Updating/Deleting Records in a Table
3. Perform the following:
Altering a Table
Dropping/Truncating/Renaming Tables
Backing up / Restoring a Database
4. Perform the following:
Simple Queries
Simple Queries with Aggregate functions
5. Queries involving
Date Functions
String Functions
Math Functions
6. Join Queries
Inner Join
Outer Join Textbooks:
1. “Fundamentals of Database System”, ElmasriRamez, NavatheShamkant, Pearson Education,
Seventh edition, 2017
2. “Database Management Systems”, Raghu Ramakrishnan and Johannes Gehrke, 3rd Edition,
2014
3. “Murach's MySQL”, Joel Murach, 3rd Edition, 3rd Edition, 2019
Additional References :
1. “Database System Concepts”, Abraham Silberschatz,HenryF.Korth,S.Sudarshan, McGraw Hill,
2017
2. “MySQL: The Complete Reference”, VikramVaswani , McGraw Hill, 2017
3. “Learn SQL with MySQL: Retrieve and Manipulate Data Using SQL Commands with Ease”,
AshwinPajankar, BPB Publications, 2020

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7. Subqueries
With IN clause
With EXISTS clause
8. Converting ER Model to Relational Model and apply Normalization on
database. (Represent entities and relationships in Tabular form, Represent
attributes as columns, identifying keys and normalization up to 3rd Normal
Form).
9. Views
Creating Views (with and without check option)
Dropping views
Selecting from a view
10. DCL statements
Granting and revoking permissions
Saving (Commit) and Undoing (rollback)
11. Creating Indexes on data tables.

Page 44

Page 42 of 50 Course Code Course Title Credits Lectures
/Week
USCS205 Calculus 2 3

About the Course:
Calculus is a branch of mathematics that involves the study of rates of change. In Computer Science,
Calculus is used in Machine Learning, Data Mining, Scientific Computing, Image Processing, and
creating the graphics and physics engines for video games, including the 3D visuals for simulations.
Course Objectives:
The prima ry objective of this course is to introduce the basic tools of Calculus which are helpful
in understanding their applications to the real world problems.
The course is designed to have a grasp of important concepts of Calculus in a scientific way.
It covers topics from as basic as definition of functions to partial derivatives of functions in a
gradual and logical way.
The learner is expected to solve as many examples as possible to a get compete clarity and
understanding of the topics covered.
Lear ning Outcomes:
After successful completion of this course, learners would be able to:
Develop mathematical skills and enhance thinking power of learners.
Understand mathematical concepts like limit, continuity, derivative, integration of functions,
partial derivatives.
Appreciate real world applications which use the learned concepts.
Skill to formulate a problem through Mathematical modelling and simulation.

Unit Topics No of
Lectures




I DERIVATIVES AND ITS APPLICATIONS:
Review of Basic Concepts: Functions, limit of a function, continuity of a
function, derivative function.

Derivative In Graphing And Applications: Increase, Decrease,
Concavity, Relative Extreme; Graphing Polynomials, Rational Functions,
Cusps and Vertica l Tangents. Absolute Maxima and Minima, Applied
Maximum and Minimum Problems, Newton‟s Method.



15




II INTEGRATION AND ITS APPLICATIONS:
Integration: An Overview of the Area Problem, Indefinite Integral,
Definition of Area as a Limit; Sigma Notation, Definite Integral, Evaluating
Definite Integrals by Substitution, Numerical Integration: Simpson‟s Rule.

Applications of Integration: Area between two cur ves, Length of a plane
curve.

Mathematical Modeling with Differential Equations : Modeling with



15

Page 45

Page 43 of 50 Differential Equations, Separation of Variables, Slope Fields, Euler ‟s
Method, First -Order Differential Equations and Applications.





III PARTIAL DERIVATIVES AND ITS APPLICATIONS:
Functions of Several Variables : Functions of two or more variables,
Limits and Continuity of functions of two or three variables.

Partial Derivatives: Partial Derivatives, Differentiability, Differentials,
and Local Linearity, Chain Rule, Implicit Differentiation, Directional
Derivatives and Gradients,

Applications ofPartial Derivatives: Tangent Planes and Normal
Vectors, Maxima an d Minima of Functions of Two Variables.




15
Textbooks:
1. Calculus: Early transcendental (10th Edition): Howard Anton, IrlBivens, Stephen Davis, John
Wiley & sons, 2012.
Additional References :
1. Calculus and analytic geometry (9th edition): George B Thomas, Ross L Finney, Addison
Wesley, 1995
2. Calculus: Early Transcendentals (8th Edition): James Stewart, Brooks Cole, 2015.
3. Calculus (10th Edition): Ron Larson, Bruce H. Edwards, Cengage Learning, 2013.
4. Thomas' Calculus (13th Edition): George B. Thomas, M aurice D. Weir, Joel R. Hass, Pearson,
2014.


Course Code Course Title Credits Lectures
/Week
USCSP205 Calculus – Practical 1 3




1 Review of Basic Concepts –
a. Functions of one variable, its domain and range, Operations on
functions
b. Limits of functions of one variable
c. Continuity of functions of one variable
d. Derivatives of functions of one variable


2 Applications of Derivatives I –
a. Increasing and Decreasing functions
b. Concavity and inflection points
c. Relative Extrema
d. Absolute Extrema


3 Applications of Derivatives II –
a. Analysis of polynomials
b. Graphing rational functions
c. Graphs With Vertical Tangents And Cusps
d. Newton‟s method to find approximate solution of an equation

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4 Integration –
a. Finding area using rectangle method and antiderivative method
b. Indefinite and definite integrals
c. Properties of integrals
d. Numerical integration using Simpson‟s rule.

5 Applications of Integration –
a. Area between two curves
b. Length of a plane curve



6 Differential Equations –
a. Solution of a first order first degree differential equation using variable
separable method
b. Solution of a first order linear differential equation using integrating
factor
c. Numerical solution of first-order equations using Euler‟s method
d. Modeling using differential equation


7 Functions of Several Variables –
a. Functions of two or more variables, its domain and range, Operations
on functions, level curves
b. Limits of functions of two or three variables
c. Continuity of functions of two or three variables


8 Partial Derivatives I –
a. Partial derivatives of functions, First and Second order partial
derivatives, Mixed derivative theorem, Higher order partial derivatives
b. Differential for functions of two or three variables
c. Local linear approximation for functions of two or three variables

9 Partial Derivatives II –
a. Chain rule for functions of two or three variables
b. Implicit differentiation
c. Directional derivatives and gradient

10 Applications of Partial Derivatives –
a. Tangent Planes and Normal Vectors for functions of two or three
variables
b. Maxima and Minima of Functions of Two Variables
NOTE Above Practical’s can also to be implemented using Sage Math/ Geogebra.

Page 47

Page 45 of 50 Course Code Course Title Credits Lectures
/Week
USCS206 Statistical Methods 2 3

About the Course:
This course introduces the key concepts in probability, conditional probabilities and distribution theory,
including probability laws, random variables, expectation and variance, functions of random variables
and its probability distributions. Emphasis is placed on theoretical understanding combined with
problem solving using various statistical inferential techniques.
Course Objectives:
To make learner aware about basic probability axioms and rules and its application.
To understand the concept of conditional probability and Independence of events.
To make learner familiar with discrete and continuous random variables as well as standard
discrete and continuous distri butions.
To learn computational skills to implement various statistical inferential approaches.
Learning Outcomes:
After successful completion of this course, learners would be able to
Calculate probability, conditional probability and independence.
Apply the given discrete and continuous distributions whenever necessary.
Define null hypothesis, alternative hypothesis, level of significance, test statistic and p value.
Perform Test of Hypothesis as well as calculate confidence interval for a population parameter
for single sample and two sample cases.
Apply non -parametric test whenever necessary.
Conduct and interpret one -way and two -way ANOVA.

Unit Topics No of
Lectures




I Probability: Random experiment, sample space, events types and
operations of events, Probability definition: classical, axiomatic,
Elementary Theorems of probability (without proof). Conditional
proba bility, „Bayes‟ theorem, independe nce, Examples on Probability.

Random Variables: Concept and definition of a discrete random variable
and continuous random variable. Probability mass function, Probability
density function and cumulative distribution function of discrete and
continuous random variable, Properties of cumulative distribution function.



15



II Mathematical Expectation and Variance: Expectation of a function,
Variance and S.D of a random variable, properties.

Standard Probability distributions: Introduction, properties, examples
and applications of each of the following distributions: Binomial
distribution, Normal distribution, Chi -square distribution, t distribution, F
distribution


15

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III Hypothesis testing: One sided, Two sided hypothesis, critical region, p -
value, tests based on t, Normal and F, confidence intervals.

Analysis of Variance: One-way, two -way analysis of variance.

Non-parametric tests: Need of non-parametric tests, Sign test,
Wilicoxon‟s signed rank test, run test, Kruskal -Walis tests, Chi square test.


15
Textbooks:
1. Gupta, S.C. and Kapoor, V.K. (1987): Fundamentals of Mathematical Statistics, S. Chand and
Sons, New Delhi
2. Goon, A. M., Gupta, M. K. and Dasgupta, B. (1983). Fundamentals of Statistics, Vol. 1, Sixth
Revised Edition, The World Press Pvt. Ltd., Calcutta.
Additional References :
1. Mood, A. M. and Graybill, F. A. and Boes D.C. (1974). Introduction to the Theory of Statistics,
Ed. 3, McGraw Hill Book Company.
2. Hoel P. G. (1971). Introduction to Mathematical Statistics, John Wiley and Sons, New York.
3. Hogg, R.V. and Craig R.G. (1989). In troduction to Mathematical Statistics, Ed. MacMillan
Publishing Co., New York.
4. Walpole R. E., Myers R. H. and Myers S. L. (1985), Probability and Statistics for Engineers and
Scientists
5. Agarwal, B. L. (2003). Programmed Statistics, Second Edition, New Age International
Publishers, New Delhi.




Course Code Course Title Credits Lectures
/Week
USCSP206 Statistical Methods – Practical 1 3


1 Probability -
a. Examples based on Probability definition: classical, axiomatic
b. Examples based on elementary Theorems of probability


2 Conditional probability and independence -
a. Examples based on Conditional probability
b. Examples ba sed on „Bayes‟ theorem
c. Examples based on independence

3 Discrete random variable -
a. Probability distribution of discrete random variable
b. Probability mass function


4 Continuous random variable -
a. Probability distribution of continuous random variable
b. Probability density function

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5 Mathematical Expectation and Variance -
a. Mean of discrete and continuous Probability distribution
b. S.D. and variance of discrete and continuous Probability distribution

6 Standard probability distributions -
a. Calculation of probability, mean and variance based on Binomial distribution
b. Calculation of probability based on Normal distribution



7 Large Sample tests based on Normal (Z) -
a. Test of significance for proportion (Single proportion Ho: P = Po)
b. Test of significance for difference between two proportions (Double proportion
Ho: P1 = P2)
c. Test of significance for mean (Single mean Ho: µ = µ0)
d. Test of significance for difference between two means. (Double mean Ho: µ1 =
µ2)




8 Small sample tests based on t and F -
a. t-test for significance of single mean, population variance being unknown
(Single mean Ho : µ = µ0 )
b. t-test for significance of the difference between two sample means
(Independent samples)
c. t-test for significance of the difference between two sample means (Related
samples)
d. F-Test to Compare Two Variances

9 Analysis of variance -
a. Perform One -way ANOVA
b. Perform Two -way ANOVA


10 Non-parametric tests -
a. Sign test and Wilcoxon Sign rank test
b. Run test
c. Kruskal -Wallis (H) test
d. Chi-square test
Note: Practical no. 6, 7, 8, 9 can also to be implemented using R programming.

Page 50

Page 48 of 50 Course Code Course Title Credits Lectures
/Week
USCS207 E-Commerce & Digital Marketing 2 3

About the Course:
This course introduces the fundamental concepts of e -commerce, its types, the various legal and ethical
issues of e -commerce and different e -commerce applications. The course also aims to introduce basic
principles and types of digital marketing and web and Google analytics
Course Objectives:
To understand increasing significance of E -Commerce and its applications in Business and
Various Sectors
To provide an insight on Digital Marketing activities on various Social Media platforms and its
emerging significance in Business
To understand Latest Trends and Practices in E -Commerce and Digital Marketing, along with
its Challenges and Opportunities for an Organization
Learning Outcomes:
After successful completion of this course, students would be able to
Understand the core concepts of E-Commerce.
Understand the various online payment techniques
Understand the core concepts of digital marketing and the role of digital marketing in business.
Apply digital marketing strategies to increase sales and growth of business
Apply digital marketing through different channels and platforms
Understand the significance of Web Analytics and Google Analytics and apply the same.

Unit Topics No of
Lectures







I Introduction to E -Commerce and E - Business: Definition and competing
in the digital economy, Impact of E -Commerce on Business Models, Factors
Driving e -commerce and e -Business Models, Economics and social impact
of e-Business, opportunities and Challenges, e -Commerce vs m- Commerce,
Different e -Comm erce Models (B2B, B2C, C2B, C2C, B2E), e -
Commerce Applications: e -Trading, e -Learning, e -Shopping, Virtual Reality
& Consumer Experience, Legal and Ethical issues in e-Commerce.

Overview of Electronic Payment system s: Types of Electronic payment
schemes (Credit cards, Debit cards, Smartcards, Internet banking), E-
checks, E -Cash Concepts and applications of EDI and Limitation

Introduction & origin of Digital Marketing : Traditional v/s Digital
Marketing. Digital Mark eting Strategy, The P -O-E-M Framework,
Segmenting & Customizing Messages, The Digital landscape, Digital
Advertising Market in India. Skills required in Digital Marketing. Digital
Marketing Plan.






15

II Social Media Marketing: Meaning, Purpose, types of social media
websites, Social Media Engagement, Target audience, Facebook Marketing:
Business through Facebook Marketing, Creating Advertising Campaigns,
15

Page 51

Page 49 of 50 Adverts, Facebook Marketing Tools, LinkedIn Marketing: Importance of
LinkedIn Marketing, Framing LinkedIn Strategy, Lead Generation through
LinkedIn, Content Strategy, Analytics and Targeting, Twitter Marketing:
Framing content strategy, Twitter Advertising Campaigns, YouTube
Marketing: Video optimization, Promoting on YouT ube, Monetization,
YouTube Analytics

Email Marketing: Types of Emails, Mailing List, Email Marketing tools,
Email Deliverability & Email Marketing automation

Mobile Marketing : Introduction, Mobile Usage, Mobile Advertising,
Mobile Marketing Types, Mobile Marketing Features, Mobile Campaign
Development, Mobile Advertising Analytics

Content Marketing: Introduction, Content marketing statistics, Types of
Content, Types of Blog posts, Content Creation, Content optimization,
Content Management & Distribution, Content Marketing Strategy, Content
creation tools and apps, Challenges of Content Marketing.







III Search Engine Optimization: Meaning, Common SEO techniques,
Understanding Search Engines, basics of Keyword search, Google
rankings, Link Building, Steps to optimize website, On -page and off -page
optimization

Search Engine Marketing: Introduction to SEM, Introduction to Ad
Words - Google Ad Words, Ad Words fundamentals, Ad Placement, Ad
Ranks, Creating Ad Campaigns, Campaign Report Generat ion, Display
marketing, Buying Models: Cost per Click (CPC), Cost per Milli (CPM),
Cost per Lead (CPL), Cost per Acquisition (CPA).

Web Analytics: Purpose, History, Goals & objectives, Web Analytic tools
& Methods. Web Analytics Mistakes and Pitfalls.

Google Analytics: Basics of Google Analytics, Installing Google Analytics
in website, Parameters of Google Analytics, Reporting and Analysis






15
Textbooks:
1. “E-Commerce Strategy, Technologies and Applications”, Whitley, David, Tata McGraw Hill,
2017
2. Digital Marketing, Seema Gupta, McGraw Hill Education, 2nd Edition
Additional References :
1. E-Commerce by S. Pankaj, A.P.H. Publication, New Delhi
2. Fundamentals of Digital Marketing, Punit Singh Bhatia, Pearson, 2nd Edition
3. “Understanding Digital Marketing: M arketing Strategies for Engaging the Digital Generation”,
Damian Ryan, Calvin Jone. Kogan Page, 4th Edition

Page 52

Page 50 of 50 Evaluation Scheme

I. Internal Evaluation for Theory Courses – 25 Marks

(i) Mid-Term Class Test – 15Marks

It should be conducted using any learning management system such as
Moodle (Modular object -oriented dynamic learning environment)
The test should have 15 MCQ’s which should be solved in a time duration of 30
minutes.

(ii) Assignment/ Case study/ Presentations – 10 Marks

Assignment / Case Study Report / Presentation can be uploaded on any learning
management system .

II. External Examination for Theory Courses – 75 Marks
Duration: 2.5 Hours
Theory question paper pattern:
All questions are compulsory.
Question Based on Options Marks
Q.1 Unit I Any 4 out of 6 20
Q.2 Unit II Any 4 out of 6 20
Q.3 Unit III Any 4 out of 6 20
Q.4 Unit I,II and III Any 5 out of 6 15

All questions shall be compulsory with internal choice within the questions.
Each Question maybe sub -divided into subquestions as a, b, c, d, etc. & the
allocation of Marks depends on the weightage of the topic.

III. Practical Examination
Each core subjectcarries50 Marks
40 marks + 05 marks (journal) + 05 marks (viva)
Duration: 2 Hours for each practical course.
Minimum 80% practical from each core subjects are required to be completed.
Certified Journal is compulsory for appearing at the time of Practical Exam
The final submission and evaluation of journal in electronic form using a Learning
Management System / Platform can be promoted by college.

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