MASTER OF COMPUTER APPLICATIONS 2024-2025

Semester I
24CAP101A PROBLEM SOLVING USING PYTHON 5H-4C
Instruction Hours / Week: L: 5 T: 0 P: 0 Marks: Internal: 40 External: 60 Total: 100
End Semester Exam:3 Hours
PREREQUISITE:
Not Required
COURSE OBJECTIVES (CO):
 To master the Python Programming Fundamentals.
 To able to solve problems methodically using Python.
 To expose to practical, real-world applications of Python programming.
COURSE OUTCOMES (COs):
Upon completion of this course, the student will be able to:
COs Course Outcomes Blooms Level
CO1 Learn the Python Syntax and Control Statements Understand
CO2 Explain how to handle Arrays, Strings, and Functions Understand
CO3 Analyze File Systems and Regular Expressions Operations Analyze
CO4 Explain and Utilize Data Structures in Python Programs Apply
CO5 Compare the grid, pack, and place layout managers in Tkinter Analyze

UNIT I PYTHON BASICS 12 HOURS

Introduction to Python – Writing our First Python Program – Data types in python- operators in python -
Input and Output-Control Statements: if..else - if..elif - while – for - infinite loops - nested loops - else
suite – break – continue –pass – assert – return command line arguments.
UNIT II SEQUENTIAL AND NON SEQUENTIAL COLLECTION OPERATIONS 12
HOURS
Arrays in Python: Creating Arrays-Mathematical operations on Arrays- Comparing-Aliasing- Slicing and
Indexing-Strings and Characters - Functions: defining – calling - returning results - Formal and Actual
arguments- Types of actual arguments-Local and Global variables - Recursive function - Anonymous
function - List and Tuples - Dictionaries.
UNIT III OBJECT ORIENTED PROGRAMMING IN PYTHON 12 HOURS
Introduction to Oops: Features of OOPs - Classes and Objects: Creating a class – self variable –
constructor – types of variables and methods – passing members – inner classes - Inheritance and
Polymorphism - Abstract classes and Interfaces - Exceptions.
UNIT IV PYTHON ADVANCES 12
HOURS
Files: Types - open, close and working file - Binary files- with statement – seek() and tell() methods-
Access binary files – zipping and unzipping files – Working with directories - Regular Expressions in
Python-Date and Time: combining -formatting - comparing – sorting - Working with Calendar module.
UNIT V GRAPHICAL USER INTERFACE 12
HOURS
GUI in Python: Root Window - Fonts and Colors- Working with Containers- Canvas- Frame- Types of
Karpagam Academy of Higher Education (Deemed to be University), Coimbatore – 641 021
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 Widget: button-label – message – text – scrollbar - checkbutton – radiobutton – entry – spinbox - listbox -
menu- Creating Tables- Pythons Database Connectivity - CRUD operations.
TOTAL: 60 HOURS
TEXT BOOKS:
1. Nageswara Rao R, (2021). Core Python Programming, 3rd Edition, Dreamtech Press, New
Delhi.
2. Kenneth A. Lambert, (2019). Fundamentals of Python – First Programs, 2nd Edition, Cengage
Publication, New Delhi,
REFERENCE BOOKS:
1. Paul Barry, 2023. Head First Python, 3rd Edition, O’Reilly Media, Beijing.
WEBSITE LINKS:
1. www.php.net/
2. en.wikipedia.org/wiki/PHP
3. www.w3schools.com/PHP/DEfaULT.asp
4. http://www.vlab.co.in/ba-nptel-labs-computer-science-and-engineering
5. http://www.nptelvideos.com/php/php_video_tutorials.php

CO, PO, PSO Mapping

PO PO PO PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO
CO
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2
CO1 3 - 2 3 - - - - 2 - - - - - - - -
CO2 3 - 2 3 - - - - 2 - - - - - - - 2
CO3 3 - 3 3 3 1 - 1 - - - - - - - - -
CO4 3 - 2 3 2 - - - 1 - - - - - - - -
CO5 3 - 3 3 1 1 - - - - - - - - - 2 -
Average 3 0 2.4 3 2 1 0 1 1.7 0 0 0 0 0 0 2 2
1 - Low, 2 - Medium, 3 - High, ‘-' - No Correlation

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MASTER OF COMPUTER APPLICATIONS 2024-2025
Semester I
24CAP102A ADVANCED COMPUTER NETWORKS 5H-4C
Instruction Hours / Week: L: 5 T: 0 P: 0 Marks: Internal: 40 External: 60 Total: 100
End Semester Exam:3 Hours
PREREQUISITE:
Not Required
COURSE OBJECTIVES (CO):
 To focus on advanced networking concepts for next generation network architecture and design.
 To cover SDN and virtualization for designing next generation networks.
 To analyze the different routing algorithms.
COURSE OUTCOMES (COs):
Upon completion of this course, the student will be able to:

COs Course Outcomes Blooms Level

CO1 Compare the various reference models in Network Understand
CO2 Analyze the different routing algorithms Analyze
CO3 Analyze the protocols used in Transport layer Analyze
CO4 Analyze and describe the working principles of Internet. Analyze

CO5 Identify the protocols involved at the application layer. Understand

UNIT I INTRODUCTION & NETWORK LAYER 12 HOURS

Network applications, network hardware, network software, reference models: OSI, TCP/IP, Internet,
Connection-oriented network - X.25, frame relay, Protocol. Network layer: Network Layer Services,
Packet Switching, Performance, provided transport layers, implementation connectionless services,
implementation connection-oriented services, comparison of virtual –circuit and datagram subnets. IPV4
Address, Forwarding of IP Packets, Internet Protocol, ICMP v4, Mobile IP
UNIT II ROUTING ALGORITHMS 12
HOURS
Routing Algorithms–Distance Vector routing, Link State Routing, Path Vector Routing, Unicast Routing
Protocol-Internet Structure, Routing Information Protocol, Open Source Path First, Border Gateway
Protocol V4, Broadcast routing, Multicasting routing, Multicasting Basics, Intradomain Multicast
Protocols, IGMP.
UNIT III TRANSPORT LAYER 12 HOURS
IPv6 Addressing, IPv6 Protocol, Transition from IPv4 to IPv6.Transport Layer Services, connectionless
versus connection oriented protocols. Transport Layer Protocols: Simple Protocol, Stop and Wal, Go-
Karpagam Academy of Higher Education (Deemed to be University), Coimbatore – 641 021
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 Back-N, Selective repeat, Piggy Backing. UDP: User datagram, Services, Applications. TCP: TCP
services, TCP features, segment, A TCP connection, Flow control, error control, congestion control.

UNIT IV INTERNET LAYER 12 HOURS

SCTP: SCTP services SCTP features, packet format, An SCTP association, flow control, error
control .QUALITY OF SERVICE: flow characteristics, flow control to improve QOS: scheduling, traffic
shaping, resource reservation, admission control.
UNIT V APPLICATION LAYER 12 HOURS
Domain name system, electronic mail, World Wide Web: architectural overview, dynamic web document
and http. APPLICATION LAYER PROTOCOLS: Simple Network Management Protocol, File Transfer
Protocol, Simple Mail Transfer Protocol, Telnet.
TOTAL: 60 HOURS
TEXT BOOKS:
1. A. S. Tanenbaum, 2022. Computer Networks, 6th edition, Pearson Education/ PHI, New Delhi,
India.
2. Stallings W, 2024. Data and Computer Communications, 10th edition, Pearson Education India.
REFERENCE BOOKS:
1. Douglas E Comer, 2015. Internet Working with TCP/IP Volume -1, Sixth Edition, Addison-
Wesley Professional.
2. Goransson P, Black C, Culver T, 2016. Software Defined Networks: a Comprehensive Approach,
Morgan Kaufmann.
PO PO PO PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO
CO
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2
CO1 3 - - - - - - - 1 - 1 - - - - - -
CO2 3 - 3 2 3 1 - 1 2 1 - - - - - 1 -
CO3 3 - 3 - 3 1 - 1 2 1 - - - - - - -
CO4 3 - 3 1 3 1 - 1 2 1 1 - - - - - -
CO5 3 - 1 - - - - - 2 - - - - - - - 1
Average 3 0 2.5 1.5 3 1 0 1 1.8 1 1 0 0 0 0 1 1
CO, PO, PSO Mapping
1 - Low, 2 - Medium, 3 - High, ‘-' - No Correlation

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Karpagam Academy of Higher Education (Deemed to be University), Coimbatore – 641 021
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MASTER OF COMPUTER APPLICATIONS 2024-2025
Semester I
24CAP103A ADVANCED DATA STRUCTURES AND ALGORITHM 5H-4C
Instruction Hours / Week: L: 5 T: 0 P: 0 Marks: Internal: 40 External: 60 Total: 100
End Semester Exam:3 Hours
PREREQUISITE:
 Proficiency in fundamental data structures such as arrays, linked lists, stacks, queues, trees.
COURSE OBJECTIVES (CO):
 To learn and use hierarchical data structures and its operations.
 To learn the usage of graph and its applications.
 To select and design data structures and algorithms that is appropriate for problems.
COURSE OUTCOMES (COs):
Upon completion of this course, the student will be able to:
COs Course Outcomes Blooms Level
CO1 Demonstrate the usage of algorithms in computing Understand
CO2 Classify the linear data structures Understand
CO3 Explain tree and perform various operations on a tree Understand
CO4 Examine the solution for solving various computing problems
Analyze
using graph data structure
CO5 Solve sorting, searching and merging problems for input
Create
elements

UNIT I ROLE OF ALGORITHMS IN COMPUTING & COMPLEXITY ANALYSIS 12 HOURS

Algorithms – Algorithms as a Technology -Time and Space complexity of algorithms- symptotic
analysis-Average and worst-case analysis-Asymptotic notation-Importance of efficient algorithms -
Program performance measurement - Recurrences: The Substitution Method – The RecursionTree
Method- Data structures and algorithms. Data Structures: Types of Data Structure – Need of Data
Structures.
UNIT II LINKED LISTS, STACKS AND QUEUES 12
HOURS
Introduction - Representation and Operations: Linear Linked List - Doubly Linked List– Circular Linked
List – Header Linked Lists Applications of Linked list -Stacks: Operations on stacks-Representation of a
stack in memory – Applications of stack – Queues: Operations – Representation of Queues in memory –
Applications of Queues.
UNIT III TREES 12
HOURS
Introduction – Tree terminology – Binary trees – Tournament trees – Binary search trees: Representation
of a binary and Binary search tree –Operations on binary and Binary search tree – Creation – Traversal –
AVL Trees – Threaded binary trees – B Tree – B+ Trees – Red Black Trees – Properties –

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 Implementations – Heap – Heap Implementation.

UNIT IV GRAPHS 12
HOURS
Introduction – Graph terminology – Representation of Graphs –Operations on Graphs – Applications of
Graph - Topological Sort – Minimum Spanning Tree – Kruskal and Prims Algorithm - Finding Shortest
paths – Bellman Ford Algorithm – Dijkstra’s Algorithm - Articulation Points, Bridges, and Biconnected
Components, Strongly connected components – Eulerian Tour – Hamiltonian Tour.
UNIT V – HASHING, SORTING, SEARCHING AND DYNAMIC PROGRAMMING 12
HOURS
Introduction – Direct Address table - Hash Table – Hash Function – Rehashing - Bubble sort – Selection
sort –Insertion Sort – Bucket / Radix Sort - Merge Sort – Quick Sort – Heap Sort – Tree sort – Shell Sort
– Searching: Linear – Binary search. Dynamic Programming: Elements of Dynamic Programming –
Greedy Algorithm – Huffman Coding.
TOTAL: 60 HOURS
TEXT BOOKS:
1. S.Sridhar.(2014). Design and Analysis of Algorithms, Oxford University Press, 1st Edition.
2. R.S.Salaria, (2022). Data structures & Algorithms Using C, 5th Edition, Khanna Book
Publishing Co.Pvt. Ltd.,SRS Enterprises, New Delhi.
REFERENCE BOOKS:
1. ReemaThareja, (2018). Data Structures using C, 2ndEdition, Oxford University Press, New
Delhi.
2. Jean Paul Tremblay and Paul G. Sorensen, (2017). An Introduction to Data Structures with
Applications, 2nd Edition, Tata McGraw Hill, New Delhi.
WEBSITE LINKS:
1. https://www.geeksforgeeks.org/learn-data-structures-and-algorithms-dsa-tutorial/
2. https://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/
3. https://www.programiz.com/dsa

CO, PO, PSO Mapping

PO PO PO PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO
CO
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2
CO1 3 - - - - - - 1 3 1 2 - - - - 1 -
CO2 3 - - - - - - - 2 - - - - - - - -
CO3 3 - - - 2 1 - 1 2 - - - - - - - 1
CO4 3 - 3 2 3 2 - 2 - - - - - - - - -
CO5 3 - 3 3 3 2 - 1 3 - - - - - - - -

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Average 3 0 3 2.5 2.7 1.7 0 1.3 2.5 1 2 0 0 0 0 1 1
1 - Low, 2 - Medium, 3 - High, ‘-' - No Correlation

MASTER OF COMPUTER APPLICATIONS 2024-2025

Semester I
24CAP104A MATHEMATICAL FOUNDATION OF COMPUTER 5H-4C
APPLICATIONS
Instruction Hours / Week: L: 5 T: 0 P: 0 Marks: Internal: 40 External: 60 Total: 100
End Semester Exam:3 Hours
PREREQUISITE:
Not Required
COURSE OBJECTIVES (CO):
 To apply matrix algebra in computer graphics, cryptography, and data analysis.
 To use set theory in database management and software development.
 To apply probability theory in areas like machine learning and cryptography.
 To conduct hypothesis testing with statistical tools and techniques.
 To use PERT and CPM for efficient project scheduling in software development.
COURSE OUTCOMES (COs):
Upon completion of this course, the student will be able to:
COs Course Outcomes Blooms Level
CO1 Apply matrix algebra techniques to solve computational problems in
Understand
fields like computer graphics, cryptography, and data analysis.
CO2 Construct logical arguments and Manipulate sets and relations in
Analyze
various disciplines.
CO3 Analyze uncertainty and apply probability distributions in real-world
Apply
scenarios
CO4 Articulate testing of hypothesis to interpret the results. Analyze
CO5 Proficiently schedule and manage projects using PERT and CPM
Apply
techniques, ensuring efficient resource utilization.

UNIT I MATRIX ALGEBRA 12 HOURS

Matrices – Rank of a matrix – Solving system of equation – Eigen values and Eigen vectors – Cayley –
Hamilton theorem – Inverse of a matrix.
UNIT II MATHEMATICAL LOGIC 12 HOURS
Propositional Logic - Propositional Equivalences - Predicates and Quantifiers - Rules of Inference. Sets -
Operations on sets - Inclusion -exclusion principle - Pigeonhole principle - Relations and their properties

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 - Closures of relations - Equivalence relations -Partial orderings.
UNIT III PROBABILITY DISTRIBUTIONS 12
HOURS
Probability – The axioms of probability – Conditional probability – Baye’s Theorem, Discrete and
Continuous random variables – Binomial, Poisson, Geometric, Uniform, Exponential and Normal
distributions.

UNIT III – TESTING OF HYPOTHESIS 12

HOURS
Testing of Hypothesis: Introduction to Inferential Statistics: Null and alternative hypothesis, Type I and
Type II errors, Standard error, level of significance, acceptance and rejection regions and procedure for
testing hypothesis. Large sample test - Z test - tests for means, variances and proportions, small sample
tests based on t, F and Chi- square distributions.
UNIT V SCHEDULING BY PERT AND CPM 12
HOURS
Network Construction – Critical Path Metod – Project Evaluation and Review Technique - Resource
Analysis in Network Scheduling.
TOTAL: 60 HOURS
TEXT BOOKS:S:
1. Bronson, R, (2011). Matrix Operation, Schaum’s outline series, Tata McGraw Hill, New York.
2. Sharma. J. K, (2011). Discrete Mathematics, Third Edition, Rajiv Beri for Macmillan
Publishers India Ltd. New Delhi.
REFERENCE BOOKS::
1. Veerarajan, (2017). Fundamentals of Mathematical Statistics, Yesdee Publishing Pvt Ltd.
2. Pillai.R.S.N, Bagavathy, (2002). Statistics, S. Chand & Compony Ltd, New Delhi.
3. Kandiswarup. P. K. Gupta and Man Mohan, (2011). Operations Research, 12th Revised Editions,
S. Chand & Sons Education Publications, New Delhi.
WEBSITE LINKS
1. https://www.stat.cmu.edu/~ryantibs/statcomp/
2. https://www.stat.cmu.edu/~cshalizi/statcomp/
3. https://www.r-project.org

CO, PO, PSO Mapping

PO PO PO PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO
CO
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2
CO1 3 3 - - 3 - - - - - - - - - - - -
CO2 3 3 - - 2 2 - - - - - - - - - - -
CO3 3 3 - - - - - - - - - - - - - - -

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 CO4 3 3 3 3 2 - - - - - - - - - - - -
CO5 3 3 3 3 - - - - - 2 1 2 - - - - -
Average 3 3 3 3 2 2 0 0 0 2 1 2 0 0 0 0 0
1 - Low, 2 - Medium, 3 - High, ‘-' - No Correlation

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MASTER OF COMPUTER APPLICATIONS 2024-2025
Semester I
24CAP105PE1 PROFESSIONAL ELECTIVE - 1 4H-3C
Instruction Hours / Week: L: 4 T: 0 P: 0 Marks: Internal: 40 External: 60 Total: 100
End Semester Exam:3 Hours

Karpagam Academy of Higher Education (Deemed to be University), Coimbatore – 641 021

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MASTER OF COMPUTER APPLICATIONS 2024-2025
Semester I
24CAP111A PROBLEM SOLVING USING PYTHON - PRACTICAL 5H-3C
Instruction Hours / Week: L: 0 T: 0 P: 5 Marks: Internal: 40 External: 60 Total: 100
End Semester Exam:3 Hours
PREREQUISITE:
Not Required
COURSE OBJECTIVES (CO):
 To implement different file handling operations such as reading from and writing to files,
handling exceptions, and manipulating file content.
 To develop Python code to interact with databases using libraries like SQLite or MySQL,
including CRUD operations (Create, Read, Update, Delete).
 To explore various GUI controls (widgets) using libraries like Tkinter or PyQt to create
interactive graphical user interfaces and understand their performance implications.
COURSE OUTCOMES (COs):
Upon completion of this course, the student will be able to:
COs Course Outcomes Blooms Level
CO1 Apply the fundamental concepts of python programming on real time Apply
applications
CO2 Construct python code to perform various operations using sequential and Create
non-sequential collections
CO3 Develop python applications using object oriented programming concepts Create

CO4 Apply operations on files, search the patterns using regular expression and Apply
working with date and time modules
CO5 Develop real-time applications to know about the interaction between Create
front-back end.

List of Programs TOTAL: 60 HOURS

1. Explore the concept of control statement and functions in simple python programs.
2. Write a python code to perform various operations with strings and arrays
3. Perform various Searching methods – Linear Search & Binary Search
4. Perform various Sorting methods – Selection Sort, Insertion Sort, Merge Sort, Quick Sort.
5. Perform various operations in tuple and list
6. Perform various operations in Dictionary
7. Show the performance Date and Time module in Python
8. Design a python program to implement different types of file functions
9. Develop a Python code to interact with Databases
10. Show the performances of various controls in GUI Programming
11. Practice the techniques in data science to extract the knowledge
12. Show the functions of Matplotlib to visualize the data
TOTAL: 60 HOURS

Karpagam Academy of Higher Education (Deemed to be University), Coimbatore – 641 021

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 TEXT BOOKS:
1. Nageswara Rao R, (2021). Core Python Programming, 3rd Edition, Dreamtech Press, New
Delhi.
2. Kenneth A. Lambert, (2019). Fundamentals of Python – First Programs, 2nd Edition, Cengage
Publication, New Delhi.
REFERENCE BOOK:
1. Paul Barry, (2023). Head First Python, 3rd Edition, O’Reilly Media, Beijing.
WEBSITE LINKS:
1. www.php.net/
2. en.wikipedia.org/wiki/PHP
3. www.w3schools.com/PHP/DEfaULT.asp
4. http://www.vlab.co.in/ba-nptel-labs-computer-science-and-engineering
5. http://www.nptelvideos.com/php/php_video_tutorials.php

CO, PO, PSO Mapping

PO PO PO PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO
CO
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2
CO1 3 - 3 2 - 2 - 1 2 1 - - - - - 1 -
CO2 3 - 3 - 3 2 - - 2 - - - - - - - -
CO3 3 - 3 3 2 2 - - 1 - 1 - - - - - 2
CO4 3 - 3 3 2 2 - - 2 - - - - - - - -
CO5 3 2 3 3 3 2 2 1 - - 1 - - - - - 2
Average 3 2 3 2.8 2.5 2 2 1 1.8 1 1 - - - - 1 2
1 - Low, 2 - Medium, 3 - High, ‘-' - No Correlation

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MASTER OF COMPUTER APPLICATIONS 2024-2025
Semester I
24CAP112A ADVANCED DATA STRUCTURES AND ALGORITHM - 4H-2C
PRACTICAL
Instruction Hours / Week: L: 0 T: 0 P: 4 Marks: Internal: 40 External: 60 Total: 100
End Semester Exam:3 Hours
PREREQUISITE:
Not Required
COURSE OBJECTIVES (CO):
 To implement stack operations (push, pop, peek) using an array-based data structure.
 To explore algorithms related to maintaining balance (AVL trees, Red-Black trees) for efficient
search and retrieval.
 To implement merge sort and quick sort algorithms for sorting lists of numbers.
COURSE OUTCOMES (COs):
Upon completion of this course, the student will be able to:
COs Course Outcomes Blooms Level
CO1 Solve the problems using linear data structures Create
CO2 Construct a tree and perform various operations on a tree along with
Create
implementation
CO3 Examine the solution for solving various computing problems using
Analyze
graph data structure
CO4 Make use of Hashing Techniques to generate hash address and to
Apply
resolve the collision on it
CO5 Design sorting, searching and merging of input elements Create

List of Programs TOTAL: 48 HOURS

1. Implementation of singly Linked List Operations
2. Develop a program to perform various stack operations using an array
3. Implement a Program using Queue Data Structures.
4. Implementation of a Binary Search Tree
5. Implement binary tree traversal: in-order, pre-order, post-order
6. Construct a Minimum Spanning Tree
7. Sort characters by frequency Using Hash table
8. Write a program to implement hash table.
9. Sort the given List of Numbers using topological sort
10. Performing Linear and Binary Search
11. Performing Bubble Sort and Insertion Sort
12. Sort the given List of Numbers using Merge and Quick Sort
TOTAL: 48 HOURS

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 TEXT BOOKS:
1. R.S.Salaria, (2022). Data structures & Algorithms Using C, 5th Edition, Khanna Book
Publishing Co.Pvt. Ltd.,SRS Enterprises, New Delhi.
2. ReemaThareja, (2018). Data Structures using C, 2nd Edition, Oxford University Press, New
Delhi.
REFERENCE BOOKS:
1. Jean Paul Tremblay and Paul G. Sorensen, (2017). An Introduction to Data Structures with
Applications, 2nd Edition, Tata McGraw Hill, New Delhi.
WEBSITE LINKS:
1. https://www.gatevidyalay.com/algorithms/
2. http://www.vssut.ac.in/lecture_notes/lecture1428551222
3. https://aunotes.in/t/cs8451-design-and-analysis-of-algorithms-notes/939
4. https://www2.cs.duke.edu/courses/fall08/cps230/Book

CO, PO, PSO Mapping

PO PO PO PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO
CO
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2
CO1 3 - 3 3 2 2 - 1 2 - 2 - - - - 2 -
CO2 3 - 3 2 2 2 - 1 2 1 - - - - - - -
CO3 3 - 3 2 2 2 - 1 2 - - - - 1 - - 3
CO4 3 - 2 2 2 2 - 1 2 - - - - - - - -
CO5 3 - 3 3 2 2 - 1 2 - - - - - - - 3
Average 3 - 2.8 2.4 2 2 - 1 2 1 2 - - 1 - 2 3
1 - Low, 2 - Medium, 3 - High, ‘-' - No Correlation

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MASTER OF COMPUTER APPLICATIONS 2024-2025
SEMESTER I
JOURNAL PAPER ANALYSIS & PRESENTATION 2H-0C
Instruction Hours / Week: L:2 T:0 P:0 Marks: Internal:00 External:00 Total:000
End Semester Exam: 3 Hours

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MASTER OF COMPUTER APPLICATIONS 2024-2025
SEMESTER I
24CAP101B PYTHON FOR DATA SCIENCE 5H-5C
Instruction Hours / Week: L:5 T:0 P:0 Marks: Internal:40 External:60 Total:100
End Semester Exam:3Hours
PREREQUISITE:

 Probability and Statistics, Programming Skills, Python Libraries

COURSE OBJECTIVES (CO):

 To understand the basics of Python syntax and semantics.
 To create interactive visualizations using libraries like Plotly.
 To implement machine learning algorithms such as linear regression, decision trees, and
clustering.
COURSE OUTCOMES (COs):
At the end of this course, students will be able to

COs Course Outcomes Blooms Level

CO1 Understand the concepts of the various programming constructs of Understand
Python programming
CO2 Make use of object oriented concepts to solve real world problems Remember
CO3 Analyze the basics of python and standard modules used for data Analyze
science with hands-on.
CO4 Understand the data structures and visualization used for data Understand
science with hands-on.
CO5 Evaluate the machine learning libraries used for data science with Evaluate
hands-on.

UNIT I PYTHON - DATA STRUCTURES, OOPS & MODULES 12 HOURS

Data structures: Dictionaries - Maps - Hash Tables - Array Data Structures - Records - Structs - Data
Transfer Objects - Sets and Multisets-Stacks (LIFOs) - Queues (FIFOs) ; Python : Python installation -
Python OOPs - Polymorphism in OOPs programming - Python String Concatenation - Print Exception in
Python - Python Libraries - Python Pandas - Python Matplotlib - Python Seaborn - Python SciPy - Chatbot
in Python - Machine Learning using Python - Exploratory Data Analysis in Python - Open CV Python -
Tkinter - Pythons Turtle Module - PyGame in Python - Pytorch - Scrapy - Web Scraping - Django -
Python Programs - Types of Data structure in Python - Built in data structures - User defined data
structures; Object Oriented Concepts and Design : APIs and Data Collection - Simple API - REST APIs &
HTTP Requests - Web scraping - HTML for Web Scraping - file formats
UNIT II PYTHON – NUMPY, PANDAS & DS LIBRARIES 12 HOURS
Installation and setup : Anaconda Distribution - Anaconda Navigator to create a New Environment -
Startup and Shutdown Process - Intro to the Jupyter Lab Interface - Code Cell - execution; Python : Basic
datatypes - Operators - variables - Built in Functions - Custom Functions - String Methods - Lists - Index
Positions and Slicing - Navigating Libraries using Jupyter Lab; Series : Create series object from a list and

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dictionary - The head and Tail methods - Passing Series to Python Built-In Functions – Methods for Data
sorting ; Dataframe : Methods and Attributes between Series and DataFrames - Fill in Missing Values -
Filtering data and methods in Dataframe - Data Extraction in dataframes -Working with Text Data -
Merging Dataframes; Data Mining - Data Processing and Modelling - Data Visualization
UNIT III VISUALIZATION 12 HOURS
Introduction to Matplotlib - Matplotlib Basics - Matplotlib - Understanding the Figure Object - Matplotlib
- Implementing Figures and Axes - Matplotlib - Figure Parameters - Matplotlib Styling - Legends -
Matplotlib Styling - Colors and Styles - Advanced Matplotlib Commands - Introduction to Seaborn -
Scatterplots with Seaborn - Distribution Plots - Part One - Understanding Plot Types - Distribution Plots -
Part Two - Coding with Seaborn - Categorical Plots - Statistics within Categories - Understanding Plot
Types - Categorical Plots - Statistics within Categories - Coding with Seaborn - Categorical Plots -
Distributions within Categories - Understanding Plot Types - Categorical Plots - Distributions within
Categories - Coding with Seaborn - Seaborn - Comparison Plots - Understanding the Plot Types - Seaborn
- Comparison Plots - Coding with Seaborn - Seaborn Grid Plots - Seaborn - Matrix Plots.
UNIT IV REGRESSION AND CLASSIFICATION 12 HOURS
Introduction to Linear Regression : Cost Functions - Gradient Descent - Python coding Simple - Overview
of Scikit-Learn and Python - Residual Plots - Model Deployment and Coefficient Interpretation -
Polynomial Regression - Theory and Motivation - Creating Polynomial Features - Training and Evaluation
- Bias Variance Trade-Off - Polynomial Regression - Choosing Degree of Polynomial - Model
Deployment - Feature Scaling; Introduction to Cross Validation : Regularization Data Setup - Ridge
Regression Theory - Lasso Regression - Background and Implementation - Elastic Net - Feature
Engineering and Data Preparation; Dealing with Outliers - Dealing with Missing Data - Evaluation of
Missing Data - Filling or Dropping data based on Rows - Fixing data based on Columns - Dealing with
Categorical Data - Encoding Options - Cross Validation - Test - Validation - Train Split - cross_val_score
- cross validate - Grid Search; Linear Regression Project: The Logistic Function - Logistic Regression -
Theory and Intuition; Linear to Logistic: Logistic Regression - Theory and Intuition - Linear to Logistic
Math; Logistic Regression: Theory and Intuition Logistic Regression Model Training - Classification
Metrics - Confusion Matrix and Accuracy - Classification Metrics - Precison, Recall, F1-Score - ROC
Curves - Logistic Regression with Scikit-Learn - Performance Evaluation - Multi-Class Classification with
Logistic Regression - Data and EDA – Model.
UNIT V UNSUPERVISED AND ADVANCED MACHINE LEARNING 12 HOURS
Introduction to KNN Section: KNN Classification, KNN Coding with Python - Choosing K, KNN
Classification Project Exercise; Introduction & history of Support Vector Machines- Hyperplanes and
Margins, Kernel Intuition, Kernel Trick and Mathematics; SVM with Scikit-Learn and Python –
Classification, Regression Tasks; Introduction to Tree Based Methods- Decision Tree, Understanding Gini
Impurity; Constructing Decision Trees with Gini Impurity, Coding Decision Trees; Introduction to
Random Forests-Key Hyperparameters, Number of Estimators and Features in Subsets, Bootstrapping and
Out-of-Bag Error; Coding Classification with Random Forest Classifier, Coding Regression with Random
Forest Regressor, Advanced Models. Introduction to K-Means Clustering Section; K-Means Color

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 Quantization; K-Means Clustering Exercise Overview, Solution; Introduction to Hierarchical Clustering,
Coding - Data and Visualization, Scikit-Learn; Introduction to Principal Component Analysis(PCA)-
Manual Implementation in Python-SciKit-Learn.
TOTAL: 60 HOURS

TEXT BOOKS:
1 Fuentes, A. (2018). Become a Python Data Analyst. Packet Publishing.
2 Motwani, B. (2020). Data Analytics using Python. Wiley.
3 Damji, J. S. (2020). Learning Spark: Lightning-Fast Data Analytics (2nd ed.). Shroff/O'Reilly.

REFERENCE BOOKS:
1 Barry, P. (2016). Head First Python (2nd ed .). O'Reilly Media.
2 McKinney, W. (2022). Python for Data Analysis: Data Wrangling with pandas, NumPy, and
Jupyter (3rd ed.). O'Reilly Media.
3 Lambert, K. A. (2019). Fundamentals of Python – First Programs (2nd ed.). Cengage Publication.

WEBSITES:
1 http://docs.python.org/3/tutorial/index.html
2 http://interactivepython.org/courselib/static/ pythons
3 http://www.ibiblio.org/g2swap/byteofpython/read/

CO, PO, PSO Mapping

PO PO PO PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO
CO
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2
CO1 3 - 3 2 - 2 - 1 2 1 - - - - - 1 -
CO2 3 - 3 - 3 2 - - 2 - - - - - - - -
CO3 3 - 3 3 2 2 - - 1 - 1 - - - - - 2
CO4 3 - 3 3 2 2 - - 2 - - - - - - - -
CO5 3 2 3 3 3 2 2 1 - - 1 - - - - - 2
Average 3 2 3 2.8 2.5 2 2 1 1.8 1 1 - - - - 1 2

1 - Low, 2 - Medium, 3 - High, ‘-' - No Correlation

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MASTER OF COMPUTER APPLICATIONS 2024-2025
SEMESTER I
24CAP102B APPLIED MACHINE LEARNING 4H-4C
Instruction Hours / Week: L:4 T:0 P:0 Marks: Internal:40 External:60 Total:100
End Semester Exam:3Hours
PREREQUISITE:

 Probability and Statistics, Data Mining Concepts

COURSE OBJECTIVES (CO):

 To introduce students to the concepts and techniques of Machine Learning.
 To be able to formulate machine learning problems corresponding to different applications.
 To apply the algorithms to a real-world problem, optimize the models learned and report on the
expected accuracy that can be achieved by applying the models.

COURSE OUTCOMES (COs):

At the end of this course, students will be able to

COs Course Outcomes Blooms Level

CO1 Know about Supervised Learning, Support Vector Understand
Machines, Unsupervised Learning
CO2 Get the knowledge about Feature Engineering, Statistical Remember
Data Analysis, Outlier Analysis and Detection
CO3 Learn about ML Model Development, Model Evaluation Evaluate
Techniques, Model Deployment and Inferences, Model
Explainability
CO4 Recognize the importance and value of Operations Apply
Research and mathematical modelling in solving practical
problems in industry
CO5 Define and formulate linear programming problems and Analyze
appreciate their limitations

UNIT I SUPERVISED LEARNING 10 HOURS

Implement and understand the cost function and gradient descent for multiple linear regression -
Implement and understand methods for improving machine learning models by choosing the learning rate
- plotting the learning curve - performing feature engineering - applying polynomial regression -
Implement and understand the logistic regression model for classification -Learn why logistic regression is
better suited for classification tasks than the linear regression model is - Implement and understand the
cost function and gradient descent for logistic regression - Understand the problem of - overfitting -
improve model performance using regularization - Implement regularization to improve both regression
and classification models
UNIT II ADVANCED LEARNING ALGORITHMS 10 HOURS
Build a neural network for binary classification of handwritten digits using TensorFlow - Gain a deeper
understanding by implementing a neural network in Python from scratch - Optionally learn how neural
network computations are vectorized to use parallel processing for faster training and prediction - Build a
neural network to perform multi-class classification of handwritten digits in TensorFlow -using categorical

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cross-entropy loss functions and the SoftMax activation - Learn where to use different activation functions
– ReLu - linear - sigmoid - SoftMax in a neural network - depending on the task you want your model to
perform - Use the advanced Adam optimizer to train your model more efficiently - Discover the value of
separating your data set into training - cross-validation -test sets - Choose from various versions of your
model using a cross-validation dataset -evaluate its ability to generalize to real- world data using a test
dataset - Use learning curves to determine if your model is experiencing high bias or high variance
UNIT III ADVANCED LEARNING ALGORITHMS 10 HOURS
Learn which techniques to apply regularization - adding more data - adding or removing input features to
improve your model’s performance - Learn how the bias-variance trade-off is different in the age of deep
learning - and apply Andrew Ng’s advice for handling bias and variance when training neural networks -
Learn to apply the iterative loop of machine learning development to train - evaluate - tune your model -
Apply data-centric AI to not only tune your model but tune your data using data synthesis or data
augmentation to improve your model’s performance - Build decision trees and tree ensembles - such as
random forest and XGBoost - boosted trees - to make predictions - Learn when to use neural network or
tree ensemble models for your task - as these are the two most commonly used supervised learning models
in practice today.
UNIT IV UNSUPERVISED LEARNING 9 HOURS
Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly
detection - Build recommender systems with a collaborative filtering approach and a content-based deep
learning method - Build a deep reinforcement learning model - Implement K-mean clustering - Implement
anomaly detection - Learn how to choose between supervised learning or anomaly detection to solve
certain tasks.
UNIT V RECOMMENDER SYSTEMS 9 HOURS
Build a recommender system using collaborative filtering - Build a recommender system using a content-
based deep learning method - Build a deep reinforcement learning model (Deep Q Network)." -
Histograms - Box Plots etc - use of frequency distributions – mean comparisons - cross tabulation -
statistical inferences using chi square - t-test and ANOVA - Outlier Analysis and Detection - outlier
analysis - density based and distance based.
TOTAL: 48 HOURS
TEXT BOOKS:
1 Li, H. (2023). Machine Learning Methods. Springer Nature Singapore.
2 Rao, R. N. (2022). Machine Learning in Data Science Using Python. Dreamtech Press.
REFERENCE BOOKS:
1 Alpaydin, E. (2014). Introduction to Machine Learning (3rd ed., Adaptive Computation and
Machine Learning Series). MIT Press.
2 Aggarwal, C. C. (2018). Neural Networks and Deep Learning (1st Kindle ed.).
WEBSITES:
1 https://ai.google/education/
2 https://machinelearningmastery.com/

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 CO, PO, PSO Mapping

PO PO PO PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO
CO
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2
CO1 2 1 - - - - 1 - - - - - - - - - 1
CO2 3 2 1 - - - - - - - - - - - - - 1
CO3 2 1 - - - - 1 - - - - - - - - - 1
CO4 2 1 - - - - - - - - - - - - - - 1
CO5 3 2 1 - - - 1 - - - - - - - - - 1
Average 2.4 1.4 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1
1 - Low, 2 - Medium, 3 - High, ‘-' - No Correlation

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22
MASTER OF COMPUTER APPLICATIONS 2024-2025
SEMESTER I
24CAP103B DATA ENGINEERING 4H-4C
Instruction Hours / Week: L:4 T:0 P:0 Marks: Internal:40 External:60 Total:100
End Semester Exam:3Hours
PREREQUISITE:

 Database Concepts, Programming Skills, Data Analysis Concepts

COURSE OBJECTIVES (CO):

 To understand the fundamentals of data engineering and its importance in modern data-driven
applications.
 To representation of complex and voluminous data.
 To identify and design the various components of an Information Retrieval system
COURSE OUTCOMES (COS):
At the end of this course, students will be able to

COs Course Outcomes Blooms Level

CO1 Identify and explain different data formats and their use cases, Apply
including structured, semi-structured, and unstructured data.
CO2 Describe various data ingestion techniques, such as ETL, and stream Understand
processing, and their advantages and limitations.
CO3 Perform data profiling and analyze data quality metrics to ensure data Understand
accuracy, completeness, and consistency.
CO4 Design and implement effective storage and retrieval methods for Evaluate
large-scale data sets, including relational databases, NoSQL
databases, and distributed file systems.
CO5 Apply data engineering principles to real-world scenarios, such as Apply
data warehousing, big data analytics, and machine learning.

UNIT I DATA TYPES & FORMATS 10 HOURS

Introduction to Data Types and Formats - Types of Data - Structured vs. Unstructured Data - Formats of
Data - Semi-Structured Data - Data Type Conversion and Transformation - Data Serialization - Choosing
the Right Data Type and Format - Tools and Technologies for Data Types and Formats.
UNIT II DATA INGESTION TECHNIQUES 10 HOURS
Introduction to Data Ingestion - Streaming Data Ingestion - Batch Data Ingestion - Hybrid Data Ingestion -
Data Ingestion vs. Data Integration - Data Ingestion Challenges - Tools and Solutions for Data Ingestion -
StreamSets DataOps Platform - Benefits of Data Ingestion - Data Ingestion Framework.
UNIT III DATA PROFILING & VISUAL REPRESENTATION VIA VARIOUS 10 HOURS
TOOLS (PANDAS)
Introduction to Data Profiling and Visualization - Exploratory Data Analysis (EDA) with Pandas - Steps
Involved in Exploratory Data Analysis (EDA) Data Analysis (EDA) with Pandas - Market Analysis with
Exploratory Data Analysis (EDA) - Data Analytics and Its Future Scope - Data Analytics with Python -
Top Business Intelligence Tools - Application of Data Analytics - Retrieving and Cleaning Data -
Exploratory Data Analysis and Feature Engineering - Inferential Statistics and Hypothesis Testing -

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 Descriptive Statistics - Types of Descriptive Statistics - Concepts of Populations, Samples, and Variables -
Statistical Methods for Describing Data Characteristics - Real-World Applications of Descriptive Statistics
using Excel - Types of Missing Data and Handling Techniques.
UNIT IV STORAGE AND RETRIEVAL METHODS 9 HOURS
Introduction to Storage and Retrieval - Types of Data and Storage Methods - Local vs. Distributed Storage
& Retrieval - Hardware Aspects of Storage & Retrieval - Choosing Storage Methods - Data Partitioning
and Sharding - Data Replication and Redundancy - Data Compression and Encoding - Data Archiving and
Retrieval - Backup and Disaster Recovery - Data Lifecycle Management.
UNIT V DATA LINEAGE ANALYSIS 9 HOURS
Introduction to Data Lineage Analysis - Building a Data Flow - ETL (Extract, Transform, Load) Process -
Usage of Data Warehouse - Edge Intelligence in Data Flow - Understanding Data Lineage - How Data
Lineage Works - Benefits of Data Lineage - Data Lineage Tool Features.
TOTAL: 48 HOURS
TEXT BOOKS:
1 Judd, C. M. (2017). Data Analysis: A Model Comparison Approach To Regression, ANOVA, and
Beyond (3rd ed.). Routledge.
2 Bonnefoy, P.-Y., Chaize, E., Mansuy, R., & Tazi, M. (2024). The Definitive Guide to Data
Integration (1st ed.). Packt Publishing.
REFERENCE BOOKS:
1 Baeza-Yates, R., & Ribeiro-Neto, B. (2011). Modern Information Retrieval: The Concepts and
Technology behind Search (2nd ed., ACM Press Books).
2 Reis, J., & Housley, M. (2022). Fundamentals of Data Engineering: Plan and Build Robust Data
Systems. Grayscale Indian Edition.
WEBSITES:
1 https://www.datacamp.com/tutorial/category/data-engineering
2 https://www.codecademy.com/catalog/subject/data-engineering
CO, PO, PSO Mapping

PO PO PO PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO
CO
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2
CO1 3 - 3 3 2 2 - 1 2 - 2 - - - - 2 -
CO2 3 - 3 2 2 2 - 1 2 1 - - - - - - -
CO3 3 - 3 2 2 2 - 1 2 - - - - 1 - - 3
CO4 3 - 2 2 2 2 - 1 2 - - - - - - - -
CO5 3 - 3 3 2 2 - 1 2 - - - - - - - 3
Average 3 - 2.8 2.4 2 2 - 1 2 1 2 - - 1 - 2 3

1 - Low, 2 - Medium, 3 - High, ‘-' - No Correlation

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24
MASTER OF COMPUTER APPLICATIONS 2024-2025
SEMESTER I
24CAP104B MATHEMATICAL FOUNDATION FOR DATA SCIENCE 4H-4C
Instruction Hours / Week: L:4 T:0 P:0 Marks: Internal:40 External:60 Total:100
End Semester Exam:3Hours
PREREQUISITE:

 Algebra, Probability and Statistics, Programming Skills.

COURSE OBJECTIVES (CO):

 To refresh the statistical knowledge learnt earlier with hands-on practical expertise
 To understand and manipulate data in high-dimensional spaces.
 To model uncertainty, make inferences about populations from samples, and make predictions.
COURSE OUTCOMES (COS):
At the end of this course, students will be able to

COs Course Outcomes Blooms Level

CO1 Refresh the mathematics knowledge with respect to Linear Remember
algebra, Vectors, Projections, Principal Component Analysis and
Generative Models
CO2 Refresh the mathematics knowledge with respect to Matrices, Understand
Gradient Calculus, Optimization models.
CO3 Refresh the mathematics knowledge with respect to probability, Apply
statistics.
CO4 Find information about the population on the basis of a random Evaluate
sample taken from that population and also to choose an
appropriate test procedure under the test of significance
CO5 Apply mathematical concepts to real-world data science Apply
problems.

UNIT I LINEAR ALGEBRA 10 HOURS

Systems of Linear Equations - Machine learning motivation - A geometric notion of singularity - Singular
vs non-singular matrices - Linear dependence and independence - Matrix row-reduction - Row operations
that preserve singularity - The rank of a matrix - Row echelon form - Reduced row echelon form- LU
decomposition- Solving Systems of Linear Equations - Machine learning motivation - Solving non-
singular systems of linear equations - Solving singular systems of linear equations - Solving systems of
equations with more variables - Gaussian elimination.
UNIT II PROBABILITY & STATISTICS 10 HOURS
Introduction to probability - Concept of probability: repeated random trials - Conditional probability and
independence - Random variables - Cumulative distribution function - Discrete random variables:
Binomial distribution - Probability mass function - Continuous random variables: Uniform distribution -
Continuous random variables: Gaussian distribution -Joint distributions - Marginal and conditional
distributions - Independence - covariance - Multivariate normal distribution - Sampling and point
estimates - Interval estimation -Confidence intervals – Confidence Interval for mean of population -
Biased vs Unbiased estimates-Maximum likelihood estimation - Intuition behind maximum likelihood
estimation - Hypothesis testing - Describing samples: sample proportion and sample mean - Two types of
Karpagam Academy of Higher Education (Deemed to be University), Coimbatore – 641 021 25
 errors - Test for proportion and means - Two sample inference for difference between groups.
UNIT III BAYESIAN STATISTICS & ITS APPLICATIONS IN VARIOUS 10 HOURS
FIELDS
Bayesian statistics and its applications in various fields - Bayesian Learning: Bayes theorem - maximum
likelihood and least squared error hypotheses – Naïve Bayes classifier- Bayesian belief networks- gradient
ascent training of Bayesian networks- learning the structure of Bayesian networks- the EM algorithm-
mixture of models- Markov models- hidden Markov models - Time series analysis and forecasting
techniques - Basic Properties of time-series data: Distribution and moments- Stationarity- Autocorrelation-
Heteroscedasticity- Normality- Survival Analysis.
UNIT IV NON-PARAMETRIC STATISTICS 9 HOURS
Non-parametric Statistics - Chi square test- Sign test -Wilcoxon signed rank test - Mann Whitney test -
Run test - Kolmogorov Smirnov test - Spearmann and Kendall’s test - Tolerance region.
UNIT V MULTIVARIATE STATISTICAL METHODS FOR ANALYZING 9 HOURS
COMPLEX DATASETS
Multivariate statistical methods for analysing complex datasets - Factor Analysis - Cluster Analysis-
Regression Analysis - Discriminant Analysis.
TOTAL: 48 HOURS
TEXT BOOKS:
1 Phillips, J. M. (2021). Mathematical Foundations for Data Analysis. Springer Series.
2 Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data
Mining, Inference, and Prediction (2nd ed.). Springer.
REFERENCE BOOKS:
1 Thompson, S. K. (2012). Sampling. John Wiley & Sons.
2 Montgomery, D. C. (2008). Introduction to Quality Control (6th ed.). John Wiley & Sons.
WEBSITES:
1 https://ibse.iitm.ac.in/course/math-foundations-of-ds/
2 https://medium.com/illumination/i-found-the-4-mathematical-foundations-that-are-essential-for-
data-science-ebe449aa30ce
CO, PO, PSO Mapping

PO PO PO PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO
CO
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2
CO1 3 - 2 2 3 2 - 3 3 - 1 - - - - 3 2
CO2 3 - 1 1 3 2 - 2 3 - - - - - - 2 1
CO3 3 - 1 - - - - - - - 2 - - - - - -
CO4 3 - 2 - 3 3 - 1 2 - 1 - - - - - -
CO5 3 - - - - - - - - - 2 - - - - - -
Average 3 - 1.5 1.5 3 2.3 - 2 2.7 - 1.5 - - - - 2.5 1.5

1 - Low, 2 - Medium, 3 - High, ‘-' - No Correlation

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MASTER OF COMPUTER APPLICATIONS 2024-2025
SEMESTER I
24CAP105PE2 PROFESSIONAL ELECTIVE - 1 4H-3C
Instruction Hours / Week: L:4 T:0 P:0 Marks: Internal:40 External:60 Total:100
End Semester Exam:3Hours

Karpagam Academy of Higher Education (Deemed to be University), Coimbatore – 641 021 27

MASTER OF COMPUTER APPLICATIONS 2024-2025
SEMESTER I
24CAP106 PROFESSIONAL SOFT SKILLS - I 3H-2C
Instruction Hours / Week: L:3 T:0 P:0 Marks: Internal:40 External:60 Total:100
End Semester Exam:3Hours
PREREQUISITE:
 Not Required

COURSE OBJECTIVES (CO):

 To improve clarity and conciseness in verbal and written communication.
 To enhance ability to adapt to changing circumstances and new challenges.
 To promote a respectful and supportive workplace environment.

COURSE OUTCOMES (COS):

At the end of this course, students will be able to

COs Course Outcomes Blooms Level

CO1 Understand and implement positive outlook, interpret the body Understand
language of team members and stakeholders, better interpersonal
relationships. Develop into self-motivated professionals with
confidence. Practice Responding instead of Reacting.
CO2 Create good Presentation and Present with confidence. Also, Create
recognize and manage Stress, Prioritize and Plan.
CO3 Listen to understand. To be able to ask good questions. Understand
CO4 Understand to be a good Team player, Team Dynamics and to Apply
understand the Business Ethics
CO5 Write and speak correctly, forming grammatically correct sentences. Apply

UNIT I POSITIVE ATTITUDE 7 HOURS

Attitude- Campus to Corporate attitude change, Recognizing Negative Attitude, Campus to Corporate
attitude change; Attitude at work- Impact of Negative Attitude in the Workplace, Overcoming Negative
Attitude, positive attitude, thought process, Building self-confidence and Assertiveness; Toxic positivity;
3Es, Motivation-Intrinsic and Extrinsic Motivation, Inspiration vs motivation; Emotional Intelligence-
Intro to EI, Four clusters. Transactional Analysis (TA), SWOT analysis - Professional analysis.
UNIT II: BODY LANGUAGE 7 HOURS
Importance of Body Language, Five Cs of Body Language, Body language in different cultures, Positive
Body Language; Voice Control- Pace. Pause and Pitch; Culture-Inclusivity and Proxemics across Global
Cultures, Understanding POSH; Stress Management-What is Stress, Eustress, Reasons of stress (work/
personal); Stress Management Techniques
UNIT III PRESENTATION SKILLS 7 HOURS
Self-introduction – Exercises, Why Give Presentations; Craft your message-Plan the visuals, Manage the
Response; How to create an effective presentation - Virtual & Physical, Do’s & Don'ts of Presentation
Skills, Objection handling, Stage Fear – Causes and Cure, Practice the Delivery; Time Management-
Common Time & Energy Wasters, Planning & Prioritizing Time Matrix & Analysis

UNIT IV LISTENING & QUESTIONING SKILLS 7 HOURS

Karpagam Academy of Higher Education (Deemed to be University), Coimbatore – 641 021 28
 Barriers to effective listening - how to overcome them; Exercises - Customer Call Flow – Role-play, Cust
calls amongst the team; How to frame Questions, Different kinds of questions, asking appropriate
questions; Spoken English-Introduction to Parts of Speech and its usage; Subject - Verb Agreement; Basic
conversation skills-sentence construction -SVO
UNIT V TEAMWORK 8 HOURS
Teamwork and Ethics - Definition of TEAM - Team vs Groups. Difference b/w Healthy competition and
cut throat competition, Importance of working in teams, Evolution of a TEAM, Benefits of team work;
Virtual teams- Challenges and ways to overcome it, Diversity and Inclusion in a team; Development of
Teams Stages of team development; Team dynamics-its importance & Interpersonal Skills Development
Ethics- to enable students to identify and deal with ethical problems, develop their moral intuitions, which
are implicit in everyday choices and actions; Conflict Management: Team building Activities-
Predetermined/ Predesigned Indoor/ Outdoor activities to build a team, enhance language and inter
personal skills
TOTAL: 36 HOURS
TEXT BOOKS:
1 Kumar, S., & PushpLata. (2015). Communication Skills (2nd ed.). New Delhi: Oxford University
Press.
2 Murphy, R. (2012). Essential English Grammar: Reference and Practice for South Asian Students
(2nd ed.). Cambridge: Cambridge University Press.
REFERENCE BOOKS:
1 Pye, G. (2011). Vocabulary in Practice, Parts 1 and 2 (1st ed.). Cambridge: Cambridge University
Press.
WEBSITES:
1 https://www.forbes.com/advisor/in/business/soft-skills-examples/
2 https://www.thebalancemoney.com/list-of-soft-skills-2063770
CO, PO, PSO Mapping

PO PO PO PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO
CO
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2
CO1 3 - 1 - 1 1 - - - 1 2 - - - - 1 -
CO2 3 - 1 - - - - - - - - - 1 - - - 2
CO3 3 - 2 - - - - 2 2 - - - 1 - - - -
CO4 3 - 1 - - 1 - 2 2 - - - - - - - -
CO5 3 - 1 1 1 - - 1 2 - - - 1 - - - -
Average 3 - 1.2 1 1 1 - 1.7 2 1 2 - 1 - - 1 2

1 - Low, 2 - Medium, 3 - High, ‘-' - No Correlation

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MASTER OF COMPUTER APPLICATIONS 2024-2025
SEMESTER I
24CAP111B DATA SCIENCE - PRACTICAL 5H-3C
Instruction Hours / Week: L:0 T:0 P:5 Marks: Internal:40 External:60 Total:100
End Semester Exam:3Hours
PREREQUISITE:
 Probability and Statistics, Programming Skills, Python Libraries, Learning Concepts
COURSE OBJECTIVES (CO):
 To learn techniques for handling missing data, outliers, and data imputation.
 To build a portfolio of projects demonstrating your proficiency and innovation in data science.
 To evaluate model performance using appropriate metrics (e.g., accuracy, precision, recall).
COURSE OUTCOMES (COS):
At the end of this course, students will be able to

COs Course Outcomes Blooms Level

CO1 Achieve proficiency in cleaning and preprocessing diverse datasets,
Apply
ensuring data integrity and quality.
CO2 Apply feature engineering techniques to extract relevant features and
Apply
improve model performance.
CO3 Generate visualizations and summary statistics that provide
Understand
meaningful insights into data characteristics.
CO4 Develop and implement machine learning models for predictive tasks
Apply
(e.g., regression, classification).
CO5 Implement advanced machine learning techniques such as ensemble
methods (e.g., random forests, gradient boosting) and deep learning Apply
for complex data problems.

LIST OF PROGRAMS (CASE STUDIES) 60 HOURS

1 Present your view on the different techniques you have employed to do outlier analysis, handling
missing data, feature engineering, feature importance and improving the accuracy of the model
both from a classifier as well as a regressor. Use any sample data and present your POV in a well-
structured presentation.
2 Present your findings on different activation functions you have used and methods to improve the
accuracy of the model using neural networks. You should be able to clearly articulate the
advantage and disadvantage of each activation function. Use any sample data and present your
POV in a well-structured presentation.
3 Present your findings on different techniques of anomaly detection and k means clustering. Use
any sample data and present your POV in a well-structured presentation
4 Present your POV on how to generate synthetic data using GANs. You can assume a sample

Karpagam Academy of Higher Education (Deemed to be University), Coimbatore – 641 021 30

 dataset from an IOT enabled machine where the failure rates are minimal.
5 Present your POV on Style related GANS. Explore the earliest models to the current models.
Articulate the successive improvements in the models. Also articulate the future of GANs in
generating realistic images.
6 Present your POV on GANs used for Deep Fakes. Articulate how we can identify the Deep Fake
from the original.
TOTAL: 60 HOURS

TEXT BOOKS:
1 Fuentes, A. (2018). Become a Python Data Analyst. Packt Publishing.
2 Motwani, B. (2020). Data Analytics using Python. Wiley.
3 Damji, J. S. (2020). Learning Spark: Lightning-Fast Data Analytics (2nd ed.). Shroff/O'Reilly.

REFERENCE BOOKS:
1 Barry, P. (2016). Head First Python (2nd ed.). O’Reilly Media.
2 McKinney, W. (2022). Python for Data Analysis: Data Wrangling with pandas, NumPy, and
Jupyter (3rd ed.). O’Reilly Media.
3 Lambert, K. A. (2019). Fundamentals of Python – First Programs (2nd ed.). Cengage
Publications.
WEBSITES:
1 http://docs.python.org/3/tutorial/index.html
2 http://interactivepython.org/courselib/static/ pythons
3 http://www.ibiblio.org/g2swap/byteofpython/read/

CO, PO, PSO Mapping

PO PO PO PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO
CO
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2
CO1 3 - 1 - - 1 - - - 1 - - - - - 3 -
CO2 3 - - - 1 - - - 1 - - - - - - - -
CO3 3 - 2 3 3 2 - 1 3 - - - - - - - 2
CO4 3 - - - 1 - - - 1 - - - 2 2 - - -
CO5 3 - 1 2 - 1 - 1 - - 2 - - 3 - - -
Average 3 - 1.3 2.5 1.7 1.3 - 1 1.7 1 2 - 2 2.5 - 3 2

1 - Low, 2 - Medium, 3 - High, ‘-' - No Correlation

Karpagam Academy of Higher Education (Deemed to be University), Coimbatore – 641 021 31

MASTER OF COMPUTER APPLICATIONS 2024-2025
SEMESTER I
24CAP112B APPLIED MACHINE LEARNING - PRACTICAL 4H-3C
Instruction Hours / Week: L:0 T:0 P:4 Marks: Internal:40 External:60 Total:100
End Semester Exam:3Hours
PREREQUISITE:
 Probability and Statistics, Programming Skills, Python Libraries, Learning Concepts
COURSE OBJECTIVES (CO):
 To deploy a machine learning model into a production environment, ensuring it meets
performance and scalability requirements.
 To implement automated decision-making processes based on machine learning predictions,
reducing reliance on manual interventions.
 To achieve higher accuracy and reliability in predictions compared to baseline or existing
methods, validated through rigorous testing and evaluation.
COURSE OUTCOMES (COS):
At the end of this course, students will be able to

COs Course Outcomes Blooms Level

CO1 Practice translating business requirements into well-defined machine
Apply
learning tasks (e.g., classification, regression, clustering).
CO2 Handle missing data, outliers, and data normalization effectively to
Analyze
improve model performance.
CO3 Implement feature engineering techniques to create informative
Understand
features from raw data.
CO4 Evaluate and compare different machine learning algorithms suitable
Evaluate
for the problem at hand.
CO5 Engage in continuous learning through projects, online courses, and
Evaluate
participation in machine learning communities.

LIST OF PROGRAMS 48 HOURS

1 Understanding "Mobile Price" dataset by doing feature analysis. Data is available at:
https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data
2 Execute data preprocessing step on the above dataset: perform outlier and missing data analysis
towards building a refined dataset
3 Build machine learning model/s to predict the actual price of the new mobile based on other given
features like RAM, Internal Memory etc
4 Calculate the prediction accuracy of the models used in Experiment 3 and do comparative analysis
among them to identify the best technique.
5 Understanding "Second Hand Car Prediction Price" dataset by doing feature analysis. Data is
available at: https://www.kaggle.com/datasets/sujithmandala/second-hand-car-price-prediction

Karpagam Academy of Higher Education (Deemed to be University), Coimbatore – 641 021

32
6 Perform data preprocessing step on the above dataset: perform outlier and missing data analysis
towards building a refined dataset.
7 Perform Feature Engineering towards building new feature which is more impactful.
Build machine learning model/s to predict the price of the car based on other given features like
Brand, Model, Year, Fuel Type etc
8 Calculate the prediction accuracy of the models used in Experiment 7 and do comparative analysis
among them to identify the best technique.
9 Plot the features (actual price and predicted price) in scatter plot to understand the variation.
10 Understanding "Marketing Campaign Positive Response Prediction" dataset by analysing all the
features. Data is available at: https://www.kaggle.com/datasets/sujithmandala/marketing-
campaign-positive-response-prediction
11 Perform exploratory data analysis on the above dataset: perform outlier and missing data analysis
towards building a refined dataset. Show the outliers in box plot or through some statistical
technique. Find the numerical and categorial features.
12 Perform Feature Engineering towards building new feature which is more impactful than the
existing ones. Build the correlation matrix and show visually the relationship among various
features.
13 Build machine learning model/s to predict the result of marketing campaign based on other given
features like customer details, gender, annual income etc
14 Calculate the prediction accuracy of the models used in Experiment 13 and do comparative
analysis among them to identify the best technique.
15 Please check whether you find imbalanced classes, overfitting, and data bias in the above two
datasets. Please apply some technique to overcome it.
TOTAL: 48 HOURS
TEXT BOOKS:
1 Li, H. (2023). Machine Learning Methods. Springer Nature Singapore.
2 Rao, R. N. (2022). Machine Learning in Data Science Using Python. Dreamtech Press.
REFERENCE BOOKS:
1 Alpaydin, E. (2014). Introduction to Machine Learning (3rd ed., Adaptive Computation and
Machine Learning Series). MIT Press.
2 Aggarwal, C. C. (2018). Neural Networks and Deep Learning (1st Kindle ed.).
WEBSITES:
1 https://ai.google/education/
2 https://machinelearningmastery.com/

CO, PO, PSO Mapping

Karpagam Academy of Higher Education (Deemed to be University), Coimbatore – 641 021

33
 PO PO PO PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO
CO
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2
CO1 3 - - - - - - - 2 2 2 - - - - 3 -
CO2 3 - 3 3 3 2 - 1 3 - - - - - - - 2
CO3 3 - 3 3 3 2 - 1 3 - - - - 2 - - -
CO4 3 - 3 3 3 2 - 1 3 - - - 2 - - - 1
CO5 3 - 2 2 2 1 1 1 2 - - - - - - - -
Average 3 - 2.8 2.8 2.8 1.8 1 1 2.6 2 2 - 2 2 - 3 1.5

1 - Low, 2 - Medium, 3 - High, ‘-' - No Correlation

Karpagam Academy of Higher Education (Deemed to be University), Coimbatore – 641 021

34
MASTER OF COMPUTER APPLICATIONS 2024-2025
SEMESTER I
JOURNAL PAPER ANALYSIS & PRESENTATION 2H-0C
Instruction Hours / Week: L:2 T:0 P:0 Marks: Internal:00 External:00 Total:000
End Semester Exam: 3 Hours

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35