IGNOU MCA Syllabus (Available); Download Semester-wise Latest Syllabus PDF

Design and Analysis of Algorithms

Theory

4

Discrete Mathematics

Theory

4

Software Engineering

Theory

4

Professional Skills and Ethics

Theory

2

Security and Cyber Laws

Theory

2

DAA and Web Design Lab

Practical

2

Software Engineering Lab

Practical

2

Data Communication and Computer Networks

Theory

4

Object Oriented Analysis and Design

Theory

4

Web Technologies

Theory

4

Data Warehousing and Data Mining

Theory

4

OOAD and Web Technologies Lab

Practical

2

Computer Networks and Data Mining Lab

Practical

2

Artificial Intelligence and Machine Learning

Theory

4

Accountancy and Financial Management

Theory

4

Data Science and Big Data

Theory

4

Cloud Computing and IoT

Theory

4

AI and Machine Learning Lab

Practical

2

Cloud and Data Science Lab

Practical

2

Digital Image Processing and Computer Vision

Theory

4

Mobile Computing

Theory

4

Project

Project

12

Design and Analysis of Algorithms

Block- 1 Introduction to Algorithms

• Unit 1: Basics of an Algorithm and Its Properties
• Unit 2: Some pre-requisites and Asymptotic Bounds
• Unit 3: Analysis of Simple Algorithm
• Unit 4: Solving Recurrences

Block- 2 Design Techniques-I

• Unit 1: Greedy Technique
• Unit 2: Divide & Conquer Technique
• Unit 3: Graph Algorithm –I

Block- 3 Design Techniques – II

• Unit 1: Graph Algorithms- II
• Unit 2: Dynamic Programming Technique
• Unit 3: String Matching Techniques

Block- 4: NP-Completeness and

Approximation Algorithm

• Unit-1: NP-Completeness
• Unit 2: NP-Completeness and NP-Hard Problems
• Unit 3: Handling Intractability

Discrete Mathematics

Block-1 Elementary Logic & Proofs

• Unit 1: Prepositional Calculus
• Unit 2: Methods of Proof
• Unit 3: Boolean Algebra and Circuits

Block- 2 Sets and Languages

• Unit 1: Sets, Relations and Functions

• Unit 2: Finite State Machines

• Unit 3: Regular Expressions and Languages

Block 3: Counting Principles

• Unit 1: Combinatorics
• Unit 2: Advanced Counting Principles
• Unit 3: Recurrence Relations
• Unit 4: Partitions and Distributions

Block-4 Graph Theory

• Unit 1: Basic Properties of Graphs
• Unit 2: Connectedness
• Unit 3: Eulerian and Hamiltonian Graphs
• Unit 4: Graph Colouring

Software Engineering

Block 1: Overview of Software

Engineering

• Unit 1: Software Engineering and its models
• Unit 2: Principles of Software Requirements Analysis
• Unit 3: Software Design
• Unit 4: Software Quality and Security

Block 2: Software Project Management

• Unit 5: Software Project Planning
• Unit 6: Risk Management and Project Scheduling
• Unit 7: Software Testing
• Unit 8: Software change management

Block 3: Web, Mobile and CASE tools

• Unit 9: Web Software Engineering
• Unit 10: Mobile Software Engineering
• Unit 11: CASE tools
• Unit 12: Advanced Software Engineering

Professional Skills and Ethics

Block 1: Professional Skills Needed at the Workplace - I

• Unit 1: The Process of Communication
• Unit 2: Telephone Techniques
• Unit 3: Job Applications and Interviews
• Unit 4: Group Discussions
• Unit 5: Managing Organisational Structure

Block 2: Professional Skills Needed at the Workplace - II

• Unit 6: Meetings
• Unit 7: Presentation Skills –I
• Unit 8: Presentation Skills –II
• Unit 9: Developing Interpersonal Skills for a Successful Life at the Workplace
• Unit 10: Work Ethics and Social Media Etiquette
• Unit 11:Copyright and Plagiarism

Security and Cyber Laws

Block 1: Cyber Security Issues

• Unit 1: Cybersecurity issues and challenges (will be adapted from MIR-11 Unit 7, PGCCL)
• Unit 2: Cryptography Mechanisms (will be adapted from MIR-11 Unit 8, PGCCL)
• Unit 3: Data Security and Management (will be adapted from MIR-14 Unit 5, PGCCL)

Block 2: Cyber Laws

• Unit 1: Regulation of Cyberspace: An Overview (will be adapted from MIR-11 Unit 9, PGCCL)
• Unit 2: Cyber Crimes
• Unit 3: IPR Issues in CyberSpace

DAA and Web Design Lab

The main objective of this laboratory course is to provide hands-on exercises to the learners based on the DAA and Web Design Course

• There will be 20 practical sessions (3 hours each), of which 10 sessions will be on DAA and 10 sessions will be on Web Designing.
• The practice problems for all 20 sessions will be listed session-wise in the lab manual.

Software Engineering Lab

The main objective of this laboratory course is to provide hands-on exercises to the learners based on the Software Engineering Course.

• There will be 20 practical sessions (3 hours each)
• The practice problems for all 20 sessions will be listed session-wise in the lab manual.

Data Communication and Computer Networks

Block- 1: Introduction to Data

• Unit 1: Introduction to the Internet
• Unit 2: Data Transmission Basics & Transmission Media
• Unit 3: Data Encoding & Multiplexing

Block- 2: Media Access Control and Data Link Layer

• Unit 1: Data Link Layer Fundamentals
• Unit 2: Retransmission Strategies
• Unit 3: Contention-based Media Access Protocols
• Unit 4: Polling-based Media Access Control Protocols

Block- 3: Network Layer

• Unit 1: Introduction to Layer
• Unit 2: Routing Algorithms
• Unit 3: Congestion Control Algorithms
• Unit 4: Emerging Networking Technology

Block- 4: Transport Layer and Application Layer Services

• Unit 1: Transport Services and Mechanism
• Unit 2: TCP/UDP
• Unit 3: Network Security I
• Unit 4: Network Security-II

Object Oriented Analysis and Design

Block 1: Object-Oriented Analysis and UML

• Unit 1: Introduction to Object-Oriented Modeling
• Unit 2: Structural Modeling using UML
• Unit 3: Behavioral Modeling using UML
• Unit 4: Advanced Behavioral Modeling using UML
• Unit 5: Architectural Modeling

Block 2: Modeling

• Unit 1: Object Modeling
• Unit 2: Dynamic Modeling
• Unit 3: Functional Modeling

Block 3: Object Oriented Design

• Unit 1: Basics of System Design
• Unit 2: Object Design
• Unit 3: Advanced Object Design

Block 4: Implementation

• Unit 1: Implementation Strategies -1
• Unit 2: Implementation Strategies -2
• Unit 3: Objects Mapping With Databases

Web Technologies

Block 1: Web Application Development using J2EE

• Unit 1: Introduction to J2EE, Architecture and Design pattern
• Unit 2: Basics of Servlet
• Unit 3: Session Management and Database Connectivity in Servlet
• Unit 4: JSP

Block 2: Frameworks for J2EE

• Unit 5: Introduction to J2EE Frameworks
• Unit 6: Discuss various Frameworks available for J2EE Development (Struts, Hibernate, Spring)
• Unit 7: Spring MVC
• Unit 8: Spring MVC with Bootstrap CSS

Block 3: Spring Boot and Hibernate (ORM)

• Unit 9: Introduction to Spring Boot
• Unit 10: Configuration of Hibernate (ORM)
• Unit 11: CRUD Application using Spring boot and Hibernate

Block 4: Web Security

• Unit 12: Spring Security configuration
• Unit 13: Custom login using Security
• Unit 14: Role-based login

Data Warehousing and Data Mining

BLOCK 1: DATA WAREHOUSE

FUNDAMENTALS AND

ARCHITECTURE

• Unit 1: Fundamentals of Data Warehouse
• Unit 2: Data Warehouse Architecture
• Unit 3: Dimensional Modeling

BLOCK 2: ETL, OLAP and TRENDS

• Unit 4: Extract, Transform and Loading
• Unit 5: Introduction to Online Analytical Processing
• Unit 6: Trends in Data Warehouse

BLOCK 3: DATA MINING FUNDAMENTALS AND FREQUENT PATTERN MINING

• Unit 7: Data Mining – An Introduction
• Unit 8: Data Preprocessing
• Unit 9: Mining Frequent Patterns and Associations

BLOCK 4: CLASSIFICATION, CLUSTERING AND WEB MINING

• Unit 10: Classification
• Unit 11: Clustering
• Unit 12: Text and Web Mining

OOAD and Web Technologies Lab

Main objective of this laboratory course is to provide hands on exercises to the learners based on Object Oriented Analysis and Design & Web Technologies Courses.

• There will be 20 practical sessions (3 hours each) of which 10 sessions will be on OOAD and 10 sessions will be on Web Technologies.
• The practice problems for all 20 sessions will be listed session-wise in the lab manual.

Computer Networks and Data Mining Lab

Main objective of this laboratory course is to provide hands on exercises to the learners based on Computer Networks and Data Mining Courses.

• There will be 20 practical sessions (3 hours each) of which 10 sessions will be on Computer Networks and 10 sessions will be on Data Mining.
• The practice problems for all 20 sessions will be listed session-wise in the lab manual.

Artificial Intelligence and Machine Learning

Block 1: Artificial Intelligence - Introduction

• Unit 1: Introduction to Artificial Intelligence
• Unit 2: Problem-Solving Using Search
• Unit 3: Uninformed and Informed Search
• Unit 4: Predicate and Propositional Logic

Block 2: Artificial Intelligence - Knowledge Representation

• Unit 5: First Order Logic
• Unit 6: Rule-based Systems and other formalisms
• Unit 7: Probabilistic Reasoning
• Unit 8: Fuzzy and Rough Set

Block 3: Machine Learning - I

• Unit 9: Introduction to Machine Learning Methods
• Unit 10: Classification
• Unit 11: Regression
• Unit 12: Neural Networks and Deep Learning

Block 4: Machine Learning - II

• Unit 13: Feature Selection and Extraction
• Unit 14: Association Rules
• Unit 15: Clustering
• Unit 16: Machine Learning Programming using Python

Accountancy and Financial Management

Block 1: Accounting System

• Unit 1: Accounting and Its Functions
• Unit 2: Accounting Concepts and Standards
• Unit 3: Basic Accounting Process: Preparation of Journal, Ledger, Trial Balance and Bank Reconciliation Statement

Block 2: Understanding and Analysis of Financial Statements

• Unit 1: Preparation and Analysis of Final Accounts
• Unit 2: Cash Flow Statement
• Unit 3: Ratio Analysis
• Unit 4: Reading and Interpretation of Financial Statements

Block 3: Financial Management and Decisions

• Unit 1: Introduction to Financial Management
• Unit 2: Time Value of Money
• Unit 3: Cost of Capital
• Unit 4: Investment Decision Methods
• Unit 5: Working Capital Decisions

Block 4: Working Capital Management

• Unit 1: Cash and Treasury Management
• Unit 2: Receivables Management
• Unit 3: Inventory Management

Data Science and Big Data

Block 1: Basics of Data Science

• Unit 1: Introduction to Data Science
• Unit 2: Portability and Statistics for Data Science
• Unit 3: Data Preparation for Analysis
• Unit 4: Data Visualization and Interpretation

Block 2: Big Data and its Management

• Unit 5: Big Architecture
• Unit 6: Programming using MapReduce
• Unit 7: Other Big Data Architecture and Tools
• Unit 8: No SQL Database

Block 3: Big Data Analysis

• Unit 9: Mining Big Data
• Unit 10: Mining Data Streams
• Unit 11: Link Analysis
• Unit 12: Web and Social Network Analysis

Block 4: Programming for Data

Analysis

• Unit 13: Basic of R Programming
• Unit 14: Data Interfacing and Visualization in R
• Unit 15: Data Analysis and R
• Unit 16: Advance Analysis using R

Cloud Computing and IoT

BLOCK 1: CLOUD COMPUTING FUNDAMENTALS AND VIRTUALIZATION

• Unit 1: Cloud Computing: An Introduction
• Unit 2: Cloud Deployment Models, Service Models and Cloud Architecture
• Unit 3: Resource Virtualization

BLOCK 2: RESOURCE PROVISIONING, LOAD BALANCING AND SECURITY

• Unit 4: Resource Pooling, Sharing and Provisioning
• Unit 5: Scaling
• Unit 6: Load Balancing
• Unit 7: Security Issues in Cloud Computing

BLOCK 3: IoT FUNDAMENTALS

AND CONNECTIVITY

TECHNOLOGIES

• Unit 8: Internet of Things: An
• Introduction
• Unit 9: IoT Networking and Connectivity Technologies

BLOCK 4: Application Development, Fog Computing and Case Studies

• Unit 10: IoT Application Development
• Unit 11: Fog Computing and Edge Computing
• Unit 12: IoT Case Studies

AI and Machine Learning Lab

Main objective of this laboratory course is to provide hands on exercises to the learners based on Artificial Intelligence and Machine Learning Course.

• There will be 20 practical sessions (3 hours each) of which 10 sessions will be on AI and 10 sessions will be on machine learning.
• The practice problems for all 20 sessions will be listed session-wise in the lab manual.

Cloud and Data Science Lab

Main objective of this laboratory course is to provide hands on exercises to the learners based on Cloud Computing and Data Science Courses.

• There will be 20 practical sessions (3 hours each) of which 10 sessions will be on cloud computing and 10 sessions will be on Data Science.
• The practice problems for all 20 sessions will be listed session-wise in the lab manual.

Digital Image Processing and Computer Vision

Block-1: Digital Images Processing -I

• Unit-1: Introduction to digital image
• Unit-2: Image Transformation
• Unit-3: Image enhancement in the spatial domain
• Unit-4: Image Filtering Operations in the Spatial Domain

Block-2: Digital Images Processing –II

• Unit-5: Transformation Techniques
• Unit-6: Image enhancement and Filtering
• Unit-7: Color image processing

Block-3: Computer Vision-I

• Unit-9: Introduction to computer vision, camera models, and Transformations.
• Unit-10: Single Camera
• Unit-11: Multiple Cameras

Block-4: Computer Vision-II

• Unit-12: Object detection
• Unit-13: Object Recognition using Supervised Learning Approaches
• Unit-14: Object Classification using Unsupervised Learning Approaches

Mobile Computing

Block-1: Introduction to Mobile Computing

• Unit-1: Introduction to Mobile Communications
• Unit-2: Introduction to Mobile Computing Architecture
• Unit-3: Mobile Client Devices and Pervasive Computing
• Unit-4: GSM and GPRS

Block-2: Mobile IP and Issues in

Mobile Computing

• Unit-5: 4G and 5G Networks
• Unit-6: Mobile IP Network Layer
• Unit-7: Mobile Transport Layer
• Unit-8: Database Management Issues in Mobile Computing

Block 3: Introduction to various

Network Technologies

• Unit-9: Mobile Adhoc Network
• Unit-10: WLAN and PAN protocols
• Unit-11: Virtual and Cloud Networks
• Unit-12: Mobility, Portability, Replication and Clustering

Block-4: Introduction to Mobile

Software Environments

• Unit-13: Smart Client and Enterprise Server-based Architecture
• Unit-14: Mobile Internet Applications
• Unit-15: Mobile Application Languages
• Unit-16: Mobile Operating Systems and Development Environments

Criteria

Details

Educational Qualification - Category I

Must have passed BCA, Bachelor’s in Computer Science Engineering, or an equivalent degree.

Educational Qualification - Category II

Candidates with B.Sc., B.Com., or B.A. are also eligible ONLY if they studied Mathematics at:

• Class 12 level, or
• Graduation level (along with required Bridge Courses).

Mandatory Subject – Mathematics

Compulsory Mathematics either at 10+2 or Graduation level.

Note: Candidates without Mathematics at either level are not eligible for the programme.

Bridge Course Requirement

Candidates under Category II, who did not study Computer Applications/IT in their degree must complete the following Bridge Courses and pay an additional amount of Rs. 2000.  

• MCS-201: Programming in C and Python
• MCS-208: Data Structures and Algorithms