IGNOU MCA Syllabus; Check semester-wise latest syllabus

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 Function

Unit 2: Finite State Machines

Unit 3: Regular Expression 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 Coloring

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 Work Place - I

Unit 1: The Process of Communication

Unit 2: Telephone Techniques

Unit 3: Job Applications and Interviews

Unit 4: Group Discussions

Unit 5: Managing Organizational Structure

Block 2: Professional Skills Needed at the Work Place - 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: Cyber security 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

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

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: Implementations 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 about 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 formalism

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 Map Reduce

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