Algorithms are the central part of computing and Design and Analysis of algorithms course is the core of the study of Computer Science discipline. The revised course on design and analysis of algorithm introduces many new topics: Deterministic and Stochastic Algorithms , how to solve recurrence relation problems through Substitution method, Recurrence tree and Master methods, An overview of local and global optima ,Fractional Knapsack problem ,Huffman Codes ,a task scheduling algorithm , Topological Sort ,Strongly Connected Components , Maximum Bipartite Matching Problem, Binomial coefficient computation , Floyd Warshall algorithm , String Matching Techniques :The naïve String Matching Algorithm, The Rabin Karp Algorithm, Knuth –Morris Pratt Algorithm, Handling Intractability: Approximation algorithms for Vertex Cover problem and Minimizing makespan as parallel machines (Graham’s algorithm) , Parameterized algorithm for Vertex Cover problem and Metaheuristic Algorithms

Discrete mathematics deal with discrete objects (that is not continuous) like a set of PhD students in computer science departments. Foundation of Commuter Science is built upon discrete mathematics. It includes Prepositional calculus, sets, relations, function, graphs, Boolean algebra and advanced counting principles. Knowledge of discrete structures helps in analyzing algorithms, and understanding different areas of computer science courses. The revised course on discrete mathematics course includes Finite State Machines: Introduction to Finite Automata, Computability and Complexity, Moore and Mealy State Machines and Deterministic Finite Automata, Regular Expression and Languages: Formal Definition of Regular expression and language, Building Regular Expressions, Finite Automata and Regular Expressions, Kleene Closure Definition and Algebra of regular Expressions.

The objective of the Course is to make the learner efficiently work as software engineer. S/he should be well acquainted with all the phases of Software Development Life Cycle as well as latest topics in Software Engineering.

This course is aimed to develop the communicational skills, professional skills and ethics at the work place. In this course, we concentrate on English at the workplace. You are probably wondering whether business English (as it is also called) is a separate language to general English. Certainly not, business English is not a separate language. It is English used at the workplace using specific vocabulary, and in certain situations 28 having a different discourse. Every profession uses a certain ‘jargon’ and the business context in no different. While business English is firmly rooted in general English, nevertheless there are certain distinguishing features which are evident. In this Course, you will learn some theoretical inputs into the process of communication, its different types, the difference between written and oral communication. We then concentrate on the structure of conversation – its characteristics and conventions, effectively speaking over the telephone, preparing Curriculum Vitae for jobs and interviews, preparing and participating in the Group Discussions, Presentation Skills, Copyright and Plagiarism issues and many more.

This course introduces the students to some of the latest topics in the context of computer security and cyber laws. These topics are very relevant in the present time.

Main objective of this laboratory course is to provide hands on exercises to the learners based on DAA and Web Design Course.

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

The course introduces the fundamental concepts of data communication and Computer Networks. In the networking field, significant changes have taken place:

1. evolution of the Internet and wireless networks
2. growth of networking services and applications.

The objective of the revised courses is to reflect these changes besides explaining the basic principles of computer networking. A several new topics have been introduced in the revised course: Personnel Area network: Bluetooth and Zigbee, Cellular Networks : Architecture, Handoff, 3G,4G and 5G networks, Mobile IP, IPV6, Mobile Adhoc Networks, Wireless Sensor Networks, Internet of Things (IOT), Network Layer Security: IPSec, VPN, Securing TCP Connections: SSL, WLAN Security, Cyber Threats and Attacks and Counter Measures, Taxonomy of various Cyber Attacks, Virus, Worm and Trojan , DoS attack, DDOS attack, Phishing attacks, Malware, Ransom, vulnerabilities, Buffer Overflow, SQL Injection, Browser Vulnerabilities, OS vulnerabilities, Basics Computer Forensics, Recent Cyber Attacks and Firewalls and Intrusion Detection Systems.

Object oriented analysis and design is a popular paradigm of analysis and design of the systems. This Course is designed to help in learning object oriented analysis and design concepts. This Course is having coverage of UML diagrams and will help in developing understanding in the area of system analysis and design concepts using object-oriented approach. This Course will cover different aspects of OOAD with explaining object modeling dynamic modeling and functional modeling. The topics covered in the course include: Object Oriented Modeling and UML Introduction to Object Oriented Modelling: OOT Object Oriented Modeling, Characteristics Object Oriented Modeling (Class and Objects, Links and Association, Generalization and Inheritance), An Object Model, Benefits of OO Modeling, Introduction to OOAD tools 41 Object Oriented Analysis: Object Oriented Analysis, Problem Statement: an Example, Differences between Structured Analysis and Object Oriented Analysis, Analysis Techniques (Object Modeling, Dynamic Modeling, Functional Modeling), Adding Operations, Analysis Iteration Using UML: UML: Introduction, Object Model Notations: Basic Concepts, Structural Diagrams (Class, Object, Composite, Package, Component, Deployment), Behavioural Diagrams (Use Case, Communication, Sequence, Interaction Overview, Activity, State), Modelling with Objects Object Oriented Design System Design: System Design: An Object-Oriented Approach, Breaking into Subsystems, Concurrency Identification, Management of data store, Controlling events between Objects, Handling Boundary Conditions Object Design: Object Design for Processing, Object Design Steps, designing a Solution, Choosing Algorithms, Choosing Data Structures, Defining Classes and delegation of Responsibilities to Methods Advance Object Design: Control and its Implementation (Control as a State within Program, Control as State Machine Engine, Control as Concurrent Task), Inheritance Adjustment, Association: Design, Object Representation, Design Optimization, Design Documentation Modeling Object Modeling: Advance Modeling Concepts (Aggregation, Abstract Class), Multiple Inheritance, Generalization as an Extension, Generalization as a Restriction, Metadata, Constraints, An Object Model Dynamic Modeling: Events, State and State Diagram, Elements of State Diagrams, Examples of State Diagrams, Advance Concepts in Dynamic Modeling, Concurrency, A Dynamic model Functional Modeling: Functional Models, Data Flow Diagrams, Features of a DFD, Design flaws in DFD, A Functional model, Relationship between Object, Dynamic, and Functional Models Implementation Implementation Strategies: Implementation (Using Programming Languages, Using Database System), Unidirectional Implementation, Bi-directional Implementation, implementing associations, Implementing Constraints, Implementing State charts, Persistency Object Mapping with Databases: Relational Database Schema for Object Modes, Object Classes to Database Tables, Mapping Associations to Tables, Mapping Generalizations to Tables, Interfacing to Database, Object Mapping with Databases: an Example.

Main objective of the Course is to introduce concepts, tools/technologies and programming to develop distributed secure, reliable and scalable Web Application using J2EE Technologies application. This course discusses some commonly used design patterns, servlet, JSP, Spring Boot & Hibernate (ORM), and Web Security. The topics covered in the course include: Introduction to Advance Java (J2EE) J2EE Architecture and Design patterns (MVC, Repository Design pattern, Singleton, Factory, etc.), Building java Application JAR and WAR and deployment in tomcat. Introduction to Servlets, Http Protocol & Http Methods, Web Server & Web Container, Servlet Architecture, Servlet Life Cycle, Steps to create a Servlet, Servlet Communication (Servlet- Browser, Web-component, ) , Session Management, Database Connectivity in Servlet, Java Server Pages(JSP) Overview, JSP Life Cycle, JSP API, Components of JSP(Directives, Scripting, Action), JSP Implicit Objects, JSP Standard Tag Library (JSTL), Exception handling using JSP, Database Connectivity in JSP. Introduction to J2EE Frameworks, discuss about various Frameworks available for J2ee Development (Struts, Hibernate, Spring)- Maven and Introduction of Annotation. Spring MVC- Configuration, Create, Read, Update, and Delete (CRUD) Application. Spring MVC with Bootstrap CSS- Configuration of Bootstrap in Application and Apply custom CSS in pages. Spring Boot & Hibernate (ORM) - Introduction to Spring boot, Configuration of Hibernate (ORM) CRUD Application using spring boot and Hibernate. Web Security- Spring Security configuration, Custom login using Security, Role based login.

Introduction to Data Warehousing, Evolution of Data Warehousing, Features of Data Warehousing, Benefits of Data Warehousing, Data Granularity, Metadata, Data Warehousing Architecture, Data Warehouse and Data Marts, Building Data Marts, Issues in building data marts, Data Warehouse Schema, Dimensional Modeling, The Star Schema, The Snowflake Schema, Aggregate Tables, Fact Constellation Schema, Dimensional Modeling, Extraction, Transformation and Loading (ETL) process, OLAP and Data Warehousing, OLTP and Data Warehousing, Trends in Data Warehousing 47 Introduction to Data Mining Systems, How Data Mining Works, Classification of Data Mining Systems Issues, Applications of Data Mining, Data Mining Tools, Issues in Data Mining, Data Preprocessing – Cleaning, Integration, Reduction, Transformation and Discretization, Data similarity and dissimilarity measures, Mining Frequent Patterns, Associations, Classification using Frequent Patterns, Decision Tree Induction , Bayesian Classification, Rule Based Classification, Classification by Back Propagation, Support Vector Machines, K- nearest Neighbor classification, Clustering, Major Clustering Methods, Partitioning Methods, Hierarchical Methods, Density Based Methods, Grid Based Methods, Hierarchical Clustering, Outlier Detection, Text and Web Mining.

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.

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

The course relates to the conceptual understanding of the Artificial Intelligence and Machine Learning. Generally, Artificial Intelligence (AI) is considered as the discipline, to deal with the solution of the hard and insolvable problems using reasonable amount of time, by exploiting the knowledge of the problem domain. In view of the significance of knowledge in AI, in this course, a number of knowledge representation formalisms are introduced. The formalisms discussed include Propositional Logic, First Order Predicate Logic, Rule-based systems, Semantic Networks and Frames. Further, the course introduces the various concepts of Machine learning, Viz. Supervised learning, Unsupervised Learning and their respective application areas. Currently these two fields i.e. Artificial Intelligence and Machine Learning are in high demand, the course will help the learners to build the understanding of these fields.

Financial Management and Accountancy course is aimed at making students aware of the basic accounting procedures and financial management processes. The central purpose of accounting is to make possible the periodic matching of costs (efforts) and revenues (accomplishments). The course describes four major topics: Accounting System, Understanding and Analysis of Financial Statements, Financial Management and Decisions and Working Capital Management. Accounting System deals with the framework of accounting. The focus is on scope and function of accounting in modern business. Understanding and Analysis of Financial Statements deals with preparation of final accounting statements, which includes preparation and analysis of Profit and Loss A/c. In this topic we also discuss ratio analysis. Ratio analysis is one of the most widely used analytic tool for financial analysis. Financial Management and Decisions deals with various aspects of financial management. Working Capital Management topic deals with the various sub components of working capital, which includes cash and treasury management. The main objective of cash management is to maintain an optimum level of cash balance.

This course introduces the students to the concepts of data science and big data, its architecture and a programming technique R that can be used to analyse big data.

ntroduction to Cloud Computing, Traditional Computing Approaches, Comparison of Cluster, grid and Cloud Computing, Evolution of Cloud Computing, Benefits and Challenges, Cloud Deployment Models (Public, Private, Community and Hybrid), Service Delivery Models (IaaS, PaaS, SaaS etc..), Cloud Architecture, Resource Virtualization, Resource Pooling, Sharing and Provisioning, Scaling in the Cloud, Load Balancing, Security Issues in Cloud Computing. IoT-An Introduction, Characteristics, IoT categories, Baseline Technologies of IoT, M2M and IoT, Multihoming, IoT Identification and Data protocols (IPv4, IPv6, MQTT, CoAP, SMPP, AMQP), Connectivity Technologies (IEEE, 802.15.4, Zigbee, 6LoWPAN, RFID, NFC, Bluetooth, Z-wave), IoT Application Development, Framework for IoT Applications, Implementation of Device Integration, Data Acquisition and Integration, Device Data Storage, Fog Computing, Edge Computing, Comparison of Cloud, Fog and Edge, IoT Case Studies (Smart Homes, Smart Grids, Smart Cities, Connected Vehicles, Industrial IoT.

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

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

The course relates to the formation of fundamental understanding of the various concepts of Digital Image processing and Computer Vision. The content coverage will help the learners to get the insight of the subject both theoretically and practically.

The following are the objectives of this course:

• Introduce Mobile Communications
• Introduce Mobile Computing Architecture
• Overview of Pervasive Computing
• Introduce GSM and GPRS
• Introduce 4G and 5G Networks Discuss Database Management Issues in Mobile Computing
• Introduce Mobile Adhoc Networks
• Introduce WLAN and PAN protocols
• Introduce Virtual and Cloud Networks
• Introduce Mobile Internet Applications
• Introduce Mobile Application Languages
• Introduce Mobile Operating Systems
• Introduce Mobile Software Development Environments

The objective of the MCA project work is to develop quality software solution by following the software engineering principles and practices. It is only possible when a learner goes about with the task independently. During the development of the project the students should involve in all the stages of the software development life cycle like requirements engineering, systems analysis, systems design, software development, testing strategies and documentation with an overall emphasis on the development of reliable software systems. The primary emphasis of the project work is to understand and gain the knowledge of the principles of software engineering practices, so as to participate and manage a large software engineering projects in future. Students are encouraged to spend efforts equavilant to 12 credits working on a project preferably in a software industry or any research organization. Topics selected should be complex and large enough to justify as a MCA project. The courses studied by the students during the MCA programme provide them the comprehensive background to work on diverse application domains. Students should strictly follow and adhere to the project guidelines. Project Guidelines will be prepared and uploaded on to the IGNOU website/printed.