MCA Syllabus 2024-2026 – Gurugram University (GU) | NEP 2020 | All 4 Semesters

Computer Fundamentals and Programming in C (CC-A01 | 241/MCA/CC101) — 4 Credits | 100 Marks (25 Internal + 50 External + 5 Viva + 20 Practical). This course introduces the fundamentals of computers, problem-solving techniques, and programming using the C language. Unit I: Computer Fundamentals — Concept of data and information, components of a computer system, input and output devices, CPU components, memory and storage devices, classification of computers, advantages and limitations of computers, and applications of computers. Social concerns of computer technology including positive and negative impacts, computer crimes, viruses, and their remedial solutions. Computer software concepts including system software and application software, overview of operating systems, programming languages (Machine Language, Assembly Language, High-Level Language, and Fourth Generation Language - 4GL), language translators, linker, and loader. Unit II: Problem Solving and C Programming Fundamentals — Problem identification and analysis, algorithms, flowcharts, pseudocode, decision tables, program coding, program testing, and execution. Introduction to C programming including keywords, variables, constants, and the structure of a C program. Unit III: Operators, Expressions and Decision Making — Arithmetic, unary, logical, bitwise, assignment, and conditional operators and expressions. Decision-making constructs using if, if-else, else-if ladder, switch statements, along with break, continue, and goto statements. Unit IV: Loops and Functions — Looping constructs using while, do-while, and for loops, including nested loops. Functions in C including user-defined functions, library functions, function prototypes, passing arguments, passing array arguments, recursion, use of library functions, macro versus functions, and pointers in C.

System Software and Operating Systems (CC-A02 | 241/MCA/CC102) — 4 Credits | 100 Marks. This course provides a comprehensive understanding of operating system concepts, process management, memory management, file systems, and disk management, along with comparative studies of modern operating systems. Unit I: Introduction, Processes and Process Scheduling — Concept of Operating Systems, generations of operating systems, types of operating systems, and OS services. Processes including definition, process relationships, process states, state transitions, Process Control Block (PCB), and context switching. Threads including definition, states, benefits, types, and multithreading. Process scheduling concepts including scheduling objectives, types of schedulers, scheduling criteria such as CPU utilization, throughput, turnaround time, waiting time, and response time. Scheduling algorithms including pre-emptive and non-pre-emptive scheduling, FCFS, SJF, SRTF, and Round Robin (RR). Unit II: Inter-process Communication and Deadlocks — Critical section, race conditions, mutual exclusion, producer-consumer problem, semaphores, event counters, monitors, and message passing. Classical IPC problems including Reader-Writer and Dining Philosopher problems. Deadlocks including definition, necessary and sufficient conditions, deadlock prevention, deadlock avoidance using Banker's Algorithm, deadlock detection, and recovery techniques. Unit III: Memory Management and Virtual Memory — Basic concepts of memory management, logical and physical address mapping, contiguous memory allocation, fixed and variable partitioning, internal and external fragmentation, and compaction. Paging concepts including page allocation, hardware support, protection and sharing mechanisms, and limitations of paging. Virtual memory concepts including locality of reference, page faults, working set, dirty page/dirty bit, demand paging, and page replacement algorithms such as Optimal, FIFO, and Least Recently Used (LRU). Unit IV: File Management, Disk Management and Case Studies — Concepts of files, file access methods, file types, file operations, directory structures, file system structure, and file allocation methods including contiguous, linked, and indexed allocation. Disk management topics including disk structure, disk scheduling algorithms (FCFS, SSTF, SCAN, and C-SCAN), disk reliability, disk formatting, boot blocks, and bad blocks. Case studies and comparative analysis of Windows, UNIX, and Linux operating systems.

Artificial Intelligence and Applications (CC-A03 | 241/MCA/CC103) — 4 Credits | 100 Marks. This course introduces the fundamental concepts of Artificial Intelligence, knowledge representation, AI programming languages, search techniques, reasoning methods, and expert systems. Unit I: Introduction to Artificial Intelligence — History and definition of Artificial Intelligence, emulation of human cognitive processes, knowledge search trade-offs, stored knowledge, semantic networks, abstract views of modelling, elementary knowledge representation, computational logic, analysis of compound statements using logical connectives, predicate logic, knowledge organization and manipulation, and knowledge acquisition techniques. Unit II: Programming and Logics in Artificial Intelligence — Introduction to LISP and other AI programming languages, LISP syntax and numerical functions, distinctions between LISP and PROLOG, input/output operations, local variables, interaction and recursion, property lists and arrays, alternative programming languages, formalized symbolic logic, properties of Well-Formed Formulas (WFFs), non-deductive inference methods, handling inconsistencies and uncertainties, Truth Maintenance Systems (TMS), default reasoning, closed-world assumption, modal logic, and temporal logic. Unit III: Search Methods and Knowledge Representation — Fuzzy logic concepts and applications, probabilistic reasoning, Bayesian probabilistic inference, Dempster-Shafer theory, possible-world representation, ad hoc reasoning methods, structured knowledge representation using graphs, frames, and related structures, object-oriented representation including object classes, messages and methods, simulation examples using OOP programs and languages, search and control strategies, search problems, uninformed (blind) search techniques, and searching AND-OR graphs. Unit IV: Knowledge Organization and Communication in Expert Systems — Matching techniques including exact, partial, and fuzzy matching, RETE matching algorithm, knowledge organization, indexing and retrieval techniques, integration of knowledge in memory organization systems, perception and communication in expert systems, overview of linguistics, basic parsing techniques, semantic analysis, knowledge representation structures, natural language generation, and natural language processing systems.

Web Designing Fundamentals (DSE-01 | 241/MCA/DS101) — 3 Credits | 75 Marks (15 Internal Theory + 35 External Theory + 5 Practical Internal + 20 Practical External) | Duration: 3 hrs. This course covers the fundamentals of the Internet and World Wide Web, web publishing, HTML-based web development, and Dynamic HTML with CSS. Unit I: Introduction to Internet and World Wide Web — Introduction to Internet and World Wide Web, evolution and history of the World Wide Web, basic features, web browsers, web servers, Hypertext Transfer Protocol (HTTP), overview of TCP/IP and its services, URLs, searching and web-casting techniques, search engines and search tools. Unit II: Web Publishing — Hosting your site, Internet Service Provider (ISP), web terminologies, phases of planning and designing a website, steps for developing a site, choosing contents, home page, domain names, Front Page views, adding pictures, links, backgrounds, relating Front Page to DHTML. Creating a website and markup languages (HTML, DHTML). Unit III: Web Development with HTML — Introduction to HTML, hypertext and HTML, HTML document features, HTML command tags, creating links, headers, text styles, text structuring, text colors and background, formatting text, and page layouts. Unit IV: Advanced HTML and DHTML — Images, ordered and unordered lists, inserting graphics, table creation and layouts, frame creation and layouts, working with forms and menus, working with radio buttons, check boxes, and text boxes. Dynamic HTML (DHTML): features of DHTML, CSSP (Cascading Style Sheet Positioning) and JSSS (JavaScript Assisted Style Sheet), layers of Netscape, the ID attributes, and DHTML events.

Blockchain Technology (VAC-1 | 241/CS/VA101) — 2 Credits | 50 Marks (15 Internal + 35 External) | Duration: 3 hrs. This Value Added Course introduces blockchain concepts, consensus models, permissioned blockchains, financial applications, and blockchain security. Unit I: Introduction to Blockchain — Overview of blockchain, need for blockchain, history of centralized services, trusted third party, distributed consensus in open environments, Distributed vs Decentralized Network, 51% attack theory, public blockchains, private blockchains, blockchain architecture and working, mining, limitations of blockchain, and applications of blockchain. Unit II: Models for Blockchain — GARAY model, RLA Model, Proof of Work (PoW), Hashcash PoW, PoW attacks and the monopoly problem, Proof of Stake (PoS), hybrid models (PoW+PoS), Proof of Burn, and Proof of Elapsed Time. Unit III: Permissioned Blockchain — Permissioned model and use cases, design issues for permissioned blockchains, state machine replication, consensus models for permissioned blockchain, distributed consensus in closed environment, Paxos, RAFT Consensus, Byzantine General Problem, Byzantine Fault Tolerant system, Lamport-Shostak-Pease BFT Algorithm, and BFT over asynchronous systems. Unit IV: Blockchain in Financial Service and Security — Digital currency, cross-border payments, Stellar and Ripple protocols, Project Ubin, Know Your Customer (KYC), privacy consents, mortgage over blockchain, blockchain-enabled trade, We Trade – Trade Finance Network, supply chain financing, insurance. Blockchain Security: security properties, security considerations for blockchain, Intel SGX, identities and policies, membership and access control, blockchain crypto service providers, privacy in a blockchain system, privacy through Fabric Channels, and smart contract confidentiality.

MDC Options – Semester 1 (Choose One from Pool):

Understanding Ambedkar (251/MPSIR/MD101) — 3 Credits | 75 Marks. Unit I: Life, Context and Intellectual Formation — social background, caste discrimination, influence of Phule, Buddhism, Enlightenment thought. Unit II: Ambedkar's Social and Political Thought — critique of caste, views on democracy, equality, political economy, critique of capitalism and Marxism. Unit III: Legacy and Contemporary Relevance — identity politics in modern India, Dalit movements, Ambedkarite politics, global relevance in Human Rights discourse.

Introduction to Economics (241/ECO/MD101) — 3 Credits | 75 Marks. Covers basic economics concepts including micro vs macro economics, law of supply and demand, economic growth vs development, GDP, sustainable development, mixed economy, capitalist economy.

Fundamentals of Geography (24/GEO/MD101) — 3 Credits | 75 Marks. Unit I: Shape/origin of Earth, maps, latitudes/longitudes, time zones. Unit II: Rocks, landforms, volcanoes, earthquakes, weathering. Unit III: Atmosphere, climate, temperature zones, winds, cyclones. Unit IV: Ocean relief, temperature, salinity, currents.

History and Culture of Haryana (MDC-1) — 3 Credits | 75 Marks. Unit I: Ancient Haryana — Harappan civilization, Vedic civilization, Mahabharata, Yaudheyas. Unit II: Medieval Haryana — Sultanate, Mughal period, Bhakti-Sufi movements. Unit III: Modern Haryana — national movement, Arya Samaj, 1857 revolt, non-cooperation movement. Unit IV: Brief overview of Haryana geography, industries, agriculture, tourism, social welfare schemes.

Database Management System (CC-A04 | 241/MCA/CC201) — 4 Credits | 100 Marks (25 Internal + 50 External + 5 Viva + 20 Practical). This course provides a comprehensive understanding of database concepts, data modeling, SQL, query processing, transaction management, concurrency control, database security, and recovery mechanisms. Unit I: Database System Concepts and Architecture — Characteristics and advantages of traditional file processing systems versus DBMS, database management systems, three-schema architecture, data independence, data models, schemas and instances, database languages and interfaces, and classification of DBMS. Data modeling concepts including Entity-Relationship (ER) diagrams, relational model constraints, relational database schemas, relational algebra, relational calculus, and Codd's Rules for relational databases. Unit II: Normalization and SQL — Functional dependencies and normalization techniques for relational databases, including normalization concepts from First Normal Form (1NF) to Boyce-Codd Normal Form (BCNF). SQL as a Fourth Generation Language (4GL), SQL components including DDL, DML, DQL, DCL, and TCL. Data definition, data types, constraints, queries, insert, delete and update statements, views, stored procedures, stored functions, database triggers, and SQL injection concepts. Unit III: Query Processing, Optimization and Transaction Processing — Translating SQL queries into relational algebra, basic algorithms for executing query operations, heuristic query optimization, selectivity and cost estimation techniques, and semantic query optimization. Transaction processing concepts including transaction properties (ACID), schedules and recoverability, serializability of schedules, transaction support in SQL, and fundamentals of database security and authorization. Unit IV: Concurrency Control and Database Recovery — Concurrency control techniques including locking mechanisms, timestamp ordering, multiversion concurrency control (MVCC), validation-based concurrency control, granularity of data items, multiple granularity locking, and concurrency control in indexes. Database recovery concepts including deferred update recovery, immediate update recovery, shadow paging, ARIES recovery algorithm, database backup strategies, and recovery from catastrophic failures.

Data Structures and Algorithms (CC-A05 | 241/MCA/CC202) — 4 Credits | 100 Marks. This course covers fundamental and advanced data structures, their implementation in C/C++, and algorithms for efficient data organization, manipulation, and problem-solving. Unit I: Introduction and Arrays — Data types including primitive, composite, and abstract data types. Concepts, classification, and importance of data structures; comparison of data structures and data types; linear and non-linear data structures. Arrays including single-dimensional and multidimensional arrays, address calculation using row-major and column-major ordering, array operations, vectors, sparse matrices, applications of arrays, and implementation of arrays in C/C++. Unit II: Stacks, Queues and Linked Lists — Representation of stacks and queues using arrays and linked lists. Circular queues, priority queues, and double-ended queues (D-Queues/Deque). Applications of stacks including infix to postfix and prefix conversion and postfix expression evaluation. Linked lists including singly linked lists and operations on linked lists, linked stacks and queues, polynomial representation and manipulation using linked lists, circular linked lists, doubly linked lists, and implementation in C/C++. Unit III: Trees and Heaps — Concepts, representation, and applications of trees and forests. Binary trees, threaded binary trees, binary tree representation of general trees, conversion of forests into trees, binary search trees, height-balanced AVL trees, B-Trees, B+ Trees, and B* Trees. Binary tree traversal methods including preorder, inorder, and postorder traversal, along with recursive algorithms. Heap structures including heap operations, binomial heaps, Fibonacci heaps, skew heaps, and heap sets. Unit IV: Graphs and Graph Algorithms — Graph representation using adjacency matrices and adjacency lists, types of graphs, Euler graphs, Hamiltonian paths and circuits, cut-sets, connectivity and separability, planar graphs, graph isomorphism, graph coloring, covering, and partitioning. Graph algorithms including Breadth-First Search (BFS), Depth-First Search (DFS), Minimum Spanning Tree algorithms (Prim's and Kruskal's), shortest-path algorithms (Dijkstra's and Floyd's), topological sorting, Ford-Fulkerson maximum flow algorithm, and Max-Flow Min-Cut theorem.

Object Oriented Programming Using Java (CC-A06 | 241/MCA/CC203) — 4 Credits | 100 Marks. This course introduces Java programming, object-oriented concepts, multithreading, exception handling, packages, interfaces, and GUI development using AWT and Swing. Unit I: Introduction to Java and Language Basics — Evolution of Java, overview and characteristics of Java, Java program compilation and execution process, Java Virtual Machine (JVM), platform independence and portability, security features, relationship between JVM, JRE, and JDK, and introduction to JAR format. Java language fundamentals including constants, data types, variables, scope of variables, type casting, operators, expressions, statements, arrays, and strings. Unit II: Object-Oriented Programming Implementation — Classes, objects, attributes, methods, data encapsulation, reference variables, introduction to methods, constructors, and constructor overloading. Inheritance and polymorphism including basics of inheritance, method overriding, the this keyword, super keyword, final variables, methods and classes, method overloading, method overriding, Java garbage collection, and the finalize() method. Unit III: Interfaces, Packages, Multithreading and Exception Handling — Interfaces and their implementation, differences between interfaces and abstract classes, using extends and implements together. Packages including defining packages, setting CLASSPATH, package naming conventions, creating packages, package accessibility, and using package members. Multithreading concepts including main thread, Java thread model, thread priorities, synchronization, and inter-thread communication. Exception handling including exceptions, try-catch blocks, handling multiple exceptions, finally clause, types of exceptions, and user-defined exceptions. Unit IV: Swing and AWT Programming — Introduction to Swing, Swing class hierarchy, containers, user interface components, and graphics context. AWT concepts including AWT components, component class, container class, and layout managers such as Border Layout, Flow Layout, Grid Layout, Card Layout, GridBag Layout, and Default Layout. Event handling in AWT including event models, listeners, adapters, Action Events, Focus Events, Key Events, Mouse Events, and Window Events.

Security in Computing (DSE | 241/MCA/DS201) — 3 Credits | 75 Marks (25 Internal + 50 External) | Duration: 3 hrs. This course covers security fundamentals, malware, network threats, cryptography, authentication, access control, intrusion detection, firewalls, and cyber laws. Unit I: Security Basics and Introduction to Malware — General overview, terminology and definitions, security models, security policy issues. Introduction to malicious code: spyware, ransomware, logic bombs, virus, bacteria and worms, and introduction to anti-malware technology. Unit II: Threats to Network Communications and Authentication — Threats to network communications including interception (eavesdropping and wiretapping), modification, fabrication (data corruption), interruption (loss of service), and port scanning. Introduction to cryptography and classical cryptosystems, steganography vs cryptography. Authentication: identification versus authentication, authentication based on something you know / something you are / something you have, federated identity management, multifactor authentication, secure authentication, and password policies. Unit III: Access Control and Intrusion Detection — Access control: access policies, implementing access control, procedure-oriented access control, role-based access control (RBAC), and CAPTCHAs. Intrusion Detection and Response: goals for intrusion detection systems, types of IDSs — anomaly-based and signature-based. Unit IV: Firewalls and Legal & Ethical Issues — Firewalls: what is a firewall, design of firewalls, types of firewalls, personal firewalls, comparison of firewall types, Network Address Translation (NAT), and example firewall configurations. Legal and Ethical Issues: protecting programs and data — copyrights, patents, trade secrets; information and the law — information as an object, legal issues relating to information, protection for computer artifacts; ethical issues in computer security; introduction to cyber crimes and cyber laws and IT Act 2000.

English Language Teaching – AEC Level 2 (241/ENG/AE201) — 2 Credits | 50 Marks (15 Internal + 35 External). This course focuses on English Language Teaching (ELT) theories and methods, grammar teaching through literature, and professional communication skills through business English and correspondence. Unit I: ELT – Approaches, Principles and Methods of ELT — English language learning theories, language teaching methods, approaches and principles of English Language Teaching (ELT), and analysis of learners' errors. Unit II: Literature, ELT and Grammar — Teaching English grammar through literature including poetry, fiction, and drama. Grammar games, English functional grammar, and the Indian and Western traditions of grammar. Unit III: Business English and Business Correspondence — Business Correspondence–I including building business vocabulary, news reports, business magazines, business letters, and business travel communication. Business Correspondence–II including writing using technology such as faxes and emails, preparing resumes, and writing minutes of meetings. Examination Pattern — Question 1: Short-answer questions (attempt any four out of six) carrying 8 marks (4 × 2). Question 2: Descriptive/essay question from Unit I (9 marks). Question 3: Descriptive/essay question from Unit II (9 marks). Question 4: Descriptive/essay question from Unit III (9 marks).

Problem Solving using Python Programming (SEC | 241/MCA/SE201) — 2 Credits | 50 Marks (5 Internal Theory + 20 External Theory + 5 Practical Internal + 20 Practical External) | Duration: 3 hrs. This Skill Enhancement Course develops algorithmic problem-solving skills and Python programming competence through hands-on practice. Unit I: Fundamentals of Computing and Algorithms — Identification of computational problems, algorithms, building blocks of algorithms (statements, state, control flow, functions), notation (pseudocode, flowchart, programming language), algorithmic problem solving, and simple strategies for developing algorithms (iteration, recursion). Illustrative problems: find minimum in a list, insert a card in a list of sorted cards, guess an integer number in a range, Towers of Hanoi. Unit II: Python Basics — Python interpreter and interactive mode, debugging; values and types: int, float, boolean, string, and list; variables, expressions, statements, tuple assignment, precedence of operators, and comments. Illustrative programs: exchange the values of two variables, circulate the values of n variables, distance between two points. Unit III: Conditionals, Iteration, Functions and Strings — Conditionals: boolean values and operators, conditional (if), alternative (if-else), chained conditional (if-elif-else). Iteration: state, while, for, break, continue, pass. Fruitful functions: return values, parameters, local and global scope, function composition, recursion. Strings: string slices, immutability, string functions and methods, string module. Lists as arrays. Illustrative programs: square root, GCD, exponentiation, sum an array of numbers, linear search, binary search. Unit IV: Lists, Tuples, Dictionaries, Files and Exceptions — Lists: list operations, list slices, list methods, list loop, mutability, aliasing, cloning lists, list parameters. Tuples: tuple assignment, tuple as return value. Dictionaries: operations and methods. Advanced list processing — list comprehension. Illustrative programs: simple sorting, histogram, student marks statement, retail bill preparation. Files and exceptions: text files, reading and writing files, format operator, command line arguments, errors and exceptions, handling exceptions, modules, packages. Illustrative programs: word count, copy file, voter's age validation, marks range validation.

MDC Options – Semester 2 (Choose One from Pool):

Understanding Gandhi (251/MPS/MD201) — 3 Credits | 75 Marks. Unit I: Life and Intellectual Influences — Gandhi's early life in India and South Africa, influence of Tolstoy, Thoreau, Ruskin, Gita, Jain traditions, experiment with truth. Unit II: Gandhi's Political Thought and Social Philosophy — critique of Western civilization, Swaraj and Trusteeship, Satyagraha, Civil Disobedience, religion, communal harmony, influence dialogue. Unit III: Legacy and Contemporary Relevance — Gandhi's influence on global movements, Black movements, environmentalism; Gandhi in Contemporary India: relevance and contestations.

Financial Institution & Market (241/ECO/MD201) — 3 Credits | 75 Marks. Covers Non-Banking Financial Companies (NBFCs), Call Money Market, Gilt-Edged Securities, Equity, Commercial Paper, Mutual Funds, SEBI objectives and functions, Money Market, Stock Market, Government Bonds, Corporate Bonds, Indian financial system.

Geography of Haryana (241/GEO/MD201 — Paper ID 234020) — Physiographic Regions, Semi-Desert Plains, Soils, Rainfall Patterns, Population Growth, Tube Well Irrigation, Rice Cultivation, IT-Based Industries, Railway Network, Khadar, Bhindawas Wildlife Sanctuary, Cyber City of Haryana.

Historical Applications of Tourism (Paper ID 234030) — Heritage tourism, cultural tourism, temple circuits, Mahabalipuram, Kanchipuram, Tanjore, Haryana tourism, rural tourism, travel agencies.

Software Engineering (CC-A07) — 4 Credits | 100 Marks. This course introduces software engineering principles, software development life cycle models, requirements engineering, software design, testing, quality assurance, project management, software metrics, maintenance, and configuration management. Unit I: Introduction, Software Processes and Requirements Engineering — Software and its characteristics, evolving role of software, software products, software processes, software crisis, evolution and principles of software engineering, programming-in-the-small versus programming-in-the-large, software components, and software engineering processes. Software Life Cycle (SLC) models including Waterfall Model, Prototype Model, Spiral Model, Evolutionary Development Models, Iterative Enhancement Models, Object-Oriented Models, and other modern software development models. Software requirements including functional and non-functional requirements, user requirements, system requirements, software requirements specification/documentation, feasibility studies, requirements elicitation and analysis, requirements validation, and requirements management. Unit II: Software Design and Testing — Basic concepts of software design, architectural design, low-level design, modularization, design structure charts, flowcharts, coupling and cohesion measures. Design strategies including function-oriented design, object-oriented design, top-down and bottom-up design approaches, user interface design, programming practices, and coding standards. Software testing concepts including verification versus validation, software reliability, levels of testing, structural testing (White Box Testing), and functional testing (Black Box Testing). Unit III: Software Quality, Project Management and Metrics — Software quality attributes, Software Quality Assurance (SQA) plans and activities, and software documentation. Software project management activities including project estimation, project planning, and project scheduling. Software risk management including reactive and proactive risk strategies, risk identification, risk projection, risk refinement, risk mitigation, risk monitoring, and risk management. Software measurement and metrics including process metrics, project metrics, LOC (Lines of Code), Halstead's Software Science, Function Point (FP) analysis, Cyclomatic Complexity measures, and software project estimation models such as Empirical Models, Putnam Model, and COCOMO I & II. Unit IV: Software Maintenance and Configuration Management — Need for software maintenance, categories of maintenance including preventive, corrective, and perfective maintenance, cost of maintenance, software re-engineering, reverse engineering, and software documentation. Software Configuration Management (SCM) activities, change control process, software version control, software reuse, and software evolution.

Full Stack Programming – 1 — 3 Credits | 75 Marks (15 Internal Theory + 35 External Theory + 5 Internal Practical + 20 External Practical). This course introduces the fundamentals of full-stack web development, covering HTML, CSS, JavaScript, responsive web design, and AngularJS for developing modern web applications. Unit I: Introduction to Full Stack Development and HTML — Need for Full Stack Development, Web Development versus Full Stack Development, Client-Server Architecture, Rules of Three-Tier Architecture, Anatomy of a Website, Web Hosting Process, HTML fundamentals, HTML Document Object Model (DOM), W3C standards for HTML, structural and semantic markup, HTML lists and links, absolute and relative path names, URL anatomy and types, HTML formatting, tables, meta tags, structural tags, character entities, and form input types. Unit II: CSS and Responsive Web Design — CSS fundamentals, W3C CSS Validator, types of CSS, CSS selectors, cascading, inheritance, specificity, units of measurement, width and height of elements, CSS Box Model, Border Box versus Content Box, responsive website design, Bootstrap Grid System, CSS preprocessors including Less and Sass, and their features. Unit III: JavaScript Programming — JavaScript language basics, objects, strings, numbers, Math object, arrays, Boolean values, JavaScript scope, events, comparison operators, conditional statements, switch statements, loops, regular expressions (RegExp), error handling, debugging techniques, hoisting, strict mode, functions, objects, forms, HTML DOM, Browser Object Model (BOM), and differences between DOM and BOM. Unit IV: AngularJS Development — Introduction to AngularJS, AngularJS expressions, modules, data binding, scopes, directives and events, controllers, filters, services, HTTP services, AngularJS tables, select controls, and fetching data from MySQL databases using AngularJS. Examination Pattern: The examiner will set nine questions in total. Question 1 will contain seven short-answer parts from all units and carry 20% of the total marks. The remaining eight questions will be set by taking two questions from each unit. Students must attempt five questions in total, including the compulsory Question 1 and one question from each unit.

Computer System Architecture (CC-A08) — 4 Credits | 100 Marks (30 Internal + 70 External). This course provides a comprehensive understanding of number systems, logic design, combinational and sequential circuits, computer organization, CPU architecture, assembly language programming, input/output organization, and parallel processing architectures. Unit I: Number Systems, Codes and Logic Design — Number systems including Binary, Octal, Decimal, and Hexadecimal representations, 1's and 2's complements, and interconversion of numbers. Codes including weighted and non-weighted codes, BCD codes, Gray codes, self-complementing codes, error-detecting and error-correcting codes, alphanumeric codes, and Hamming codes. Floating-point numbers, binary arithmetic, binary addition and subtraction, 2's complement arithmetic, Booth coding, and binary multiplication. Logic design concepts including logic gates, truth tables, Boolean algebra, Boolean expressions, variables and literals, equivalent and complement expressions, Boolean algebra theorems, simplification techniques, and SOP/POS representations. Unit II: Combinational and Sequential Circuits — Combinational logic circuits including arithmetic circuits, adders and subtracters, BCD adders, code converters, magnitude comparators, parity generators/checkers, multiplexers, demultiplexers, decoders, and encoders. Sequential circuits including latches, RS flip-flops, level-triggered and edge-triggered flip-flops, JK flip-flops, master-slave flip-flops, T flip-flops, and D flip-flops. Unit III: Basic Computer Design and CPU Architecture — Computer instructions and instruction types, instruction sets, instruction cycle, instruction formats, addressing modes, computer registers, bus systems, and register transfer language (RTL) terminology. Programming in 8086/8088 Assembly Language including program structure, segments, registers, instructions, macros, and assembly language directives. CPU design concepts including CPU registers, micro-operations and their types, ALU design, control unit design using microprogramming, timing and control, hardwired and microprogrammed control units, and processor architectures including RISC, CISC, Scalar, Superscalar, and Pipelined Architectures. Unit IV: Input/Output Organization and Advanced Architectures — Input/Output organization including peripheral devices, input-output interfaces, asynchronous data transfer, modes of transfer, priority interrupts, direct memory access (DMA), input-output processors, and serial communication. Advanced computer architectures including parallel processing concepts, pipelining, parallel computer structures, architectural classifications, instruction and arithmetic pipelines, principles of pipelined processor design, SIMD array processors, SIMD interconnection networks, vector processing, and applications of parallel processing.

Data Communication and Computer Networks (CC-A09) — 4 Credits | 100 Marks. This course introduces the fundamentals of computer communications, networking technologies, transmission media, data link layer protocols, routing algorithms, and network security concepts. Unit I: Introduction to Computer Communications and Networking — Introduction to computer communications and networking technologies, uses of computer networks, network devices, nodes and hosts, types of computer networks and their topologies. Network software concepts including network design issues and protocols, connection-oriented and connectionless services, network applications and application protocols. Computer communication and networking models including decentralized and centralized systems, distributed systems, client-server model, peer-to-peer model, and web-based model. Network architecture including the OSI Reference Model and TCP/IP Reference Model. Overview of example networks such as the Internet, X.25, Frame Relay, and ATM. Unit II: Data Communication and Transmission Technologies — Analog and digital communication concepts, data, signals, channels, bit rate, maximum channel data rate, representation of data as analog and digital signals, data rate and bandwidth capacity, baud rate, asynchronous and synchronous transmission. Data encoding techniques, modulation techniques, guided and wireless transmission media, communication satellites, switching and multiplexing techniques, dial-up networking, analog modem concepts, and DSL services. Unit III: Data Link Layer and LAN Technologies — Data Link Layer concepts including framing, flow control, error control, error detection and correction techniques, and sliding window protocols. Media Access Control (MAC) protocols including random access protocols and token passing protocols. LAN technologies including Ethernet, Switched Ethernet, VLAN, Fast Ethernet, Gigabit Ethernet, Token Ring, FDDI, Wireless LANs, and Bluetooth. Network hardware components including connectors, transceivers, repeaters, hubs, network interface cards (NICs), PC cards, bridges, switches, routers, and gateways. Unit IV: Network Layer, Routing and Security — Network Layer concepts including virtual circuits and datagrams, routing algorithms, flooding, shortest path routing, distance vector routing, link state routing, hierarchical routing, congestion control algorithms, and internetworking. Network security concepts including security threats, encryption methods, authentication mechanisms, symmetric-key cryptography, and public-key cryptography algorithms.

MDC Options – Semester 3 (Choose One from Pool):

Probability and Statistics (41/MCA/MD301) — 3 Credits | 75 Marks. Unit I: Statistical Methods, definition and scope of statistics, statistical population and sample, quantitative and qualitative data, attributes and variables, scales of measurement, measures of central tendency (Mean, Median, Mode), measures of dispersion (Range, Quartile Deviation, Mean Deviation, Standard Deviation), coefficient of variation, moments, skewness and kurtosis. Unit II: Correlation and Regression Analysis, Karl Pearson’s coefficient of correlation, rank correlation, regression lines, regression problems, curve fitting using the method of least squares. Unit III: Probability concepts, random experiments, sample space, events, classical, statistical and axiomatic definitions of probability, conditional probability, laws of addition and multiplication, independent events, theorem of total probability and Bayes’ theorem. Unit IV: Probability distributions, random variables (discrete and continuous), probability mass and density functions, Binomial, Poisson, Exponential and Normal distributions.

Fundamentals of Electrical and Electronics Engineering (41/MCA/MD302) — 3 Credits | 75 Marks. Unit I: DC Circuits, electrical quantities, circuit elements (R, L, C), voltage and current sources, series and parallel circuits, Kirchhoff’s laws, nodal and mesh analysis, source transformation, star-delta conversion, Superposition, Thevenin, Norton, Millman, Substitution and Reciprocity theorems. Unit II: AC Circuits, sinusoidal waveforms, RMS and average values, impedance, phasor representation, real and reactive power, power factor, resonance, analysis of RL, RC and RLC circuits, introduction to three-phase circuits. Unit III: Semiconductor devices, p-n junction diode, rectifiers, clipping and clamping circuits, varactor, voltage regulators, Bipolar Junction Transistors (BJT), transistor characteristics, biasing methods, switching circuits, thermal stability and transistor testing. Unit IV: Field Effect Devices, JFET characteristics, pinch-off voltage, transconductance, amplification factor, small signal models, MOSFET operation, depletion and enhancement modes, Shockley equation and MOS capacitor concepts.

Other MDC Subjects — Students may also be offered additional Multidisciplinary Course (MDC) subjects by the College/University as per NEP guidelines and university regulations.

Mobile Application Development (SEC-2) — 2 Credits | 100 Marks (30 Internal + 70 External). This course introduces the fundamentals of Android application development, covering Android architecture, UI design, event handling, fragments, location-based services, and data persistence using SQLite. Unit I: Android Fundamentals and UI Design — Introduction to Android, features of Android, Android architecture, Android file structure, layouts including Linear, Relative, Grid, and Table layouts. Views and resources, activities and intents, activity lifecycle and saving state. User Interface (UI) components including editable and non-editable text views, buttons, radio and toggle buttons, checkboxes, spinners, dialogs and pickers, list view, and spinner view. Unit II: Event Handling and Intents — Event handling for various UI components such as click and change events. Intents in Android including explicit intents for starting new activities, implicit intents, passing data between activities, retrieving results from activities, and using intents for actions such as dialing a number or sending SMS. Unit III: Fragments and Location-Based Services — Fragments in Android including creation, lifecycle, states, adding fragments to activities, and fragment transactions such as adding, removing, and replacing fragments. Location and mapping services including location-based services, Google Maps integration, working with MapView and MapActivity, and multimedia handling including audio and video playback and recording within applications. Unit IV: Data Persistence and Application Deployment — Persisting data using internal and external storage. Introduction to SQLite database including creating and opening databases, creating tables, inserting, retrieving, and deleting data. Application signing, API keys for Google Maps, and publishing applications to the Android Market.

Human Values and Community Outreach (VAC-2) — 3 Credits | 50 Marks (15 Internal + 35 External). This course focuses on human values, ethical understanding, self-awareness, communication skills, and community-oriented thinking for holistic personal and social development. Unit I: Human Values and Education — Motivation and objectives of the human values course, purpose of education, complementarity of skills and values, limitations of the current education system, peer pressure and social pressure in various dimensions of life, concept of competition, and time management. Unit II: Human Relationships and Social Understanding — Concept of preconditioning, natural acceptance in human beings, understanding relationships, dealing with anger, nine universal values in human relationships, concept of prosperity, idea of society, decentralization of politics, economics, education, and justice, comparison with centralized systems, and balance in nature. Unit III: Writing and Communication Skills — Techniques of good writing, self-assessment writing tasks, précis writing and note making, paragraph and essay writing, article writing, and summarizing skills. Unit IV: Business Communication — Formal and informal letter writing, statement of purpose, job application writing, CV/resume preparation including academic and professional profiles, and PowerPoint presentations using relevant slides.

Soft Computing (CC-A10) — 4 Credits | 100 Marks. This course introduces the fundamental concepts of soft computing, genetic algorithms, artificial neural networks, fuzzy systems, and their applications in solving real-world problems. Unit I: Introduction to Soft Computing and Genetic Algorithms — Introduction to soft computing, Soft Computing versus Hard Computing, components of soft computing including Artificial Intelligence systems, Neural Networks, Fuzzy Logic, and Genetic Algorithms. Genetic Algorithms (GA) covering basic concepts, encoding techniques, fitness functions, reproduction methods including roulette wheel selection, Boltzmann selection, tournament selection, rank selection, and steady-state selection. Convergence of genetic algorithms and problem-solving using GA. Unit II: Artificial Neural Networks — Introduction to biological and artificial neural networks, various artificial neural network models, supervised learning, unsupervised learning, and reinforcement learning. Hebbian learning and Generalized Hebbian Learning Algorithm. Artificial Neural Network architecture including the basic building block of an artificial neuron, activation functions, McCulloch-Pitts model, Single Perceptron, Backpropagation Networks, Multi-Layer Perceptron (MLP), Hopfield Network, and applications of neural networks. Unit III: Fuzzy Systems and Applications — Concepts of fuzziness, membership functions, fuzzification and defuzzification processes, operations on fuzzy sets, fuzzy functions, linguistic variables, fuzzy relations, fuzzy rules, fuzzy inference mechanisms, fuzzy control systems, and fuzzy rule-based systems. Unit IV: Applications of Soft Computing — Applications of soft computing in pattern recognition, image processing, biological sequence alignment, drug design, robotics and sensor systems, information retrieval systems, share market analysis, and natural language processing (NLP).

Data Science and Visualization (CC-A11) — 4 Credits | 100 Marks (25 Internal Theory + 50 External Theory + 5 Internal Practical + 20 External Practical). This course introduces Python programming, data analysis using NumPy and Pandas, data visualization techniques, machine learning, deep learning frameworks, and natural language processing tools. Unit I: Python Programming Fundamentals — Overview of Python programming concepts including data types, variables, assignments, numerical data types, operators and expressions, control structures, string manipulation, file handling (creating, reading, and writing text and numeric files), dictionaries, functions, and object-oriented programming (OOP) concepts. Unit II: NumPy and Pandas for Data Analysis — Introduction to NumPy including array creation, array generation using uniform distributions, random array generation, reshaping arrays, finding maximum and minimum values, arithmetic operations, mathematical functions, bracket indexing and selection, broadcasting, and indexing two-dimensional arrays (matrices). Data manipulation using Pandas including creating Series from lists, arrays, and dictionaries, storing data from intrinsic sources, creating DataFrames, data imputation, grouping and aggregation, merging, joining, concatenation, finding null values, and reading data from CSV, TXT, Excel, and web sources. Unit III: Data Visualization Techniques — Introduction to visualization, installation and setup of visualization libraries, canvas and axes, subplots, common plots including scatter plots, histograms, boxplots, logarithmic scale plots, tick placement and custom labels. Pandas visualization, style sheets, plot types, area plots, bar plots, line plots, scatter plots, box plots, hexagonal bin plots, Kernel Density Estimation (KDE) plots, distribution plots, categorical data plots, combined categorical plots, matrix plots, regression plots, grids, and overview of Python visualization toolkits and libraries. Unit IV: Machine Learning and Natural Language Processing — Introduction to machine learning using Scikit-Learn and PyTorch, data representation, estimators, parameters, model validation, model selection, learning curves, grid search, feature engineering, Naive Bayes Classification, Linear Regression, Support Vector Machines (SVM), and overview of Python machine learning and deep learning libraries. Introduction to Natural Language Processing (NLP) using NLTK, including tokenization, speech tagging, parsing, segmentation, recognition, text cleaning and normalization, along with an overview of other Python NLP toolkits and libraries. Examination Pattern: The examiner will set nine questions in total. Question 1 will contain seven short-answer parts from all units and will carry 20% of the total marks. The remaining eight questions will be set by taking two questions from each unit. Students must attempt five questions in total, including the compulsory Question 1 and one question from each unit.

Full Stack Programming – 2 (DSE-04) — 3 Credits | 75 Marks (15 Internal Theory + 35 External Theory + 5 Internal Practical + 20 External Practical). This course focuses on modern full-stack web development technologies including Node.js, Express.js, MongoDB, Angular, React, and the MERN stack for building scalable web applications. Unit I: Web Development Frameworks and Architecture — Understanding the fundamentals of web development including User, Browser, Web Server, Backend Services, and MVC (Model-View-Controller) Architecture. Overview of different technology stacks and the roles of Express.js, Angular, Node.js, MongoDB, and React in modern full-stack application development. Unit II: Node.js Fundamentals and Backend Development — Basics of Node.js, installation and setup, working with Node packages, using Node Package Manager (NPM), creating simple Node.js applications, event-driven programming, event listeners, timers, callbacks, handling data input/output operations, and implementing HTTP services using Node.js. Unit III: NoSQL Databases and MongoDB — Introduction to NoSQL databases and MongoDB, setting up the MongoDB environment, user accounts and access control, database administration, collection management, connecting MongoDB with Node.js applications, and developing simple database-driven applications. Unit IV: Express.js, Angular and React Development — Implementing Express.js in Node.js applications, Angular fundamentals including TypeScript, Angular components, expressions, data binding, and built-in directives. MERN Stack development concepts, creating basic React applications, React components, React state management, Express REST APIs, modularization using Webpack, routing with React Router, and server-side rendering techniques. Examination Pattern: The examiner will set nine questions in total. Question 1 will contain seven short-answer parts from all units and carry 20% of the total marks. The remaining eight questions will be set by taking two questions from each unit. Students must attempt five questions in total, including the compulsory Question 1 and one question from each unit.

MDC Options – Semester 4 (Choose One from Pool):

Cloud, Edge & Fog Computing (241/MCA/MD401) — 3 Credits | 75 Marks. Unit I: Introduction to Cloud Computing, cloud characteristics, benefits and limitations, evolution of cloud computing, NIST model, cloud cube model, cloud vs client-server, cluster and grid computing, deployment models (public, private, hybrid, community), service models (IaaS, PaaS, SaaS, IDaaS, CaaS), cloud applications and healthcare use cases. Unit II: Cloud Management and Virtualization, Service Oriented Architecture (SOA), Service Level Agreements (SLAs), cloud lifecycle management, virtualization concepts, hypervisors, machine imaging, load balancing, VMware case study, cloud security challenges, security standards and cloud services by Amazon, Microsoft and Oracle. Unit III: Fog Computing concepts, architecture, applications, services, fog protocols, DDS/RTPS protocols, Fog Kit, privacy-preserving computation, blockchain technology and multi-party computation in fog environments. Unit IV: Edge Computing concepts, architectures, applications, cloud-edge-fog comparison, mobile edge computing, resource federation challenges, middleware infrastructures and security management in edge cloud architectures.

Internet of Things (41/MCA/MD402) — 3 Credits | 75 Marks. Unit I: Introduction to IoT, characteristics, physical and logical design of IoT, functional blocks, communication models, APIs, Machine-to-Machine (M2M) communication, software-defined networking and IoT security challenges. Unit II: Network and Communication Aspects, wireless medium access issues, MAC protocols, routing protocols, sensor deployment, node discovery, data aggregation and dissemination techniques. Unit III: Web of Things (WoT), IoT vs WoT, web architecture and standardization, unified multi-tier WoT architecture, business intelligence, cloud of things, cloud middleware and cloud standards. Unit IV: Resource Management in IoT, home automation, industrial and surveillance applications, domain-specific IoT solutions, clustering, synchronization, software agents and performance analysis of IoT systems.

Other MDC Subjects — Students may also be offered additional Multidisciplinary Course (MDC) subjects by the College/University as per NEP guidelines and university regulations.

English Language Communication – Level 3 (AEC | 241/ENG/AE301) — 2 Credits | 50 Marks (15 Internal + 35 External). This Ability Enhancement Course develops advanced professional communication competencies including technical writing, presentation skills, and workplace English for postgraduate students entering the IT industry. Unit I: Advanced Reading and Critical Thinking — Reading strategies for academic and professional texts, reading comprehension at advanced level, critical analysis of arguments, evaluating sources and evidence, inference and interpretation, summarizing and synthesizing information from multiple sources, and reading technical documentation and research abstracts. Unit II: Technical and Professional Writing — Writing technical reports, project reports, and proposals; executive summaries; formal and informal email etiquette in professional settings; writing research abstracts and literature reviews; documentation writing for software projects; editing and proofreading for clarity and conciseness; and writing for digital platforms. Unit III: Presentation and Oral Communication Skills — Planning and structuring presentations, visual aids and slide design principles, public speaking techniques, handling questions and answers, group discussions and debate skills, interview skills and mock interviews, telephone and video conferencing etiquette, and cross-cultural communication in global workplaces. Unit IV: Workplace English and Soft Skills — Professional vocabulary for the IT industry, writing minutes of meetings and agendas, negotiation language, conflict resolution communication, networking and professional relationship building, LinkedIn and professional digital presence, workplace etiquette, and preparing for campus placements (GD, PI, and aptitude communication). Examination Pattern: Question 1: Short-answer questions (attempt any four out of six) carrying 8 marks (4 × 2). Questions 2, 3, 4: One descriptive/essay question each from Units I, II, and III respectively, each carrying 9 marks.