A clear understanding of the UGC NET Computer Science & Applications Syllabus 2026 is the first step toward smart preparation. Computer Science (Subject Code 87) is a highly technical subject covering everything from discrete mathematics and computer architecture to DBMS, operating systems, networks, algorithms and artificial intelligence. In this guide we explain the full syllabus for Paper 1 and Paper 2, break down all 10 units topic-by-topic, cover the exam pattern, list important topics and trusted books, and share a practical study plan — all in simple language.

Unit I • Discrete Structures and Optimization

• Mathematical logic – propositional and predicate logic, normal forms
• Sets, relations and partial ordering
• Counting, permutations, combinations and probability
• Group theory – groups, rings and fields
• Graph theory – trees, graph colouring, Eulerian and Hamiltonian graphs
• Boolean algebra and simplification
• Optimization – linear programming, simplex, transportation, PERT-CPM

Unit II • Computer System Architecture

• Digital logic – gates, flip-flops and sequential circuits
• Data representation and number systems
• Register transfer and micro-operations
• Computer organization – instruction cycle, interrupts
• CPU organization – addressing modes, RISC and CISC
• Memory hierarchy – cache, virtual and main memory
• Multiprocessors and cache coherence

Unit III • Programming Languages and Computer Graphics

• Language concepts and programming paradigms
• Programming in C – arrays, pointers, functions, files
• Object-oriented programming – class, inheritance, polymorphism
• Programming in C++ – templates, constructors, exception handling
• Web programming – HTML, XML, Java, servlets, applets
• Computer graphics – raster scanning, line drawing, polygon filling
• 2D and 3D transformations – rotation, scaling, projection

Unit IV • Database Management Systems

• Database concepts and DBMS architecture
• Data modeling – ER model, relational algebra, Codd’s rules
• SQL – queries, constraints, procedures, triggers
• Normalization and functional dependencies
• Advanced databases – distributed and XML databases
• Data mining – clustering, classification, regression
• Big Data and NoSQL – MapReduce, HDFS, Hadoop

Unit V • System Software and Operating System

• System software – assemblers, linkers, loaders
• OS basics – system calls, OS structure, boot process
• Process management and interprocess communication
• CPU scheduling – real-time and multi-core
• Deadlocks – prevention, detection and recovery
• Memory management – paging and segmentation
• Security – access control, cryptography, authentication

Unit VI • Software Engineering

• Process models – SDLC, Agile, Scrum, XP
• Software requirements and SRS
• Software design – modularity, coupling, cohesion
• Software quality – ISO standards, risk management
• Estimation – LOC, function points, COCOMO
• Testing – unit, black box, regression testing
• Configuration management and version control

Unit VII • Data Structures and Algorithms

• Data structures – arrays, trees, graphs, hashing
• Algorithm analysis – time and space complexity
• Design techniques – divide and conquer, greedy, dynamic programming
• Graph algorithms – BFS, DFS, shortest path
• Complexity theory – P, NP, NP-complete
• Advanced algorithms – FFT and approximation algorithms

Unit VIII • Theory of Computation and Compilers

• Formal languages and Church-Turing thesis
• Regular languages – DFA, NFA, regular expressions
• Context-free grammar – PDA, CNF, parse trees
• Turing machines and recursive languages
• Syntax analysis – LL(1), LR and LALR parsers
• Semantic analysis – attribute grammar, type checking
• Code generation and optimization

Unit IX • Data Communication and Computer Networks

• Data communication – bandwidth, transmission media, modulation
• Computer networks – LAN, WAN, wireless networks
• Network models – OSI and TCP/IP
• Internet protocols – IPv4, IPv6, TCP, UDP
• WWW concepts – DNS, SMTP, POP, FTP
• Network security – firewall, VPN, cryptography
• Cloud and IoT – SaaS, PaaS, IaaS, IoT basics

Unit X • Artificial Intelligence

• AI approaches – Turing test, rational agents
• Knowledge representation – semantic networks, ontologies
• Planning – goal stack planning, STRIPS
• Natural language processing – parsing, semantic analysis
• Multi-agent systems and the semantic web
• Fuzzy logic and membership functions
• Genetic algorithms and neural networks

• DBMS – ER modelling, normalization, transactions
• Data Structures – sorting techniques, dynamic programming
• Operating System – process scheduling, synchronization, deadlock, memory
• Computer Networks – IP addressing and protocols
• Theory of Computation – context-sensitive grammar and automata
• Software Engineering – development process, coding and testing

• Graph Theory with Applications to Engineering and Computer Science – Narsingh Deo
• Computer Networks – Andrew S. Tanenbaum
• Operating System Concepts – Silberschatz, Galvin
• Database System Concepts – Korth & Sudarshan
• Trueman’s UGC NET Computer Science & previous-year papers

Tip: Build strong fundamentals first, then practise numerical and application-based questions through previous-year papers and mock tests.

1. Follow the latest syllabus: Study strictly from the current NTA syllabus (Code 87) and list out all important topics first.

2. Set weekly goals: Divide the 10 units across weeks so the whole syllabus is covered with time for revision.

3. Strengthen core subjects: Focus on DBMS, OS, Data Structures, Networks and Theory of Computation – they carry heavy weightage.

4. Make short notes: Prepare concise notes and formula/definition sheets for quick last-minute revision.

5. Solve PYQs & mocks: Practise previous-year papers and take regular mock tests to understand the pattern and improve speed.