Course Structure
| # | Category | Subject | L-T-P | Credits |
|---|---|---|---|---|
| 1 | Professional Core | Data Warehousing & Data Mining | 3-0-0 | 3 |
| 2 | Professional Core | Computer Networks | 3-0-0 | 3 |
| 3 | Professional Core | Formal Languages and Automata Theory | 3-0-0 | 3 |
| 4 | Professional Elective-I | Object Oriented Analysis and Design / Artificial Intelligence / Microprocessors & Microcontrollers / Quantum Computing / 12-week MOOC (SWAYAM/NPTEL) | 3-0-0 | 3 |
| 5 | Open Elective-I | Open Elective-I OR Entrepreneurship Development & Venture Creation | 3-0-0 | 3 |
| 6 | Professional Core | Data Mining Lab | 0-0-3 | 1.5 |
| 7 | Professional Core | Computer Networks Lab | 0-0-3 | 1.5 |
| 8 | Skill Enhancement Course | Full Stack Development-2 | 0-1-2 | 2 |
| 9 | Engineering Science | User Interface Design using Flutter / SWAYAM Plus – Android Application Development (with Flutter) | 0-0-2 | 1 |
| 10 | Community Service | Evaluation of Community Service Internship | – – – | 2 |
Total: L 15 – T 1 – P 10 – C 23
Professional Core Subjects
Data Warehousing & Data Mining
— the course that turns raw transactional data into the classification, clustering, and association models behind recommendation engines and BI dashboards.
- Unit 1: Data warehouse fundamentals — OLAP, data cube modeling, warehouse design and implementation, cloud data warehousing, plus core data/pattern mining concepts, data objects, statistical description, and visualization.
- Unit 2: Data preprocessing — cleaning noisy records, integrating multiple sources, reducing dimensionality, and transforming/discretizing raw attributes before modeling.
- Unit 3: Classification — decision tree induction, attribute selection measures and pruning, Bayesian and rule-based classifiers, and model evaluation/selection.
- Unit 4: Association rule mining — frequent itemset generation, confidence-based rule pruning, the Apriori algorithm, and the more scalable FP-Growth approach.
- Unit 5: Cluster analysis — K-means and its variants (bisecting K-means), agglomerative hierarchical clustering, and density-based DBSCAN, including their respective strengths and weaknesses.
Computer Networks
— the layered-architecture course (OSI/TCP-IP) that explains how data actually gets from one machine to another, and the base for every networking interview question.
- Unit 1: Network types and topologies, a comparison of the OSI and TCP/IP reference models, and the physical layer — guided media (twisted pair, coaxial, fiber) versus unguided transmission.
- Unit 2: Data link layer — framing techniques, error detection/correction (CRC, checksums), elementary protocols for noisy and noiseless channels, sliding-window schemes (Go-Back-N, Selective Repeat), HDLC, and PPP.
- Unit 3: Media access control — random access (ALOHA, CSMA/CD, CSMA/CA), controlled access and channelization (FDMA/TDMA/CDMA), and Ethernet standards from classic to 10-Gigabit.
- Unit 4: Network layer — packet-switching design issues, routing algorithms (shortest path, flooding, distance vector, link state), congestion control, and internetworking including IPv4/IPv6 addressing, CIDR, and fragmentation.
- Unit 5: Transport and application layers — UDP and TCP mechanics (flow, error, and congestion control), plus HTTP, email, Telnet, and DNS as the services riding on top.
Formal Languages and Automata Theory
— the theoretical-CS course explaining why some problems are computable and others aren’t, and the mathematical backbone behind every compiler and regex engine.
- Unit 1: Finite automata — DFA/NFA design, epsilon-transitions, DFA-NFA equivalence and conversion, minimization, and Mealy/Moore machines.
- Unit 2: Regular expressions and regular sets — identity rules, the pumping lemma, closure properties, and the Chomsky grammar hierarchy linking regular grammars to finite automata.
- Unit 3: Context-free grammars — leftmost/rightmost derivations, parse trees, ambiguity, grammar simplification, and normal forms (Chomsky and Greibach).
- Unit 4: Pushdown automata — design, deterministic versus non-deterministic variants, and their equivalence to context-free grammars.
- Unit 5: Turing machines — models and design, decidability versus undecidability, the halting problem, Post’s Correspondence Problem, and the P/NP/NP-hard/NP-complete complexity classes.
Professional Elective-I (choose one)
Object Oriented Analysis and Design
— a UML-first modeling course for students who want to design real systems on a whiteboard before writing a line of code.
- Unit 1: The nature of complex software systems — why large systems get complicated, their key attributes, and structured approaches to managing that complexity.
- Unit 2: UML foundations — modeling principles, the UML conceptual architecture, and basic structural modeling using classes, relationships, and standard diagrams.
- Unit 3: Class and object diagrams in depth — advanced structural modeling covering interfaces, types, roles, and packages.
- Unit 4: Behavioral modeling — use cases, use-case diagrams, interaction diagrams, and activity diagrams for capturing system behavior.
- Unit 5: Advanced behavioral and architectural modeling — events, state machines, state-chart diagrams, and component/deployment diagrams for runtime architecture.
Artificial Intelligence
— the foundational AI course walking from search algorithms to knowledge representation to the reasoning logic behind expert systems, underpinning every later ML/AI elective.
- Unit 1: Intelligent agents — problem formulation, the concept of rationality, and how agents perceive and act across different environment types.
- Unit 2: Search strategies — uninformed search (BFS, DFS), heuristic search (Hill Climbing, A, AO), and adversarial game-playing search including minimax and alpha-beta pruning.
- Unit 3: Knowledge representation — predicate logic, semantic networks, frames, rule-based deduction, and reasoning under uncertainty via probability and Dempster-Shafer theory.
- Unit 4: Logic and learning — first-order logic inference, unification, forward/backward chaining, resolution, and learning approaches from decision trees to reinforcement learning.
- Unit 5: Expert systems — architecture, knowledge acquisition, and classic case studies such as MYCIN, DART, and XCON.
Microprocessors & Microcontrollers
— the hardware-facing elective that drops students to register level with 8086 assembly and 8051 interfacing, ahead of any embedded-systems work.
- Unit 1: 8086 architecture — internal structure, bus interface and execution units, interrupt handling, and minimum/maximum mode configurations.
- Unit 2: 8086 assembly programming — instruction set, addressing modes, assembler directives, and program development tools.
- Unit 3: 8086 interfacing — memory interfacing, the 8255 peripheral interface, switches/LEDs/seven-segment displays, USART, DMA, and A/D-D/A conversion.
- Unit 4: 8051 microcontroller architecture — special function registers, I/O ports, instruction set, and addressing modes.
- Unit 5: 8051 interfacing — timers, serial communication, interrupts, LCD/keyboard interfacing, ADC/DAC/sensor interfacing, and a comparison against PIC and ARM processors.
Quantum Computing
— an emerging elective introducing the qubit-based computing model, giving students their first formal exposure to Grover’s and Shor’s algorithms.
- Unit 1: Foundations — the history of quantum computing and the conceptual shift from classical bits to qubits and quantum logic operations.
- Unit 2: Background mathematics and physics — linear algebra, Hilbert spaces, superposition, entanglement, and a brief look at genomics/proteomics.
- Unit 3: Qubits and circuits — physical realizations of qubits, the Bloch sphere, single- and multi-qubit gates, and Bell states.
- Unit 4: Quantum algorithms — Deutsch’s and Deutsch-Jozsa algorithms, Shor’s factorization algorithm, and Grover’s search algorithm, compared against classical complexity.
- Unit 5: Error correction and applications — noise and fault-tolerant computation, quantum cryptography, and quantum teleportation.
Open Elective-I
Open Elective-I / Entrepreneurship Development & Venture Creation
— a floating slot where CSE students either take a cross-department open elective (offered outside the CSE syllabus) or, as an alternative, a course on startup fundamentals and venture creation. Because it’s administered outside the CSE-specific syllabus document, there is no CSE unit-wise breakdown to report here.
Labs
Data Mining Lab
— the hands-on counterpart to Data Warehousing & Data Mining, built almost entirely around WEKA and Python.
- Building data warehouses/marts and running OLAP operations (slice, dice, roll-up, drill-down) using tools such as Pentaho or Microsoft SSIS.
- Using WEKA for preprocessing, association rule mining (Apriori, FP-Growth), classification (ID3, J48, Naive Bayes, k-NN), and clustering (k-means, hierarchical, DBSCAN).
- Writing original Python/Java/R programs for chi-square computation, Naive Bayes classification, k-means clustering, dissimilarity matrices, and data visualization with Matplotlib.
Computer Networks Lab
— the protocol-simulation lab that pairs with Computer Networks theory, running from manual framing exercises to live packet capture.
- Implementing data link layer framing (bit/character stuffing), error detection (checksum, Hamming code, CRC), and sliding-window protocols (Go-Back-N, Selective Repeat, Stop-and-Wait).
- Coding routing algorithms — Dijkstra’s shortest path and Distance Vector routing — plus congestion control via the leaky bucket algorithm.
- Hands-on network analysis using Wireshark for packet capture, Nmap for OS detection/scanning, and NS2 simulations for packet loss, TCP/UDP behavior, and throughput comparison.
Full Stack Development-2
— the MERN-stack sequel lab, moving from Express JS backends into React JS front ends and MongoDB persistence.
- Building Express JS applications with routing, middleware, templating, sessions, cookie-based authentication, and RESTful APIs.
- Building React JS interfaces — JSX, function/class components, props/state, conditional rendering, forms, React Router, and hooks.
- Wiring up MongoDB with Mongoose for CRUD operations, collections, indexing, and aggregation, culminating in a to-do list or quiz-app project.
User Interface Design using Flutter
— a mobile UI elective introducing Flutter/Dart for students who want to build cross-platform apps without leaving the CSE syllabus.
- Installing the Flutter/Dart SDK and exploring core widgets (Text, Image, Container) and layout structures (Row, Column, Stack).
- Building responsive, multi-screen UIs with navigation/routing, state management (setState, Provider), and custom themed widgets.
- Implementing animations, form validation, REST API data fetching, and basic unit testing/debugging of Flutter apps.
Community Service Internship
Evaluation of Community Service Internship
— not a taught course but a credit-bearing evaluation of a community service internship completed by the student, carrying 2 credits with no lecture, tutorial, or practical hours and no unit-wise syllabus in the CSE course document.