JNTUK R23 B.Tech CSE III Year I Semester (3-1) Syllabus & Subject-wise Topics

Course Structure

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.