JNTUK B.Tech · R23 course register

Data ScienceSemester 3-1 syllabus

Review the course structure, credits and unit-wise topics for third year, semester one.

Regulation: R23
Semester: 3-1
Coverage: 12 subject sections

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Institution: JNTUK
Regulation: R23
Branch: Data Science
Links checked: 12 July 2026

Open CSE department syllabus Read R23 regulations

Autonomous colleges may publish approved local course structures. If your college document differs, follow the document issued by your college or JNTUK.

The third year opens with the program’s core machine-learning and networking courses alongside software engineering, plus the student’s first professional elective — a choice among automata/compiler theory, object-oriented analysis and design, soft computing, or IoT. A skill-enhancement full-stack web development course and a Flutter-based UI tinkering lab add practical breadth, and the community-service internship from the prior year is formally evaluated in this term.

Semester load: roughly 15 lecture, 1 tutorial and 10 practical hours per week, totaling 23 credits.

Subjects

Machine Learning

Unit 1: Machine-learning paradigms, the modeling pipeline, and dataset considerations
Unit 2: Proximity-based models such as k-nearest neighbors
Unit 3: Decision-tree and Bayes-rule-based classifiers
Unit 4: Linear discriminants — perceptrons, SVMs, logistic regression, and multi-layer perceptrons
Unit 5: Clustering approaches, including k-means, fuzzy c-means, and spectral clustering

Computer Networks

Unit 1: Network types, topologies, and the OSI/TCP-IP reference models
Unit 2: Data-link layer framing, error control, and sliding-window protocols
Unit 3: Media access control schemes and Ethernet standards
Unit 4: Network-layer routing algorithms, congestion control, and IPv4/IPv6
Unit 5: Transport-layer protocols (UDP/TCP) and application-layer services like HTTP, email, and DNS

Software Engineering

Unit 1: Software life-cycle models from waterfall through agile and spiral approaches
Unit 2: Project management, effort estimation, and requirements specification
Unit 3: Software design principles, agile practices, and user-interface design
Unit 4: Coding practices, testing strategies, and software quality standards
Unit 5: CASE tools, software maintenance, and software reuse

Professional Elective-I options:

students choose one of Automata Theory & Compiler Design, Object Oriented Analysis and Design, Soft Computing, Internet of Things, or an approved NPTEL/SWAYAM course.

Automata Theory & Compiler Design

Unit 1: Regular expressions, finite automata, and their equivalence
Unit 2: Context-free grammars and pushdown automata
Unit 3: Lexical analysis and top-down parsing
Unit 4: Bottom-up parsing and syntax-directed translation
Unit 5: Intermediate code generation and code optimization

Object Oriented Analysis and Design

Unit 1: Managing complexity in large software systems
Unit 2: UML fundamentals and structural modeling
Unit 3: Class/object diagrams and advanced structural constructs
Unit 4: Behavioral modeling — use cases, interactions, and activity diagrams
Unit 5: Advanced behavioral and architectural modeling (state charts, components, deployment)

Soft Computing

Unit 1: Neural network basics and biological inspiration
Unit 2: Perceptron learning and backpropagation networks
Unit 3: Fuzzy sets, relations, and membership functions
Unit 4: Fuzzy inference systems and neuro-fuzzy hybrids
Unit 5: Genetic algorithms and genetic-fuzzy hybrid systems

Internet of Things

Unit 1: IoT overview, M2M communication, and connectivity principles
Unit 2: Business models, layered IoT architectures, and standardization
Unit 3: Web connectivity protocols for connected devices
Unit 4: Data acquisition, organization, and business-process integration
Unit 5: Cloud-based storage and computing for IoT, plus sensing/RFID technology

Machine Learning Lab

Central-tendency and dispersion computations, and preprocessing techniques
Implementing KNN, decision tree, and random forest classifiers
Naïve Bayes, SVM, and multi-layer perceptron classification exercises
Regression algorithms and clustering (k-means and related methods)
Total: 3 practical hours per week, 1.5 credits

Computer Networks Lab

Framing, checksum, and error-correction coding exercises
Sliding-window and stop-and-wait protocol simulations
Routing algorithm implementation (Dijkstra, distance-vector)
Packet analysis with Wireshark and network scanning with Nmap
NS2-based simulation of packet loss, congestion, and throughput
Total: 3 practical hours per week, 1.5 credits

Full Stack Development-1 (Skill Enhancement Course)

HTML structuring — lists, links, images, tables, forms, and frames
CSS styling, selector types, and the box model
JavaScript fundamentals — I/O, conditional logic, loops, and built-in/user-defined objects
Functions, event handling, and form validation
An introduction to Node.js
Total: 1 tutorial and 2 practical hours per week, 2 credits

User Interface Design using Flutter (Tinkering Lab)

Dart language basics and Flutter widget exploration
Layout composition using Row, Column, and Stack widgets
Responsive design and navigation between screens
State management and custom widget/theme styling
Form validation, animation, and REST API data fetching
Total: 2 practical hours per week, 1 credit

Note: the Community Service Project Internship completed the previous summer is formally evaluated this semester, and students may alternatively take Entrepreneurship Development & Venture Creation in place of Open Elective-I.