by | Jul 11, 2026 | JNTUK R23 Syllabus
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.
by | Jul 11, 2026 | JNTUK R23 Syllabus
The second half of third year shifts focus to deep learning, operating systems, and data visualization as core subjects, with two elective slots covering topics from cryptography and cloud computing to computer vision and NoSQL databases. A soft-skills course and a technical-writing/IPR audit course prepare students for placements and research communication, alongside a mandatory summer industry internship or mini-project.
Semester load: roughly 20 lecture, 1 tutorial and 8 practical hours per week, totaling 23 credits.
Subjects
Deep Learning
- Unit 1: Biological neuron models and the perceptron learning algorithm
- Unit 2: Multilayer perceptrons, backpropagation, and regularization
- Unit 3: Modern optimizers (Adam, RMSprop, etc.) and training stabilization techniques
- Unit 4: Recurrent networks (LSTM, GRU) and convolutional architectures
- Unit 5: Recent developments — variational autoencoders and transformer models
Operating Systems
- Unit 1: OS services, system calls, and overall system structure
- Unit 2: Process management, threading models, and CPU scheduling
- Unit 3: Synchronization primitives and deadlock handling
- Unit 4: Memory management strategies and virtual memory
- Unit 5: File systems and protection mechanisms
Data Visualization
- Unit 1: The visualization process and principles of visual perception
- Unit 2: Building visual representations and visual-analytics workflows
- Unit 3: Classifying visualization systems and handling multi-dimensional/text data
- Unit 4: Visualizing hierarchical and network structures
- Unit 5: Visualizing volumetric, geographic, and collaborative data; evaluating visualizations
Professional Elective-II options:
Social Media Analytics, Cryptography & Network Security, Recommender Systems, Cloud Computing, or Sensor Networks.
Social Media Analytics
- Unit 1: Evolution of the web and characteristics of social media platforms
- Unit 2: The seven-layer social media analytics framework
- Unit 3: Text analytics techniques applied to social content
- Unit 4: Action analytics on social platforms
- Unit 5: Hyperlink analytics and viral-content detection
Cryptography & Network Security
- Unit 1: Core security concepts and classical encryption techniques
- Unit 2: Mathematical foundations of symmetric and asymmetric cryptography
- Unit 3: Block and stream ciphers, plus public-key algorithms (RSA, Diffie-Hellman, elliptic curve)
- Unit 4: Hash functions, message authentication, and digital signatures
- Unit 5: Transport, IP, and email security protocols
Recommender Systems
- Unit 1: Recommender system fundamentals and rating data
- Unit 2: Collaborative filtering approaches
- Unit 3: Content-based and knowledge-based recommendation
- Unit 4: Hybrid recommendation strategies
- Unit 5: Evaluation methods and the role of community/trust signals
Cloud Computing
- Unit 1: Cloud service and deployment models (IaaS/PaaS/SaaS, public/private/hybrid)
- Unit 2: Distributed computing foundations and service-oriented architecture
- Unit 3: Virtualization and container technologies (Docker, Kubernetes)
- Unit 4: Cloud economics, interoperability, and security challenges
- Unit 5: Serverless computing and cloud-centric IoT/edge computing
Sensor Networks
- Unit 1: Wireless network types and an introduction to sensor networks
- Unit 2: Single-node hardware architecture and network scenarios
- Unit 3: MAC and routing protocols for sensor networks
- Unit 4: Topology control, time synchronization, and localization
- Unit 5: Sensor node platforms, operating systems, and simulation tools
Professional Elective-III options:
Software Project Management, Quantum Computing, Computer Vision, NoSQL Databases, or an approved NPTEL/SWAYAM course.
Software Project Management
- Unit 1: Conventional software management and software economics
- Unit 2: Project life-cycle phases from inception through transition
- Unit 3–5: Project planning, tracking, organizational structures, and the tools used to manage schedule, cost, and resources
Quantum Computing
- Unit 1: Origins of quantum computing and qubits versus classical bits
- Unit 2: Underlying linear algebra and quantum-mechanical principles
- Unit 3: Qubit representation and quantum circuit design
- Unit 4: Core quantum algorithms (Deutsch-Jozsa, Shor, Grover)
- Unit 5: Quantum error correction and quantum cryptography
Computer Vision
- Unit 1: Camera models, radiometry, and shading
- Unit 2: Linear filtering, edge detection, and texture analysis
- Unit 3: Multi-view geometry and image segmentation
- Unit 4: Model fitting and motion tracking
- Unit 5: Geometric camera calibration and model-based vision
NoSQL Databases
- Unit 1: History and categories of NoSQL databases
- Unit 2: Comparing relational and NoSQL data models, replication, and sharding
- Unit 3: Document databases (MongoDB) and their use cases
- Unit 4: Column-family stores (HBase, Cassandra)
- Unit 5: Key-value and graph databases (Riak, Neo4j)
Deep Learning Lab
- Multi-layer perceptron and CNN implementations for image classification
- Text-classification exercises using embeddings and RNNs
- Transfer learning with pre-trained models such as VGG16
- Total: 3 practical hours per week, 1.5 credits
Data Visualization Lab
- Histogram, line-chart, and bar-chart exercises in R
- Box plots, scatter plots, and mosaic plots across sample datasets
- Heatmaps, geographic map visualizations, and 3D graphing
- Total: 3 practical hours per week, 1.5 credits
Soft Skills (Skill Enhancement Course)
- Unit 1: Analytical thinking, listening, and communication skills
- Unit 2: Self-management — time, stress, and anger management, plus workplace etiquette
- Unit 3: Grammar, correspondence, and professional writing
- Unit 4: Group discussions, resumes, and interview preparation
- Unit 5: Interpersonal relationships in professional settings
- Total: 1 tutorial and 2 practical hours per week, 2 credits
Technical Paper Writing & IPR (Audit Course)
- Unit 1: Fundamentals of technical report writing
- Unit 2: Drafting, editing, and plain-English writing conventions
- Unit 3: Proofreading and presenting final reports
- Unit 4: Word-processor techniques for formatting long documents
- Unit 5: Intellectual property fundamentals — patents, copyright, and the patenting process
- Ungraded audit course; no credits attached
Note: this semester is paired with a mandatory eight-week Industry Internship or Mini Project during the summer vacation.
by | Jul 11, 2026 | JNTUK R23 Syllabus
The final taught semester combines Big Data Analytics as the last major core subject with a human-resources/project-management course, two professional elective slots spanning topics like blockchain, DevOps, NLP, agile methods, and high-performance computing, and two open electives taken from other departments. A second full-stack development course and a Constitution of India audit course round out the term, alongside evaluation of the prior summer’s industry internship or mini-project.
Semester load: roughly 19 lecture, 1 tutorial and 2 practical hours per week, totaling 21 credits.
Subjects
Big Data Analytics
- Unit 1: Java data structures and generics needed for MapReduce-style programming
- Unit 2: Hadoop Distributed File System architecture and cluster configuration
- Unit 3: Writing MapReduce programs — mappers, reducers, and combiners
- Unit 4: Stream processing concepts and Spark’s RDD-based architecture
- Unit 5: Pig and Hive for higher-level querying over Hadoop data
Human Resources & Project Management (Management Course-II)
- Unit 1: HRM functions, planning, recruitment, and selection
- Unit 2: Training, performance appraisal, and career development
- Unit 3: Project management basics, resource planning, and project life cycle
- Unit 4: Managing different project types and their unique challenges
- Unit 5: Project implementation, control, and post-project review
Professional Elective-IV options:
Software Architecture & Design Patterns, Blockchain Technology, DevOps, Natural Language Processing, or an approved NPTEL/SWAYAM course.
Software Architecture & Design Patterns
- Unit 1: What design patterns are and how object-oriented design approaches them
- Unit 2: Systems analysis — gathering and structuring requirements
- Unit 3: The structural design-pattern catalog (adapter, bridge, composite, decorator, etc.)
- Unit 4: The MVC architectural pattern in practice
- Unit 5: Distributed-object design, including web services
Blockchain Technology
- Unit 1: Origins of Bitcoin, blockchain fundamentals, and cryptographic building blocks
- Unit 2: Underlying technologies — hash pointers, wallets, mining, and double-spending
- Unit 3: Consensus mechanisms — proof of work, proof of stake, and hybrid models
- Unit 4: Ethereum, smart contracts, and Solidity
- Unit 5: Hyperledger Fabric and broader blockchain applications beyond cryptocurrency
DevOps
- Unit 1: DevOps lifecycle, workflows, and CI/CD automation concepts
- Unit 2: Source-code management with Git, plus unit-testing and code-coverage tools
- Unit 3: Continuous integration with Jenkins
- Unit 4: Continuous delivery and containerization with Docker
- Unit 5: Configuration management with Ansible and container orchestration with Kubernetes
Natural Language Processing
- Unit 1: Language modeling basics, morphology, and tokenization
- Unit 2: N-grams, part-of-speech tagging, and statistical language models
- Unit 3: Syntactic parsing and context-free grammars
- Unit 4: Semantics, word-sense disambiguation, and pragmatics
- Unit 5: Discourse analysis, coreference resolution, and standard NLP lexical resources
Professional Elective-V options:
Agile Methodologies, Expert Systems, Reinforcement Learning, High Performance Computing, or an approved NPTEL/SWAYAM course.
Agile Methodologies
- Unit 1: Agile theory, the manifesto, and agile project management
- Unit 2: Agile process families — Scrum, Crystal, XP, and feature-driven development
- Unit 3: Knowledge-sharing practices such as story cards
- Unit 4: Requirements engineering in agile environments
- Unit 5: Agile metrics, quality assurance, and test-driven development
Expert Systems
- Unit 1: AI search strategies and game-playing algorithms
- Unit 2: Knowledge representation — predicate logic, semantic nets, and rule-based systems
- Unit 3: Expert system architecture and problem types
- Unit 4: Expert-system development tools and knowledge engineering
- Unit 5: Building an expert system and common pitfalls in practice
Reinforcement Learning
- Unit 1: Core reinforcement-learning concepts and terminology
- Unit 2: The multi-armed bandit problem and action-value methods
- Unit 3: Finite Markov decision processes and value functions
- Unit 4: Monte Carlo prediction and control methods
- Unit 5: Applied case studies such as TD-Gammon and job-shop scheduling
High Performance Computing
- Unit 1: Motivations for parallelism and parallel programming platforms
- Unit 2–5: Parallel algorithm design, interconnection networks, performance analysis, and techniques for parallelizing computational tasks
Open Elective-III and Open Elective-IV:
cross-department electives; Data Science students typically draw from subjects such as Operating Systems, Computer Networks, Software Engineering, or IoT Based Smart Systems as offered.
Full Stack Development-2 (Skill Enhancement Course)
- ExpressJS routing, middleware, sessions, and RESTful API design
- ReactJS components, props/state, conditional rendering, and hooks
- MongoDB installation, CRUD operations, and aggregation queries
- A capstone build such as a to-do list or quiz application
- Total: 1 tutorial and 2 practical hours per week, 2 credits
Constitution of India (Audit Course)
- Unit 1: History and drafting of the Indian Constitution
- Unit 2: Fundamental rights, directive principles, and fundamental duties
- Unit 3: Structure of the legislature, executive, and judiciary
- Unit 4: Local self-government — municipalities and panchayati raj institutions
- Unit 5: The Election Commission and welfare bodies for marginalized groups
- Ungraded audit course; no credits attached
Note: this semester includes evaluation of the Industry Internship or Mini Project completed the previous summer.
by | Jul 11, 2026 | JNTUK R23 Syllabus
The final semester is dedicated entirely to a full-time internship or project placement, giving students a capstone opportunity to apply their data science training in an industry or research setting under faculty and/or organizational supervision. Students must also complete at least one MOOC course during their program to satisfy the university’s mandatory-credit requirement.
Subjects
Full Semester Internship / Project Work
- A single continuous engagement spanning the entire semester, assessed through progress reviews, a final report, and a viva-style evaluation
- Expected to reflect substantial applied work in a data-science-relevant domain (industry placement, research project, or product build)
- Total: 24 practical hours per week, 12 credits — the entire semester’s credit load
Note: JNTUK’s R23 regulations require every student to complete at least one MOOC (3-credit-equivalent) course across the program; this is typically fulfilled by the end of this semester. Minor-in-Data-Science and Honors tracks (e.g., Agentic AI, Quantum Machine Learning, Real-Time Data Processing) run as optional add-on credit pools alongside the core curriculum for students who opt in earlier in the program.
by | Jul 11, 2026 | JNTUK R23 Syllabus
The opening semester of the Data Science branch balances foundational mathematics and programming with the student’s first dedicated data-science course. Students move from discrete math and human-values training into Java, advanced data structures, and Python, while a lab-heavy skill track builds hands-on coding fluency early. An audit course in environmental science and a required summer community-service internship round out the term.
Semester load: roughly 15 lecture, 2 tutorial and 10 practical hours per week, totaling 20 credits, plus the non-credited audit course.
Subjects
Discrete Mathematics and Graph Theory
- Unit 1: Propositional and predicate logic — statement forms, truth tables, equivalence, and inference rules
- Unit 2: Set theory, relations, functions, and lattice structures
- Unit 3: Counting principles, permutations/combinations, and recurrence relations solved via generating functions
- Unit 4: Graph fundamentals — representations, isomorphism, and Eulerian/Hamiltonian paths
- Unit 5: Multigraphs, planarity, graph coloring, and spanning-tree algorithms (Prim, Kruskal, BFS/DFS)
Universal Human Values — Understanding Harmony and Ethical Human Conduct
- Unit 1: Foundations of value education and the case for self-exploration
- Unit 2: Harmony within the individual — distinguishing the self from the body
- Unit 3: Harmony in family and society, built around trust and mutual respect
- Unit 4: Harmony with nature and the wider order of existence
- Unit 5: Applying holistic understanding to professional ethics and organizational life
- Delivered as a mix of lecture sessions and practice-based tutorials with reflective exercises
Introduction to Data Science
- Unit 1: What data science is, the end-to-end process, and its place within the big-data ecosystem
- Unit 2: Where machine learning fits into the data science pipeline, common Python tooling, and strategies for large datasets
- Unit 3: NoSQL and distributed storage concepts, including Hadoop, the CAP theorem, and illustrative case studies
- Unit 4: Graph databases (Neo4j/Cypher) and text-mining libraries for unstructured data
- Unit 5: Visualization techniques and building an interactive dashboard as a capstone case study
Advanced Data Structures & Algorithm Analysis
- Unit 1: Complexity analysis fundamentals, AVL trees, and B-trees
- Unit 2: Heaps, graph traversal, and divide-and-conquer algorithms
- Unit 3: Greedy strategies and dynamic programming
- Unit 4: Backtracking and branch-and-bound techniques
- Unit 5: NP-hard and NP-complete problem classes
Object-Oriented Programming through Java
- Unit 1: Java program structure, data types, operators, and control flow
- Unit 2: Classes, objects, constructors, and method design
- Unit 3: Arrays and inheritance mechanics
- Unit 4: Packages, the Java standard library, and exception handling
- Unit 5: String handling, multithreading, JDBC connectivity, and building simple JavaFX interfaces
Data Science Lab
- Practical work on NumPy array creation, reshaping, and slicing
- Pandas-based data wrangling: building dataframes, handling missing values, reading multiple file formats
- Web scraping and preprocessing techniques such as scaling, standardization, and encoding
- Visualization exercises using Matplotlib
- Introductory exposure to NLTK and scikit-learn for text and predictive tasks
- Total: 3 practical hours per week, 1.5 credits
Object-Oriented Programming through Java Lab
- Core language exercises: primitive types, control structures, and basic algorithms (searching, sorting)
- Class design, constructors, method and constructor overloading
- Single and multilevel inheritance, abstract classes, and interfaces
- Exception handling, both built-in and custom
- Multithreading exercises and producer-consumer style problems
- JavaFX GUI building and JDBC database connectivity
- Total: 3 practical hours per week, 1.5 credits
Python Programming (Skill Enhancement Course)
- Unit 1: Language basics, control flow, and the Jupyter/Anaconda environment
- Unit 2: Functions, string handling, and list operations
- Unit 3: Dictionaries, tuples, and sets
- Unit 4: File handling and object-oriented programming in Python
- Unit 5: An introduction to data-science-oriented Python — JSON handling, NumPy, and Pandas
- Paired with weekly lab exercises that reinforce each unit’s concepts
- Total: 1 tutorial and 2 practical hours per week, 2 credits
Environmental Science (Audit Course)
- Unit 1: Multidisciplinary scope of environmental studies and natural resource use
- Unit 2: Ecosystem structure, biodiversity, and conservation
- Unit 3: Pollution types, causes, and solid-waste management
- Unit 4: Sustainable development, environmental law, and disaster-related social issues
- Unit 5: Population growth, human health, and field-based environmental observation
- Ungraded audit course; no credits attached
