by | Jul 11, 2026 | JNTUK R23 Syllabus
Third year opens with core systems and information-retrieval courses — Computer Networks, Operating Systems, and Information Retrieval Systems — while introducing the branch’s first Professional Elective and Open Elective choice points. Two lab courses reinforce networking and retrieval theory, and a Flutter-based UI design course plus a second full-stack development module build front-end and mobile skills. A community service project internship is evaluated this semester as well.
Subjects
Information Retrieval Systems
- Unit 1: Foundations of information storage and retrieval, including domain analysis of IR systems.
- Unit 2: Inverted files and signature files as core indexing structures.
- Unit 3: Advanced text indices, lexical analysis, and PAT trees/arrays.
- Unit 4: Stemming algorithms and thesaurus construction.
- Unit 5: String-searching algorithms, from naive matching through more efficient pattern-matching techniques.
L-T-P: 3-0-0, 3 credits
Computer Networks
- Unit 1: Network types, topologies, and reference models (OSI/TCP-IP).
- Unit 2: The data link layer — framing techniques and flow control.
- Unit 3: Medium access control, including random-access schemes like ALOHA and CSMA.
- Unit 4: Network layer design issues, packet switching, and routing services.
- Unit 5: The transport layer — protocol services, port numbers, and connection management.
L-T-P: 3-0-0, 3 credits
Operating Systems
- Unit 1: OS overview, functions, and services.
- Unit 2: Process concepts, scheduling, threading, and inter-process communication.
- Unit 3: Synchronization tools (critical sections, mutex locks, semaphores) and deadlock handling.
- Unit 4: Memory-management strategies, paging, and virtual memory.
- Unit 5: File systems — interfaces, implementation, and storage internals.
L-T-P: 3-0-0, 3 credits
Professional Elective-I options:
- Software Engineering — covers the evolution of software development practice, project-management complexities, the software design process, coding and testing strategies (including black-box testing), and CASE tools.
- Cloud Computing — covers cloud fundamentals, enabling technologies like virtualization and distributed computing, containerization, cloud economics and interoperability, and emerging serverless/FaaS models.
- Internet of Things — covers IoT technology and architecture, business models for connected devices, web-connectivity design principles, data acquisition and analytics, and cloud-based IoT data platforms.
- Exploratory Data Analysis with Python — covers EDA fundamentals, visual aids like line/bar/scatter charts, data transformation and merging, descriptive statistics, and end-to-end model development and evaluation.
- Automata Theory & Compiler Design — covers regular languages and finite automata, context-free grammars and pushdown automata, lexical analysis and top-down parsing, bottom-up parsing, and intermediate code generation/optimization.
L-T-P: 3-0-0, 3 credits (one option selected)
Open Elective-I is filled either from the cross-department open elective pool (each engineering branch offers a course, commonly foundational subjects like Operating Systems or Database Management Systems, to other departments’ students) or by choosing the in-house alternative, Entrepreneurship Development & Venture Creation.
Information Retrieval Lab
- Representing text documents in a vector-space model and computing inter-document similarity.
- Text preprocessing — stop-word removal and stemming.
- Building an inverted index over a moderately sized document collection.
- Classifying and clustering text documents, and evaluating results with standard metrics.
- Web crawling, topic-specific PageRank computation, matrix decomposition/LSI, and social-media text mining.
L-T-P: 0-0-3, 1.5 credits
Computer Networks Lab
- Setting up LAN devices and implementing data-link framing methods (character/bit stuffing, checksums).
- Implementing Hamming code and CRC-based error detection.
- Building sliding-window protocols (Go-Back-N, selective repeat) and the stop-and-wait protocol.
- Implementing congestion control (leaky bucket) and routing algorithms (Dijkstra’s, distance vector).
- Using Wireshark and Nmap for packet analysis and scanning, plus NS2-based network simulation.
L-T-P: 0-0-3, 1.5 credits
Full Stack Development-2
(Skill Enhancement Course; alternative: SWAYAM Plus Data Engineer/AI Engineer track)
- Express.js fundamentals — routing, HTTP methods, and middleware.
- Sessions, cookies, authentication, and building RESTful APIs in Express.js.
- React.js fundamentals — components, JSX, props, state, and styling.
- Conditional rendering, list rendering, forms, and client-side routing in React.
- React hooks and inter-component data sharing, paired with MongoDB CRUD operations.
L-T-P: 0-1-2, 2 credits
User Interface Design using Flutter
- Installing the Flutter/Dart toolchain and writing introductory Dart programs.
- Exploring core widgets and layout structures (Row, Column, Stack).
- Designing responsive UIs with media queries and breakpoints.
- Implementing navigation, state management, and custom themed widgets.
- Building forms with validation, adding animations, and consuming REST APIs within the UI.
L-T-P: 0-0-2, 1 credit
The Community Service Project Internship completed over the prior summer is formally evaluated this semester for 2 credits.
Semester total: 15-1-10 contact hours, 23 credits. Students may additionally opt into a Minor specialization course (3-0-3, 4.5 credits), a NPTEL/SWAYAM minor course (3 credits), or Honors-pool courses (3 credits each).
by | Jul 11, 2026 | JNTUK R23 Syllabus
The second half of third year deepens the AI&ML core with Natural Language Processing, Deep Learning, and Data Visualization, alongside two further Professional Elective slots and a second Open Elective. Dedicated labs give hands-on practice in deep learning frameworks and visualization tooling, while Soft Skills and a Technical Paper Writing & IPR audit course build communication and research-documentation ability. A mandatory industry internship or mini-project follows over the summer.
Subjects
Natural Language Processing
- Unit 1: Origins and challenges of NLP, including grammar-based and statistical language modeling.
- Unit 2: Word-level analysis — N-gram models, their evaluation, and smoothing techniques.
- Unit 3: Syntactic analysis using context-free grammars and English grammar rules.
- Unit 4: Semantics and pragmatics — representation requirements and first-order logic approaches.
- Unit 5: Discourse analysis and lexical resource construction.
L-T-P: 3-0-0, 3 credits
Deep Learning
- Unit 1: Biological inspiration for neural computation and the McCulloch-Pitts unit.
- Unit 2: Feedforward networks, gradient descent, and backpropagation.
- Unit 3: Modern optimization methods for more effective neural network training.
- Unit 4: Recurrent neural networks, backpropagation through time, and LSTM units.
- Unit 5: Recent developments — variational autoencoders, transformers, and GPT-style applications across vision and language tasks.
L-T-P: 3-0-0, 3 credits
Data Visualization
- Unit 1: What visualization is, its history, and its relationship to related disciplines.
- Unit 2: Creating visual representations — reference models and visual mapping.
- Unit 3: Classifying visualization systems and interaction techniques, including common pitfalls like misleading charts.
- Unit 4: Visualizing groups, trees, graphs, clusters, and networks.
- Unit 5: Visualizing volumetric data, vector fields, and simulation processes.
L-T-P: 3-0-0, 3 credits
Professional Elective-II options:
- Software Testing Methodologies — covers the purpose of testing, transaction-flow testing, path-based testing techniques, state-graph-based transition testing, and graph-matrix approaches to test design.
- Cryptography & Network Security — covers core security principles, symmetric cryptography’s algebraic foundations, block ciphers (DES, AES, Blowfish), cryptographic hash functions, and transport/web-level security.
- DevOps — covers the SDLC-to-DevOps transition, source-code management with Git, build automation and continuous integration, continuous delivery practices, and configuration management with Ansible.
- Recommender Systems — covers recommender-system fundamentals, collaborative filtering, content-based recommendation, hybrid approaches, and methods for evaluating recommender quality.
L-T-P: 3-0-0, 3 credits (one option selected)
Professional Elective-III options:
- Software Project Management — covers conventional (waterfall) project management, lifecycle phases, model-based software architectures, project organization structures, and Agile/Scrum adoption.
- Mobile Adhoc Networks — covers ad hoc and cellular network fundamentals, MANET routing protocols, ad hoc network security, wireless sensor network basics, and WSN security/key management.
- Computer Vision — covers camera models and radiometry, linear filters and convolution, multi-view geometry and stereopsis, model-fitting segmentation (Hough transform), and geometric camera calibration.
- NoSQL Databases — covers the four major NoSQL database types, comparisons with relational databases, key/value and document stores (MongoDB), column-oriented stores (HBase), and Riak-based key/value databases.
L-T-P: 3-0-0, 3 credits (one option selected)
Open Elective-II is drawn from the cross-department elective pool in the same way as Open Elective-I.
Deep Learning Lab
- Building multi-layer perceptrons for MNIST digit classification.
- Designing networks for binary and multi-class text classification (IMDB, Reuters datasets).
- Predicting housing prices with a regression network on the Boston Housing dataset.
- Building convolutional neural networks for digit and image classification, including transfer learning with VGG16.
- Implementing word embeddings and a recurrent neural network for movie-review sentiment classification.
L-T-P: 0-0-3, 1.5 credits
Data Visualization Lab
- Visualizing datasets with histograms and line charts.
- Building bar charts and box plots across multiple datasets.
- Creating scatter plots, mosaic plots, and multi-variable scatter matrices.
- Producing map-based visualizations and heatmaps.
- Building correlograms and 3D visualizations for multivariate data.
L-T-P: 0-0-3, 1.5 credits
Soft Skills
(Skill Enhancement Course)
- Analytical thinking and listening skills, including self-introduction and structured talks.
- Self-management skills — anger, stress, and time management.
- Standard operating methods for communication — grammar, tenses, and pronunciation.
- Job-oriented skills — group discussions and resume preparation.
- Interpersonal relationships — their importance, types, and influencing factors.
L-T-P: 0-1-2, 2 credits
Technical Paper Writing & IPR
(Audit Course)
- Introduction to technical report writing and sentence construction.
- Drafting reports and handling illustrations/graphics.
- Proofreading and summarization practice.
- Using word-processing tools for structured report elements like tables of contents.
- The nature of intellectual property — patents, designs, trademarks, and copyright.
L-T-P: 2-0-0 (non-credit audit course)
A mandatory industry internship or mini-project of 8 weeks’ duration is undertaken during the following summer vacation.
Semester total: 20-1-8 contact hours, 23 credits, with optional Minor and Honors-pool courses available as in the prior semester.
by | Jul 11, 2026 | JNTUK R23 Syllabus
The first semester of final year centers on Reinforcement Learning as the last major AI/ML core course, paired with Human Resource & Project Management to build workplace-readiness skills. Two Professional Elective slots open onto emerging-technology tracks — from Responsible AI and Blockchain to High Performance Computing and Big Data Analytics — while two Open Electives broaden exposure further. A Prompt Engineering skill course reflects the rise of generative AI tooling, and an evaluated industry internship/mini-project closes out the pre-capstone coursework.
Subjects
Reinforcement Learning
- Unit 1: The reinforcement learning problem and its core elements.
- Unit 2: Multi-armed bandits and action-value methods.
- Unit 3: Finite Markov decision processes and the agent-environment interface.
- Unit 4: Monte Carlo methods for prediction and control.
- Unit 5: Applications and case studies, including TD-Gammon and the Acrobot problem.
L-T-P: 3-0-0, 3 credits
Human Resource & Project Management
(Management Course-II)
- Unit 1: HRM nature, scope, and the functions of an HR manager.
- Unit 2: Human resource development, training models, and HR accounting.
- Unit 3: Project management basics, including resource management and project environment.
- Unit 4: Project types and the unique management challenges each presents.
- Unit 5: Project implementation and review, including organizational forms and planning.
L-T-P: 2-0-0, 2 credits
Professional Elective-IV options:
- Responsible AI — covers an overview of AI and its common risks, fairness and bias types, explainability and interpretability techniques, safety/security/privacy concerns, and real-world case studies of AI failures.
- Blockchain Technology — covers blockchain fundamentals and its borrowed technologies, consensus mechanisms like Proof of Work, Ethereum and the Ethereum Virtual Machine, and enterprise blockchain via Hyperledger Fabric.
- Quantum Computing — covers the historical and mathematical foundations of quantum computing, qubits and their physical implementations, quantum algorithms, and noise/error correction.
- Robotic Process Automation — covers RPA scope and techniques, record-and-play automation with UiPath, data manipulation and control logic, exception handling in assistant bots, and bot deployment/maintenance.
L-T-P: 3-0-0, 3 credits (one option selected)
Professional Elective-V options:
- Agile Methodologies — covers agile management theory, agile processes like Scrum and Feature-Driven Development, agile knowledge sharing via story cards, agility’s impact on requirements engineering, and agile quality assurance.
- Augmented Reality & Virtual Reality — covers AR fundamentals and computer-vision-based tracking, VR fundamentals and history, the physiology of human vision, and motion perception in real and virtual environments.
- High Performance Computing — covers the motivation and scope of parallelism, parallel algorithm design principles, basic communication operations, analytical performance models, and parallel sorting/graph algorithms.
- Big Data Analytics — covers Java data structures for big data work, foundational big-data infrastructure (GFS, HDFS), MapReduce programming, stream processing with Spark, and Pig for simplified Hadoop programming.
L-T-P: 3-0-0, 3 credits (one option selected)
Open Elective-III and Open Elective-IV are drawn from the cross-department elective pool, following the same structure as earlier open electives.
Prompt Engineering
(Skill Enhancement Course; alternative: SWAYAM Plus certificate in Prompt Engineering and ChatGPT)
- Unit 1: Foundations of prompt engineering and how it differs from traditional programming.
- Unit 2: Advanced prompt patterns and techniques, including enhanced prompt anatomy and contextual detail.
- Unit 3: Structured outputs and reasoning techniques for reliable LLM responses.
- Unit 4: Retrieval-augmented generation and LangChain-based workflows.
- Unit 5: LLM agents, multimodal AI, and ethical evaluation of generative systems.
L-T-P: 0-1-2, 2 credits
Constitution of India
(Audit Course)
- Unit 1: The history and drafting of the Indian Constitution.
- Unit 2: Fundamental rights and duties, including the right to equality.
- Unit 3: The organs of governance, including Parliament’s composition and functions.
- Unit 4: Local administration and district-level governance.
- Unit 5: The Election Commission’s role and functioning.
L-T-P: 2-0-0 (non-credit audit course)
The Industry Internship/Mini-Project undertaken over the summer is evaluated this semester for 2 credits.
Students may optionally pursue Honors-pool electives such as Agentic AI (covering agentic intelligence foundations, decision-making and planning, LLM-driven agent behaviour, agent frameworks and system design, and responsible applied agentic AI) or Adversarial Machine Learning (covering ML/deep-learning foundations, adversarial attack techniques, defense mechanisms and robust training, privacy/backdoor threats, and advanced topics including GAN-based attacks), alongside a Minor-pool course from the same specialization track chosen in earlier semesters.
Semester total: 19-1-2 contact hours, 21 credits.
by | Jul 11, 2026 | JNTUK R23 Syllabus
The final semester is dedicated entirely to a full-time internship or project work, giving students a capstone opportunity to apply the AI&ML curriculum — from data structures and machine learning through deep learning, NLP, and reinforcement learning — to a substantial real-world or industry-aligned problem. No classroom coursework runs alongside it; the semester’s credit weight is concentrated in supervised project execution and evaluation.
Subjects
Full-Semester Internship / Project Work
- Problem identification and scoping in consultation with an academic or industry supervisor.
- Literature or technology survey relevant to the chosen problem domain.
- System design and iterative implementation, drawing on the programming, ML, and systems foundations built across earlier semesters.
- Testing, evaluation, and refinement of the resulting system or research outcome.
- Documentation of the work and a final viva-voce/project defense.
L-T-P: 0-0-24, 12 credits
Per R23 regulations, students must complete at least one MOOC course (3 of the 160 total programme credits) by this point in the degree if not already fulfilled earlier. Students who opted into the Honors track may also complete their second Honors-pool course here, drawing from options such as Agentic AI or Adversarial Machine Learning introduced in the prior semester.
Semester total: 0-0-24 contact hours, 12 credits — the smallest contact-hour load of the programme, reflecting its fully project-based structure.
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
by | Jul 11, 2026 | JNTUK R23 Syllabus
This semester layers statistical and data-engineering foundations onto the programming base built earlier, introducing DBMS, computer organization, and a dedicated data-engineering track alongside its lab. A design-thinking course and an optimization-techniques course broaden the student’s problem-solving toolkit, and the term closes with a mandatory eight-week community-service internship over the summer break.
Semester load: roughly 15 lecture, 1 tutorial and 10 practical hours per week, totaling 21 credits.
Subjects
Optimization Techniques
- Unit 1: Formulating optimization problems and classical unconstrained/constrained techniques (Lagrange multipliers, Kuhn-Tucker)
- Unit 2: Linear programming and the simplex method
- Unit 3: Transportation problems and feasibility methods
- Unit 4: Nonlinear programming, one-dimensional search methods, and penalty-function approaches
- Unit 5: Dynamic programming and multistage decision processes
Statistical Methods for Data Science
- Unit 1: Exploratory data visualization techniques and common probability distributions
- Unit 2: Parametric estimation and hypothesis testing
- Unit 3: Linear and multiple regression, including categorical predictors
- Unit 4: Time-series analysis — trend, seasonality, and smoothing methods
- Unit 5: Logistic regression for classification problems
Data Engineering
- Unit 1: What data engineering involves and how the role differs from data science
- Unit 2: The data engineering life cycle — generation, storage, ingestion, transformation, and serving
- Unit 3: Principles of sound data architecture and how source systems generate data
- Unit 4: Storage systems (warehouses, lakes, lakehouses) and ingestion strategies
- Unit 5: Query optimization, data modeling, and streaming transformations
Database Management Systems
- Unit 1: Database fundamentals, schema architecture, and entity-relationship modeling
- Unit 2: The relational model, relational algebra, and basic SQL
- Unit 3: Intermediate SQL — joins, subqueries, grouping, and views
- Unit 4: Normalization theory from first through fifth normal form
- Unit 5: Transaction management, concurrency control, recovery, and indexing structures
Computer Organization and Architecture
- Unit 1: Number systems, data representation, and Boolean logic minimization
- Unit 2: Combinational and sequential digital circuits
- Unit 3: Computer arithmetic, register-transfer operations, and the stored-program model
- Unit 4: Microprogrammed control and CPU instruction/addressing design
- Unit 5: Memory hierarchy and input-output organization
Data Engineering Lab
- Setting up pipeline tooling such as Apache NiFi, Airflow, Elasticsearch, and PostgreSQL
- Reading, writing, and transforming data across files and databases
- Building, versioning, and monitoring data pipelines
- Deploying a pipeline into a production-like environment
- Total: 3 practical hours per week, 1.5 credits
DBMS Lab
- DDL/DML/DCL exercises, nested queries, and aggregate functions
- PL/SQL programming — control structures, procedures, functions, cursors, and triggers
- Indexing exercises and JDBC-based database connectivity from Java
- Total: 3 practical hours per week, 1.5 credits
Exploratory Data Analysis with Python (Skill Enhancement Course)
- Unit 1: EDA fundamentals and getting comfortable with common Python libraries
- Unit 2: Visual analysis techniques — line, bar, scatter, and other chart types
- Unit 3: Data transformation — merging, reshaping, and handling missing values
- Unit 4: Descriptive statistics, distribution types, and correlation analysis
- Unit 5: A basic model-development and evaluation workflow
- Total: 1 tutorial and 2 practical hours per week, 2 credits
Design Thinking & Innovation
- Unit 1: Elements and principles of design, and the history of design thinking
- Unit 2: The design thinking process — empathize, analyze, ideate, and prototype
- Unit 3: Innovation versus creativity, and building teams around innovation
- Unit 4: Product design strategy, specification, and planning
- Unit 5: Applying design thinking to business strategy and startups
Note: this semester is paired with a mandatory eight-week Community Service Project Internship during the summer vacation.
