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.
by | Jul 11, 2026 | JNTUK R23 Syllabus
| # | Category | Subject | L-T-P | Credits |
|---|
| 1 | Professional Core | Advanced Java | 3-0-0 | 3 |
| 2 | Professional Core | Computer Networks | 3-0-0 | 3 |
| 3 | Professional Core | Automata Theory & Compiler Design | 3-0-0 | 3 |
| 4 | Professional Elective-I | Object Oriented Analysis and Design / Cyber Security / Artificial Intelligence / Microprocessors & Microcontrollers / Data Warehousing & Data Mining / 12-week MOOC (SWAYAM/NPTEL) | 3-0-0 | 3 |
| 5 | Open Elective-I OR | Entrepreneurship Development & Venture Creation | 3-0-0 | 3 |
| 6 | Professional Core | Advanced Java Lab | 0-0-3 | 1.5 |
| 7 | Professional Core | Computer Networks Lab | 0-0-3 | 1.5 |
| 8 | Skill Enhancement Course | Full Stack Development-1 | 0-1-2 | 2 |
| 9 | Engineering Science | User Interface Design using Flutter / SWAYAM Plus – Android App Development (with Flutter) | 0-0-2 | 1 |
| 10 | — | Evaluation of Community Service Internship | – | 2 |
| | Total | 15-1-10 | 23 |
| MC | Minor Course (from the specialized minors pool) | 3-0-3 | 4.5 |
| MC | Minor Course through SWAYAM/NPTEL (12-week, 3-credit) | 3-0-0 | 3 |
| HC | Honors Course (from the honors pool) | 3-0-0 | 3 |
| HC | Honors Course (from the honors pool) | 3-0-0 | 3 |
The Minor/Honors rows are optional 18-credit add-on tracks, not part of the core 23-credit semester load. The document’s “Minor in IT” pool draws on subjects already covered elsewhere in this file (Principles of Database Management Systems, Principles of Software Engineering, Advanced Data Structures & Algorithm Analysis, Principles of Operating Systems) plus a set of NPTEL MOOCs, so no separate unit-wise content exists for the minor slot itself. The “Evaluation of Community Service Internship” row is a credit-bearing evaluation of the II-II summer internship and has no unit-wise syllabus in this document.
Advanced Java
extends core Java into enterprise web development, covering JDBC, servlets, JSP, and the Spring framework so students can build database-backed, server-side web applications.
- Unit 1: JDBC programming — JDBC architecture, statement types, batch updates, and transaction management
- Unit 2: J2EE and web development — J2EE architecture, containers, and HTTP request processing
- Unit 3: Servlet API — servlet lifecycle, session tracking, and filter API
- Unit 4: JavaServer Pages — JSP lifecycle, scripting elements, JSTL, and exception handling
- Unit 5: Java web frameworks — Spring MVC, dependency injection, and Spring DAO/database transactions
Computer Networks
covers the layered network stack from physical media through data link, MAC, network, and transport layers, giving students the protocol-level understanding needed for network design, security, and troubleshooting.
- Unit 1: Network fundamentals — topologies, the OSI and TCP/IP reference models, and physical media
- Unit 2: Data link layer — framing, error detection/correction, and sliding window protocols
- Unit 3: Media access control — ALOHA, CSMA variants, and Ethernet standards
- Unit 4: Network layer — routing algorithms, congestion control, and IPv4/IPv6 addressing
- Unit 5: Transport and application layers — UDP/TCP services, HTTP, email, and DNS
Automata Theory & Compiler Design
pairs formal-language theory (finite automata, grammars) with the practical stages of building a compiler (lexical analysis, parsing, code generation), showing how theoretical computation models translate into real language-processing tools.
- Unit 1: Regular expressions and finite automata — DFA/NFA construction, minimization, and equivalence with regular expressions
- Unit 2: Context-free grammars and pushdown automata — CFG design, ambiguity, and PDA-CFG equivalence
- Unit 3: Lexical analysis and top-down parsing — token recognition, LEX, and recursive-descent/LL(1) parsing
- Unit 4: Bottom-up parsing — shift-reduce, LR/LALR parsing, and syntax-directed translation
- Unit 5: Code generation and optimization — three-address code, type checking, and peephole optimization
Object Oriented Analysis and Design
(Professional Elective-I) — teaches UML-based modeling and object-oriented design principles for translating real-world problem domains into structured software architectures.
- Unit 1: Complex systems — structure and organization of complex software systems
- Unit 2: UML fundamentals — object-oriented modeling concepts and basic structural diagrams
- Unit 3: Class and object diagrams — advanced structural modeling, interfaces, and packages
- Unit 4: Basic behavioral modeling — interaction diagrams, use cases, and activity diagrams
- Unit 5: Advanced behavioral and architectural modeling — state charts, component, and deployment diagrams
Cyber Security
(Professional Elective-I) — surveys cybercrime, attack techniques, and digital forensics, giving students the investigative and legal grounding to identify, respond to, and analyze security incidents.
- Unit 1: Introduction to cybercrime — cybercriminal classifications, mobile device threats, and botnets
- Unit 2: Tools and methods of attack — phishing, keyloggers, spoofing, DoS/DDoS, and SQL injection
- Unit 3: Cybercrime investigation — digital evidence collection, email tracking, and password recovery
- Unit 4: Computer forensics — forensic tools, biometric recognition, and OS-specific forensics
- Unit 5: Legal perspectives — the Indian IT Act, digital signatures, and cybercrime law
Artificial Intelligence
(Professional Elective-I) — introduces intelligent-agent design, search-based problem solving, knowledge representation, and expert systems as the foundation for later machine-learning and deep-learning coursework.
- Unit 1: Introduction — AI problems, intelligent agents, and problem formulation
- Unit 2: Searching — uninformed and heuristic search, game-playing, and alpha-beta pruning
- Unit 3: Knowledge representation — predicate logic, semantic nets, and probabilistic reasoning
- Unit 4: Logic and learning — first-order logic inference, inductive learning, and reinforcement learning
- Unit 5: Expert systems — architecture, knowledge acquisition, and case studies like MYCIN and DART
Microprocessors & Microcontrollers
(Professional Elective-I) — covers 8086 microprocessor and 8051 microcontroller architecture, programming, and interfacing, connecting the digital-logic course to real embedded hardware design.
- Unit 1: 8086 architecture — internal architecture, bus interfacing, and interrupts
- Unit 2: 8086 programming — instructions, addressing modes, and assembler directives
- Unit 3: 8086 interfacing — memory interfacing, 8255 PPI, and DMA controllers
- Unit 4: 8051 microcontroller architecture — special function registers, I/O ports, and instruction set
- Unit 5: 8051 interfacing — timers, serial ports, LCD/keyboard interfacing, and ADC/DAC
Data Warehousing & Data Mining
(Professional Elective-I) — covers building data warehouses and mining patterns from large datasets, bridging database systems with the analytics and machine-learning tracks later in the program.
- Unit 1: Data warehousing and OLAP — data cube modeling, warehouse design, and data preprocessing basics
- Unit 2: Data preprocessing — data cleaning, integration, reduction, and transformation
- Unit 3: Classification — decision tree induction and Bayesian classification methods
- Unit 4: Association analysis — frequent itemset generation and the Apriori/FP-Growth algorithms
- Unit 5: Cluster analysis — K-means, hierarchical clustering, and DBSCAN
Open Elective-I: Principles of Operating Systems / Computer Organization and Architecture
the two subjects IT’s department documentation lists as its own open-elective offering to other branches; both mirror the core II-II Operating Systems and II-I Digital Logic & Computer Organization syllabi already summarized above, condensed to a standalone lecture-only course. The document does not include a syllabus for “Entrepreneurship Development & Venture Creation,” the alternative named in the same table row, beyond its title.
Advanced Java Lab
hands-on JDBC, servlet, JSP, and Spring exercises that build a working CRUD web application end to end.
- JDBC operations using Statement, PreparedStatement, and stored procedures; scrollable/updatable result sets
- Servlet deployment, session management with cookies/HTTP sessions, and JSP/JSTL tag usage
- MVC implementation and database transaction management using the Spring framework
Computer Networks Lab
protocol simulation and packet-analysis exercises that make the OSI/TCP-IP layer concepts from lecture concrete.
- Framing, checksum, CRC, and Hamming code implementations for error detection/correction
- Sliding window protocol, routing algorithm (Dijkstra, distance vector), and congestion control simulations
- Wireshark packet capture/analysis and Nmap-based network/OS scanning
Full Stack Development-1
a skill-enhancement lab covering the front-end web trio (HTML, CSS, JavaScript) needed to build interactive, validated static web pages before moving to back-end frameworks.
- HTML lists, links, images, tables, forms, and frames; HTML5 semantic tags
- CSS selectors, the box model, and styling techniques (color, font, background)
- JavaScript I/O, control flow, built-in/user-defined objects, functions, events, and form validation
User Interface Design using Flutter
introduces cross-platform mobile UI development with Flutter and Dart, covering widgets, layouts, state management, and basic API-driven apps.
- Installing Flutter/Dart and exploring core widgets, layouts (Row/Column/Stack), and responsive design
- Navigation, stateful/stateless widgets, state management, and custom widget theming
- Form validation, animations, REST API data fetching, and basic UI testing/debugging
