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

by | Jul 11, 2026 | JNTUK R23 Syllabus

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

JNTUK R23 B.Tech IT III Year II Semester (3-2) Syllabus & Subject-wise Topics

by | Jul 11, 2026 | JNTUK R23 Syllabus

A mandatory Industry Internship / Mini Project of 8 weeks runs during the following summer vacation, with no unit-wise syllabus in the source document beyond a note that interested students may pursue SWAYAM Plus courses on Data Analytics or AI for Real-World Applications alongside it.

Cloud Computing

covers cloud service and deployment models, the enabling technologies (virtualization, containers, SOA) behind them, and emerging serverless/edge computing patterns.

• Unit 1: Cloud fundamentals — IaaS/PaaS/SaaS, deployment models, and major cloud providers
• Unit 2: Cloud-enabling technologies — parallel/distributed computing, RPC, SOA, and virtualization
• Unit 3: Virtualization and containers — XEN/VMware, Docker, and Kubernetes orchestration
• Unit 4: Cloud challenges — interoperability, scalability, energy efficiency, and cloud security
• Unit 5: Advanced concepts — serverless computing, cloud-centric IoT, edge/fog computing, and DevOps

Cryptography & Network Security

covers the mathematical foundations and practical mechanisms (symmetric/asymmetric encryption, hashing, digital signatures) used to secure data and communications, common to CSE, CS, and IT.

• Unit 1: Basic principles — security goals, cryptographic attacks, and the mathematics of cryptography
• Unit 2: Symmetric encryption — DES and AES structure and analysis
• Unit 3: Asymmetric encryption — RSA, Rabin, ElGamal, and elliptic-curve cryptosystems
• Unit 4: Data integrity — message authentication, hash functions, digital signatures, and key management
• Unit 5: Network security protocols — PGP/S-MIME, SSL/TLS, IPSec, and firewalls/IDS

Machine Learning

introduces supervised and unsupervised learning paradigms — nearest-neighbor methods, decision trees, linear discriminants, and clustering — as the algorithmic core behind data-driven prediction systems.

• Unit 1: Introduction to machine learning — learning paradigms, data acquisition, and model evaluation stages
• Unit 2: Nearest-neighbor models — proximity measures, KNN classification, and regression
• Unit 3: Decision trees and Bayes classifiers — impurity measures, random forests, and Naive Bayes
• Unit 4: Linear discriminants — perceptrons, support vector machines, logistic regression, and multi-layer perceptrons
• Unit 5: Clustering — K-means, hierarchical clustering, fuzzy C-means, and spectral clustering

Software Testing Methodologies

(Professional Elective-II) — covers systematic testing techniques (path testing, data-flow testing, state-based testing) and the tooling used to automate them, common across the CSE/IT family of branches.

• Unit 1: Testing fundamentals and path testing — bug taxonomy, flow graphs, and path sensitizing
• Unit 2: Transaction flow and data flow testing — transaction flow techniques and domain testing
• Unit 3: Paths and logic-based testing — path expressions, decision tables, and KV charts
• Unit 4: State-based testing — state graphs and transition testing
• Unit 5: Graph matrices — matrix-based test techniques and tool exposure (JMeter/Selenium/SoapUI)

Augmented Reality & Virtual Reality

(Professional Elective-II) — covers the display, tracking, and interaction technologies behind AR/VR systems, alongside the human perceptual science that makes immersive experiences work.

• Unit 1: Introduction to AR — displays, tracking, calibration, and coordinate systems
• Unit 2: Computer vision for AR — marker tracking, natural feature tracking, and AR software architectures
• Unit 3: Introduction to VR — geometry of virtual worlds and the physics of light and optics
• Unit 4: Human vision — visual perception, rendering, and correcting optical distortions
• Unit 5: Motion and interaction — vestibular response, locomotion, and audio rendering in VR

DevOps

(Professional Elective-II) — covers the culture, tooling, and automation pipeline (Git, Jenkins, Docker, Ansible, Kubernetes) that connects development and operations into continuous integration/delivery, common across CSE, CS, IT, and AI/ML branches.

• Unit 1: Introduction to DevOps — SDLC, DevOps lifecycle, and CI/CD automation concepts
• Unit 2: Source code management — Git workflow, branching, and code-quality tools like SonarQube
• Unit 3: Continuous integration — Jenkins architecture, pipelines, and build automation
• Unit 4: Continuous delivery — Docker containerization and Selenium-based testing
• Unit 5: Configuration management — Ansible playbooks and Kubernetes/OpenShift container orchestration

Generative AI

(Professional Elective-II) — introduces generative modeling architectures — transformers, GANs, VAEs, and diffusion models — behind modern text, image, and multimedia generation tools.

• Unit 1: Introduction to Gen AI — generative vs. discriminative modeling and generative model types
• Unit 2: Text generation — transformer architecture, BERT/GPT models, and prompt engineering with RLHF
• Unit 3: Image generation — GANs, variational autoencoders, stable diffusion, and CLIP/DALL-E
• Unit 4: Painting, music, and play generation — cyclic GANs, style transfer, and music-generating RNNs
• Unit 5: Open-source models and frameworks — fine-tuning, LangChain, Llama, and Hugging Face deployment

Software Project Management

(Professional Elective-III) — covers planning, estimating, and tracking software projects across their lifecycle, extending into agile and DevOps delivery models.

• Unit 1: Conventional software management — the waterfall model and software economics
• Unit 2: Life cycle phases — inception, elaboration, construction, and transition artifacts
• Unit 3: Model-based architectures — process workflows, milestones, and iterative planning
• Unit 4: Project organization — line-of-business structures and project control metrics
• Unit 5: Agile and DevOps — Scrum adoption patterns and the DevOps delivery pipeline

Mobile Adhoc Networks

(Professional Elective-III) — covers the design of MANETs and wireless sensor networks, from MAC and routing protocols through security, common across CSE, CS, IT, and AI/ML/CSD branches.

• Unit 1: Introduction to ad hoc networks — MANET characteristics and MAC protocol design
• Unit 2: Routing protocols — topology-based vs. position-based routing and transport-layer solutions
• Unit 3: Security protocols — network security requirements, attacks, and intrusion detection in MANETs
• Unit 4: Wireless sensor basics — sensing/communication range, clustering, and data retrieval
• Unit 5: WSN security — key management, secure data aggregation, and sensor network operating systems

Natural Language Processing

(Professional Elective-III) — covers computational techniques for processing human language, from tokenization and part-of-speech tagging through parsing, semantics, and discourse analysis.

• Unit 1: Introduction — language modeling, finite-state automata, and spelling correction
• Unit 2: Word-level analysis — N-grams, part-of-speech tagging, and Hidden Markov Models
• Unit 3: Syntactic analysis — context-free grammars, dependency grammar, and probabilistic parsing
• Unit 4: Semantics and pragmatics — word sense disambiguation and thematic roles
• Unit 5: Discourse analysis — anaphora/coreference resolution and lexical resources like WordNet

Distributed Operating System

(Professional Elective-III) — covers the design issues unique to distributed systems: message passing, remote procedure calls, distributed shared memory, and distributed file systems.

• Unit 1: Fundamentals — distributed computing system models and message-passing systems
• Unit 2: Remote procedure calls — the RPC model, stub generation, and client-server binding
• Unit 3: Distributed shared memory — DSM architecture, consistency models, and synchronization
• Unit 4: Resource management — global scheduling algorithms and process migration
• Unit 5: Distributed file systems — file-sharing semantics, caching, replication, and fault tolerance

Open Elective-II: Principles of Database Management Systems

IT’s contribution to the university’s open-elective pool for other branches; it mirrors the core II-II Database Management Systems syllabus already summarized above, minus the lab component. The table lists “Open Elective-II” as its own numbered row without repeating the title inline; this is the subject named in the department’s open-elective offering list.

Cloud Computing Lab

hands-on virtualization, containerization, and cloud-platform exercises that put the lecture course’s service models into practice.

• Setting up VirtualBox/VMware VMs and installing compilers inside them
• Launching AWS EC2 instances, Docker containers, and Google App Engine applications
• Simulating cloud scheduling scenarios with CloudSim and serverless functions with OpenFaaS

Machine Learning Lab

implements the classification, regression, and clustering algorithms from lecture using Python/R/Weka on real datasets.

• Computing central tendency/dispersion measures and applying data preprocessing techniques
• Implementing KNN, decision tree, random forest, Naive Bayes, SVM, and logistic regression models
• Implementing K-means, fuzzy C-means, and expectation-maximization clustering

Soft Skills

a skill-enhancement course building the communication, interpersonal, and job-readiness competencies (group discussions, interviews, etiquette) students need heading into placements and internships.

• Analytical thinking, listening skills, and verbal/non-verbal communication
• Self-management skills (anger, stress, and time management) and etiquette
• Job-oriented skills — group discussions, resume preparation, and mock interviews

Technical Paper Writing & IPR

an audit course teaching technical writing conventions and the basics of intellectual property rights, preparing students to document and protect original work.

• Unit 1: Introduction to technical report writing — sentence structure, transitions, and report planning
• Unit 2: Drafting and design — use of drafts, illustrations, and plain-English editing
• Unit 3: Proofreading and presentation — summaries and proposal writing
• Unit 4: Word processing tools — tables of contents, tracked changes, and citations
• Unit 5: Intellectual property — patents, copyrights, and the patenting process

JNTUK R23 B.Tech IT IV Year I Semester (4-1) Syllabus & Subject-wise Topics

by | Jul 11, 2026 | JNTUK R23 Syllabus

Internet of Things

covers the sensing, connectivity, and processing building blocks that let embedded devices communicate and act autonomously, from low-level protocols up to fog computing and real-world IoT case studies.

• Unit 1: Predecessors and emergence of IoT — WSNs, M2M communication, and IoT networking components
• Unit 2: Sensing and actuation — sensor/actuator characteristics and IoT processing topologies
• Unit 3: Connectivity technologies — Zigbee, RFID, NFC, LoRa, Wi-Fi, and Bluetooth
• Unit 4: Interoperability and fog computing — IoT standards and fog computing architecture
• Unit 5: Paradigms and challenges — emerging IoT trends and agricultural/vehicular IoT case studies

Human Resources & Project Management

combines HR management fundamentals (recruitment, training, performance appraisal) with project management essentials (planning, scheduling, monitoring), preparing students for the managerial dimension of engineering careers.

• Unit 1: HRM fundamentals — HR functions, planning, recruitment, and selection
• Unit 2: HR development — training methods, performance appraisal, and career development
• Unit 3: Project management basics — project life cycle, proposals, and project appraisal
• Unit 4: Project types — management challenges specific to different project categories
• Unit 5: Project implementation and review — project control, human aspects, and performance evaluation

Software Architecture & Design Pattern

(Professional Elective-IV) — teaches reusable object-oriented design patterns and architectural styles (like MVC) for structuring complex, maintainable software systems.

• Unit 1: Introduction to design patterns — the pattern catalog and pattern selection
• Unit 2: System analysis — gathering requirements and defining conceptual classes
• Unit 3: Structural design patterns — adapter, bridge, composite, decorator, facade, and proxy
• Unit 4: MVC architecture — applying MVC to interactive system design
• Unit 5: Distributed objects — Java RMI, web services (SOAP/REST), and enterprise service buses

Deep Learning

(Professional Elective-IV) — covers neural network fundamentals through convolutional and recurrent architectures, building the foundation for modern computer vision and NLP applications.

• Unit 1: Fundamentals of deep learning — evolution of machine learning and overfitting/underfitting
• Unit 2: Introducing deep learning — biological vs. machine vision and training deep networks
• Unit 3: Neural networks — Keras/TensorFlow basics and binary/multiclass classification
• Unit 4: Convolutional and recurrent networks — CNN representation learning and RNNs in PyTorch
• Unit 5: Applications and research — GANs, deep reinforcement learning, and autoencoders

Computer Vision

(Professional Elective-IV) — covers how machines interpret images, from camera models and filtering through segmentation and multi-view geometry, for applications like object recognition and robotic perception.

• Unit 1: Cameras and radiometry — pinhole cameras, shading models, and color perception
• Unit 2: Linear filters and texture — convolution, edge detection, and texture analysis
• Unit 3: Multiple-view geometry — stereopsis and segmentation by clustering
• Unit 4: Model fitting — the Hough transform and Kalman filter-based tracking
• Unit 5: Geometric camera models — camera calibration and model-based vision

Blockchain Technology

(Professional Elective-IV) — covers blockchain fundamentals, consensus mechanisms, and smart contracts across public, private, and consortium blockchain systems, plus their security and application landscape.

• Unit 1: Blockchain fundamentals — consensus mechanisms and cryptocurrency basics
• Unit 2: Public blockchain systems — Bitcoin/Ethereum and smart contracts
• Unit 3: Private and consortium blockchains — Hyperledger, Multichain, and initial coin offerings
• Unit 4: Blockchain security — security challenges and applications in banking, healthcare, and supply chain
• Unit 5: Case studies and implementation — blockchain platforms using Python and Hyperledger Fabric

Agile Methodologies

(Professional Elective-V) — covers the Agile Manifesto, Scrum, XP, and Lean/Kanban practices that shape how modern software teams plan and deliver iteratively.

• Unit 1: Learning agile — the Agile Manifesto and understanding agile values
• Unit 2: Agile principles — the 12 principles and project delivery practices
• Unit 3: Scrum — self-organizing teams, sprints, and scrum planning
• Unit 4: Extreme Programming — XP practices and embracing change through simplicity
• Unit 5: Lean and Kanban — eliminating waste and agile coaching

Big Data Analytics

(Professional Elective-V) — covers the tools and techniques (Hadoop, NoSQL, Spark) used to store, process, and analyze datasets too large for traditional systems.

• Unit 1: Big data landscape — industry examples and Hadoop-based technologies
• Unit 2: NoSQL — aggregate data models, sharding, replication, and Cassandra
• Unit 3: Hadoop ecosystem — HDFS architecture, MapReduce, and Hive
• Unit 4: Apache Spark — RDDs, data frames, and the Catalyst optimizer
• Unit 5: Stream processing — event-time processing and structured streaming

Mobile Computing

(Professional Elective-V) — covers mobile communication standards, network architectures (GSM through 5G/6G), and mobile application infrastructure like synchronization and mobile IP.

• Unit 1: Mobile communications overview — signal propagation and mobile computing architecture
• Unit 2: 2G/3G/4G architectures — GSM services, GPRS, and wireless communication standards
• Unit 3: Mobile IP — packet delivery, handover management, and MANET/WSN basics
• Unit 4: Synchronization — mobile agents and data synchronization strategies
• Unit 5: Short-range networks — WLAN architecture and the wireless application protocol

Cyber Physical Systems

(Professional Elective-V) — covers the integration of computation with physical processes, including synthesis, security, synchronization, and real-time scheduling of CPS.

• Unit 1: Symbolic synthesis — construction of symbolic models for CPS
• Unit 2: CPS security — cyber security requirements and attack models
• Unit 3: Synchronization — distributed consensus algorithms and time-triggered architecture
• Unit 4: Real-time scheduling — fixed timing parameters and multicore scheduling
• Unit 5: Model integration — semantic domains for time and CPS modeling languages

Quantum Science and Technology

(Open Elective-IV option) — introduces quantum mechanics fundamentals and their application to quantum computing, communication, and sensing technologies emerging as a new computing paradigm.

• Unit 1: Fundamentals of quantum mechanics — the Schrödinger equation and quantum measurement theory
• Unit 2: Quantum information theory — qubits, entanglement, Bell states, and quantum gates
• Unit 3: Quantum computing — Grover’s and Shor’s algorithms and quantum error correction
• Unit 4: Quantum communication — quantum key distribution (BB84, E91) and quantum teleportation
• Unit 5: Quantum technologies — quantum sensors, hardware platforms, and global research initiatives

Open Elective-III / Open Elective-IV

the IT department’s own open-elective offering list pairs Open Elective-III with Object Oriented Programming Through Java (mirroring the II-I core course) and Open Elective-IV with Principles of Software Engineering / Computer Networks / Quantum Science and Technology (Principles of Software Engineering and Computer Networks mirror the II-II and III-I core courses respectively; Quantum Science and Technology is summarized above with its own full syllabus). As with earlier semesters, IT students’ actual Open Elective picks may instead be drawn from another department’s own offering, which this IT syllabus document does not include.

Prompt Engineering

a skill-enhancement course teaching how to design, refine, and evaluate prompts for large language models, from basic prompt anatomy through retrieval-augmented generation and agentic/multimodal applications.

• Foundations of prompt engineering — prompt anatomy, iterative refinement, and diagnosing prompt failures
• Advanced prompt patterns — few-shot prompting, role-based prompting, and constraint specification
• Structured outputs and reasoning — JSON/YAML generation and chain-of-thought prompting
• Retrieval-augmented generation — building LangChain-based RAG pipelines with vector stores
• Agents and multimodal AI — LLM agents, multimodal prompting, and prompt-injection/ethics evaluation

Constitution of India

an audit course covering the structure, rights, and governance mechanisms of the Indian Constitution, building civic literacy alongside the technical curriculum.

• Unit 1: History of the Indian Constitution — the drafting committee and constitutional philosophy
• Unit 2: Constitutional rights and duties — fundamental rights and directive principles
• Unit 3: Organs of governance — Parliament, the executive, and the judiciary
• Unit 4: Local administration — municipalities and Panchayati Raj institutions
• Unit 5: Election Commission — its role, functioning, and welfare bodies for SC/ST/OBC and women

JNTUK R23 B.Tech IT IV Year II Semester (4-2) Syllabus & Subject-wise Topics

by | Jul 11, 2026 | JNTUK R23 Syllabus

IV Year II Semester in the R23 IT structure consists entirely of a full-semester Industry Internship and Project Work component (12 credits, 24 practical hours/week). The source document does not include a unit-wise syllabus for this slot — evaluation is based on the internship/project itself rather than a lecture-based curriculum. A note in the document also reminds students that at least one MOOC/NPTEL course (3 credits, out of the 160-credit total) must be completed by this point to satisfy the R23 regulation’s mandatory-MOOC requirement.