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

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