by | Jul 11, 2026 | JNTUK R23 Syllabus
The final semester is dedicated entirely to a full-time industry internship combined with the major project, giving students an extended, immersive capstone experience rather than coursework. There are no theory subjects or lab courses this term — the entire semester’s credit weight sits on a single internship-and-project track.
Subjects
Internship & Project Work
- Total: 0-0-24, 12 credits
- Full-semester placement combining an industry internship with the student’s capstone project.
- Structured as sustained hands-on engagement rather than discrete units, culminating in a project report and evaluation in place of end-semester examinations.
by | Jul 11, 2026 | JNTUK R23 Syllabus
The third year opens with the program’s core machine-learning and networking courses alongside software engineering, plus the student’s first professional elective — a choice among automata/compiler theory, object-oriented analysis and design, soft computing, or IoT. A skill-enhancement full-stack web development course and a Flutter-based UI tinkering lab add practical breadth, and the community-service internship from the prior year is formally evaluated in this term.
Semester load: roughly 15 lecture, 1 tutorial and 10 practical hours per week, totaling 23 credits.
Subjects
Machine Learning
- Unit 1: Machine-learning paradigms, the modeling pipeline, and dataset considerations
- Unit 2: Proximity-based models such as k-nearest neighbors
- Unit 3: Decision-tree and Bayes-rule-based classifiers
- Unit 4: Linear discriminants — perceptrons, SVMs, logistic regression, and multi-layer perceptrons
- Unit 5: Clustering approaches, including k-means, fuzzy c-means, and spectral clustering
Computer Networks
- Unit 1: Network types, topologies, and the OSI/TCP-IP reference models
- Unit 2: Data-link layer framing, error control, and sliding-window protocols
- Unit 3: Media access control schemes and Ethernet standards
- Unit 4: Network-layer routing algorithms, congestion control, and IPv4/IPv6
- Unit 5: Transport-layer protocols (UDP/TCP) and application-layer services like HTTP, email, and DNS
Software Engineering
- Unit 1: Software life-cycle models from waterfall through agile and spiral approaches
- Unit 2: Project management, effort estimation, and requirements specification
- Unit 3: Software design principles, agile practices, and user-interface design
- Unit 4: Coding practices, testing strategies, and software quality standards
- Unit 5: CASE tools, software maintenance, and software reuse
Professional Elective-I options:
students choose one of Automata Theory & Compiler Design, Object Oriented Analysis and Design, Soft Computing, Internet of Things, or an approved NPTEL/SWAYAM course.
Automata Theory & Compiler Design
- Unit 1: Regular expressions, finite automata, and their equivalence
- Unit 2: Context-free grammars and pushdown automata
- Unit 3: Lexical analysis and top-down parsing
- Unit 4: Bottom-up parsing and syntax-directed translation
- Unit 5: Intermediate code generation and code optimization
Object Oriented Analysis and Design
- Unit 1: Managing complexity in large software systems
- Unit 2: UML fundamentals and structural modeling
- Unit 3: Class/object diagrams and advanced structural constructs
- Unit 4: Behavioral modeling — use cases, interactions, and activity diagrams
- Unit 5: Advanced behavioral and architectural modeling (state charts, components, deployment)
Soft Computing
- Unit 1: Neural network basics and biological inspiration
- Unit 2: Perceptron learning and backpropagation networks
- Unit 3: Fuzzy sets, relations, and membership functions
- Unit 4: Fuzzy inference systems and neuro-fuzzy hybrids
- Unit 5: Genetic algorithms and genetic-fuzzy hybrid systems
Internet of Things
- Unit 1: IoT overview, M2M communication, and connectivity principles
- Unit 2: Business models, layered IoT architectures, and standardization
- Unit 3: Web connectivity protocols for connected devices
- Unit 4: Data acquisition, organization, and business-process integration
- Unit 5: Cloud-based storage and computing for IoT, plus sensing/RFID technology
Machine Learning Lab
- Central-tendency and dispersion computations, and preprocessing techniques
- Implementing KNN, decision tree, and random forest classifiers
- Naïve Bayes, SVM, and multi-layer perceptron classification exercises
- Regression algorithms and clustering (k-means and related methods)
- Total: 3 practical hours per week, 1.5 credits
Computer Networks Lab
- Framing, checksum, and error-correction coding exercises
- Sliding-window and stop-and-wait protocol simulations
- Routing algorithm implementation (Dijkstra, distance-vector)
- Packet analysis with Wireshark and network scanning with Nmap
- NS2-based simulation of packet loss, congestion, and throughput
- Total: 3 practical hours per week, 1.5 credits
Full Stack Development-1 (Skill Enhancement Course)
- HTML structuring — lists, links, images, tables, forms, and frames
- CSS styling, selector types, and the box model
- JavaScript fundamentals — I/O, conditional logic, loops, and built-in/user-defined objects
- Functions, event handling, and form validation
- An introduction to Node.js
- Total: 1 tutorial and 2 practical hours per week, 2 credits
User Interface Design using Flutter (Tinkering Lab)
- Dart language basics and Flutter widget exploration
- Layout composition using Row, Column, and Stack widgets
- Responsive design and navigation between screens
- State management and custom widget/theme styling
- Form validation, animation, and REST API data fetching
- Total: 2 practical hours per week, 1 credit
Note: the Community Service Project Internship completed the previous summer is formally evaluated this semester, and students may alternatively take Entrepreneurship Development & Venture Creation in place of Open Elective-I.
by | Jul 11, 2026 | JNTUK R23 Syllabus
The second half of third year shifts focus to deep learning, operating systems, and data visualization as core subjects, with two elective slots covering topics from cryptography and cloud computing to computer vision and NoSQL databases. A soft-skills course and a technical-writing/IPR audit course prepare students for placements and research communication, alongside a mandatory summer industry internship or mini-project.
Semester load: roughly 20 lecture, 1 tutorial and 8 practical hours per week, totaling 23 credits.
Subjects
Deep Learning
- Unit 1: Biological neuron models and the perceptron learning algorithm
- Unit 2: Multilayer perceptrons, backpropagation, and regularization
- Unit 3: Modern optimizers (Adam, RMSprop, etc.) and training stabilization techniques
- Unit 4: Recurrent networks (LSTM, GRU) and convolutional architectures
- Unit 5: Recent developments — variational autoencoders and transformer models
Operating Systems
- Unit 1: OS services, system calls, and overall system structure
- Unit 2: Process management, threading models, and CPU scheduling
- Unit 3: Synchronization primitives and deadlock handling
- Unit 4: Memory management strategies and virtual memory
- Unit 5: File systems and protection mechanisms
Data Visualization
- Unit 1: The visualization process and principles of visual perception
- Unit 2: Building visual representations and visual-analytics workflows
- Unit 3: Classifying visualization systems and handling multi-dimensional/text data
- Unit 4: Visualizing hierarchical and network structures
- Unit 5: Visualizing volumetric, geographic, and collaborative data; evaluating visualizations
Professional Elective-II options:
Social Media Analytics, Cryptography & Network Security, Recommender Systems, Cloud Computing, or Sensor Networks.
Social Media Analytics
- Unit 1: Evolution of the web and characteristics of social media platforms
- Unit 2: The seven-layer social media analytics framework
- Unit 3: Text analytics techniques applied to social content
- Unit 4: Action analytics on social platforms
- Unit 5: Hyperlink analytics and viral-content detection
Cryptography & Network Security
- Unit 1: Core security concepts and classical encryption techniques
- Unit 2: Mathematical foundations of symmetric and asymmetric cryptography
- Unit 3: Block and stream ciphers, plus public-key algorithms (RSA, Diffie-Hellman, elliptic curve)
- Unit 4: Hash functions, message authentication, and digital signatures
- Unit 5: Transport, IP, and email security protocols
Recommender Systems
- Unit 1: Recommender system fundamentals and rating data
- Unit 2: Collaborative filtering approaches
- Unit 3: Content-based and knowledge-based recommendation
- Unit 4: Hybrid recommendation strategies
- Unit 5: Evaluation methods and the role of community/trust signals
Cloud Computing
- Unit 1: Cloud service and deployment models (IaaS/PaaS/SaaS, public/private/hybrid)
- Unit 2: Distributed computing foundations and service-oriented architecture
- Unit 3: Virtualization and container technologies (Docker, Kubernetes)
- Unit 4: Cloud economics, interoperability, and security challenges
- Unit 5: Serverless computing and cloud-centric IoT/edge computing
Sensor Networks
- Unit 1: Wireless network types and an introduction to sensor networks
- Unit 2: Single-node hardware architecture and network scenarios
- Unit 3: MAC and routing protocols for sensor networks
- Unit 4: Topology control, time synchronization, and localization
- Unit 5: Sensor node platforms, operating systems, and simulation tools
Professional Elective-III options:
Software Project Management, Quantum Computing, Computer Vision, NoSQL Databases, or an approved NPTEL/SWAYAM course.
Software Project Management
- Unit 1: Conventional software management and software economics
- Unit 2: Project life-cycle phases from inception through transition
- Unit 3–5: Project planning, tracking, organizational structures, and the tools used to manage schedule, cost, and resources
Quantum Computing
- Unit 1: Origins of quantum computing and qubits versus classical bits
- Unit 2: Underlying linear algebra and quantum-mechanical principles
- Unit 3: Qubit representation and quantum circuit design
- Unit 4: Core quantum algorithms (Deutsch-Jozsa, Shor, Grover)
- Unit 5: Quantum error correction and quantum cryptography
Computer Vision
- Unit 1: Camera models, radiometry, and shading
- Unit 2: Linear filtering, edge detection, and texture analysis
- Unit 3: Multi-view geometry and image segmentation
- Unit 4: Model fitting and motion tracking
- Unit 5: Geometric camera calibration and model-based vision
NoSQL Databases
- Unit 1: History and categories of NoSQL databases
- Unit 2: Comparing relational and NoSQL data models, replication, and sharding
- Unit 3: Document databases (MongoDB) and their use cases
- Unit 4: Column-family stores (HBase, Cassandra)
- Unit 5: Key-value and graph databases (Riak, Neo4j)
Deep Learning Lab
- Multi-layer perceptron and CNN implementations for image classification
- Text-classification exercises using embeddings and RNNs
- Transfer learning with pre-trained models such as VGG16
- Total: 3 practical hours per week, 1.5 credits
Data Visualization Lab
- Histogram, line-chart, and bar-chart exercises in R
- Box plots, scatter plots, and mosaic plots across sample datasets
- Heatmaps, geographic map visualizations, and 3D graphing
- Total: 3 practical hours per week, 1.5 credits
Soft Skills (Skill Enhancement Course)
- Unit 1: Analytical thinking, listening, and communication skills
- Unit 2: Self-management — time, stress, and anger management, plus workplace etiquette
- Unit 3: Grammar, correspondence, and professional writing
- Unit 4: Group discussions, resumes, and interview preparation
- Unit 5: Interpersonal relationships in professional settings
- Total: 1 tutorial and 2 practical hours per week, 2 credits
Technical Paper Writing & IPR (Audit Course)
- Unit 1: Fundamentals of technical report writing
- Unit 2: Drafting, editing, and plain-English writing conventions
- Unit 3: Proofreading and presenting final reports
- Unit 4: Word-processor techniques for formatting long documents
- Unit 5: Intellectual property fundamentals — patents, copyright, and the patenting process
- Ungraded audit course; no credits attached
Note: this semester is paired with a mandatory eight-week Industry Internship or Mini Project during the summer vacation.
by | Jul 11, 2026 | JNTUK R23 Syllabus
The final taught semester combines Big Data Analytics as the last major core subject with a human-resources/project-management course, two professional elective slots spanning topics like blockchain, DevOps, NLP, agile methods, and high-performance computing, and two open electives taken from other departments. A second full-stack development course and a Constitution of India audit course round out the term, alongside evaluation of the prior summer’s industry internship or mini-project.
Semester load: roughly 19 lecture, 1 tutorial and 2 practical hours per week, totaling 21 credits.
Subjects
Big Data Analytics
- Unit 1: Java data structures and generics needed for MapReduce-style programming
- Unit 2: Hadoop Distributed File System architecture and cluster configuration
- Unit 3: Writing MapReduce programs — mappers, reducers, and combiners
- Unit 4: Stream processing concepts and Spark’s RDD-based architecture
- Unit 5: Pig and Hive for higher-level querying over Hadoop data
Human Resources & Project Management (Management Course-II)
- Unit 1: HRM functions, planning, recruitment, and selection
- Unit 2: Training, performance appraisal, and career development
- Unit 3: Project management basics, resource planning, and project life cycle
- Unit 4: Managing different project types and their unique challenges
- Unit 5: Project implementation, control, and post-project review
Professional Elective-IV options:
Software Architecture & Design Patterns, Blockchain Technology, DevOps, Natural Language Processing, or an approved NPTEL/SWAYAM course.
Software Architecture & Design Patterns
- Unit 1: What design patterns are and how object-oriented design approaches them
- Unit 2: Systems analysis — gathering and structuring requirements
- Unit 3: The structural design-pattern catalog (adapter, bridge, composite, decorator, etc.)
- Unit 4: The MVC architectural pattern in practice
- Unit 5: Distributed-object design, including web services
Blockchain Technology
- Unit 1: Origins of Bitcoin, blockchain fundamentals, and cryptographic building blocks
- Unit 2: Underlying technologies — hash pointers, wallets, mining, and double-spending
- Unit 3: Consensus mechanisms — proof of work, proof of stake, and hybrid models
- Unit 4: Ethereum, smart contracts, and Solidity
- Unit 5: Hyperledger Fabric and broader blockchain applications beyond cryptocurrency
DevOps
- Unit 1: DevOps lifecycle, workflows, and CI/CD automation concepts
- Unit 2: Source-code management with Git, plus unit-testing and code-coverage tools
- Unit 3: Continuous integration with Jenkins
- Unit 4: Continuous delivery and containerization with Docker
- Unit 5: Configuration management with Ansible and container orchestration with Kubernetes
Natural Language Processing
- Unit 1: Language modeling basics, morphology, and tokenization
- Unit 2: N-grams, part-of-speech tagging, and statistical language models
- Unit 3: Syntactic parsing and context-free grammars
- Unit 4: Semantics, word-sense disambiguation, and pragmatics
- Unit 5: Discourse analysis, coreference resolution, and standard NLP lexical resources
Professional Elective-V options:
Agile Methodologies, Expert Systems, Reinforcement Learning, High Performance Computing, or an approved NPTEL/SWAYAM course.
Agile Methodologies
- Unit 1: Agile theory, the manifesto, and agile project management
- Unit 2: Agile process families — Scrum, Crystal, XP, and feature-driven development
- Unit 3: Knowledge-sharing practices such as story cards
- Unit 4: Requirements engineering in agile environments
- Unit 5: Agile metrics, quality assurance, and test-driven development
Expert Systems
- Unit 1: AI search strategies and game-playing algorithms
- Unit 2: Knowledge representation — predicate logic, semantic nets, and rule-based systems
- Unit 3: Expert system architecture and problem types
- Unit 4: Expert-system development tools and knowledge engineering
- Unit 5: Building an expert system and common pitfalls in practice
Reinforcement Learning
- Unit 1: Core reinforcement-learning concepts and terminology
- Unit 2: The multi-armed bandit problem and action-value methods
- Unit 3: Finite Markov decision processes and value functions
- Unit 4: Monte Carlo prediction and control methods
- Unit 5: Applied case studies such as TD-Gammon and job-shop scheduling
High Performance Computing
- Unit 1: Motivations for parallelism and parallel programming platforms
- Unit 2–5: Parallel algorithm design, interconnection networks, performance analysis, and techniques for parallelizing computational tasks
Open Elective-III and Open Elective-IV:
cross-department electives; Data Science students typically draw from subjects such as Operating Systems, Computer Networks, Software Engineering, or IoT Based Smart Systems as offered.
Full Stack Development-2 (Skill Enhancement Course)
- ExpressJS routing, middleware, sessions, and RESTful API design
- ReactJS components, props/state, conditional rendering, and hooks
- MongoDB installation, CRUD operations, and aggregation queries
- A capstone build such as a to-do list or quiz application
- Total: 1 tutorial and 2 practical hours per week, 2 credits
Constitution of India (Audit Course)
- Unit 1: History and drafting of the Indian Constitution
- Unit 2: Fundamental rights, directive principles, and fundamental duties
- Unit 3: Structure of the legislature, executive, and judiciary
- Unit 4: Local self-government — municipalities and panchayati raj institutions
- Unit 5: The Election Commission and welfare bodies for marginalized groups
- Ungraded audit course; no credits attached
Note: this semester includes evaluation of the Industry Internship or Mini Project completed the previous summer.
by | Jul 11, 2026 | JNTUK R23 Syllabus
The first semester of second year builds the mathematical and programming backbone the AI&ML specialization rests on, introducing Artificial Intelligence as a standalone course alongside advanced data structures and Java. Two hands-on labs pair with the theory courses, and Python is introduced as a skill-enhancement course to prepare students for the data-centric work ahead. A humanities course on human values and an audit course on environmental science round out the semester’s non-technical requirements.
Subjects
Discrete Mathematics & Graph Theory
- Unit 1: Mathematical logic — statements, connectives, well-formed formulas, truth tables, tautologies, normal forms, and inference techniques for both propositional and predicate calculus.
- Unit 2: Set theory — set operations, inclusion-exclusion, relations (partitions, closures, partial orders, Hasse diagrams), and functions including bijections, composition, and recursion.
- Unit 3: Combinatorics and recurrence relations — counting principles, permutations and combinations, binomial/multinomial theorems, generating functions, and methods for solving recurrences.
- Unit 4: Graph theory fundamentals — subgraphs, adjacency/incidence representations, isomorphism, and Eulerian/Hamiltonian paths.
- Unit 5: Multigraphs — bipartite and planar graphs, graph colouring and covering, chromatic number, spanning trees, and Prim’s/Kruskal’s/BFS/DFS-based tree construction.
L-T-P: 3-0-0, 3 credits
Universal Human Values — Understanding Harmony and Ethical Human Conduct
- Unit 1: Introduction to value education — self-exploration as a process, and the distinction between values and skills for sustained happiness.
- Unit 2: Harmony within the individual — the self as distinct from the body, and self-regulation as a path to inner balance.
- Unit 3: Harmony in family and society — trust and respect as foundational relational values, extending to a vision of a harmonious social order.
- Unit 4: Harmony with nature — interconnectedness among the orders of nature and existence understood as co-existence.
- Unit 5: Professional ethics — translating holistic understanding into ethical conduct, humanistic education, and value-based professional practice.
L-T-P: 2-1-0, 3 credits
Artificial Intelligence
- Unit 1: Foundations — the history of AI, intelligent agents, rationality, environment types, and problem formulation.
- Unit 2: Search — uninformed strategies (BFS, DFS), heuristic search (hill climbing, A, AO), and adversarial game-playing including minimax and alpha-beta pruning.
- Unit 3: Knowledge representation — predicate logic, semantic networks, frames, rule-based systems, and reasoning under uncertainty using Bayesian and Dempster-Shafer approaches.
- Unit 4: First-order logic and learning — inference techniques, forward/backward chaining, resolution, and learning paradigms including decision trees and reinforcement learning.
- Unit 5: Expert systems — architecture, knowledge acquisition, heuristics, and classic systems such as MYCIN and DART.
L-T-P: 3-0-0, 3 credits
Advanced Data Structures & Algorithm Analysis
- Unit 1: Algorithm analysis fundamentals, asymptotic notation, and self-balancing/multi-way search structures — AVL trees and B-trees.
- Unit 2: Heap-based priority queues, graph representations and traversal, and divide-and-conquer techniques such as quicksort, mergesort, and Strassen’s algorithm.
- Unit 3: Greedy strategies (job sequencing, minimum spanning trees, shortest paths) and dynamic programming (all-pairs shortest paths, optimal BSTs, knapsack, TSP).
- Unit 4: Backtracking (N-Queens, subset sum, graph colouring) and branch-and-bound approaches to knapsack and TSP.
- Unit 5: Computational complexity theory — NP-hard and NP-complete problems, Cook’s theorem, and classic hard graph/scheduling problems.
L-T-P: 3-0-0, 3 credits
Object Oriented Programming through Java
- Unit 1: OOP fundamentals, Java program structure, data types, operators, and control-flow statements.
- Unit 2: Classes, objects, constructors, access control, and method design including overloading and recursion.
- Unit 3: Arrays and inheritance mechanics, plus interfaces including default and static methods.
- Unit 4: Packages, the Java class library, exception handling, and Java I/O.
- Unit 5: String handling, multithreading, JDBC-based database connectivity, and building simple JavaFX GUIs.
L-T-P: 3-0-0, 3 credits
Advanced Data Structures & Algorithm Analysis Lab
- Hands-on construction and manipulation of AVL trees, B-trees, and heaps.
- Graph traversal implementations (BFS/DFS) and detection of connected/biconnected components.
- Comparative implementation of sorting algorithms and shortest-path techniques.
- Applying greedy and dynamic-programming strategies to knapsack, job sequencing, and spanning-tree problems.
- Backtracking and branch-and-bound implementations for N-Queens and the travelling salesperson problem.
L-T-P: 0-0-3, 1.5 credits
Object Oriented Programming through Java Lab
- Core language exercises covering primitive types, control structures, and basic I/O.
- Class design exercises: constructors, overloading, and access modifiers.
- Inheritance, interfaces, and runtime polymorphism implementations, including custom exception classes.
- Multithreading exercises (thread creation, synchronization, producer-consumer) and package creation.
- JavaFX GUI building and JDBC-based database connectivity exercises.
L-T-P: 0-0-3, 1.5 credits
Python Programming
(Skill Enhancement Course)
- Unit 1: Python fundamentals — installation and tooling, core language elements, and control-flow statements.
- Unit 2: Functions, string handling, and list operations.
- Unit 3: Dictionaries, tuples, and sets, along with their built-in operations.
- Unit 4: File handling and an introduction to object-oriented programming in Python.
- Unit 5: A first look at data-science tooling — JSON/XML handling, NumPy arrays, and pandas data frames.
L-T-P: 0-1-2, 2 credits
Environmental Science
(Audit Course)
- Unit 1: The multidisciplinary nature of environmental studies and the state of renewable/non-renewable natural resources.
- Unit 2: Ecosystem structure and function, energy flow, and biodiversity conservation.
- Unit 3: Causes, effects, and control measures for major categories of pollution, plus solid-waste and disaster management.
- Unit 4: Social dimensions of environmental sustainability, environmental legislation, and climate-related issues.
- Unit 5: Population growth, human health, and welfare programmes, supported by field-based observation exercises.
L-T-P: 2-0-0 (non-credit audit course)
Semester total: 16-2-8 contact hours, 20 credits, plus a mandatory 8-week community service project internship during the following summer vacation.
by | Jul 11, 2026 | JNTUK R23 Syllabus
This semester marks the shift into the AI&ML specialization proper, introducing Machine Learning and Database Management Systems as core subjects alongside the mathematical grounding of Optimization Techniques and Probability & Statistics. Digital Logic & Computer Organization supplies systems-level understanding, while two labs give students hands-on ML and SQL practice. Full Stack Development-1 and Design Thinking & Innovation add practical web-development and ideation skills.
Subjects
Optimization Techniques
(Management Course-I)
- Unit 1: Formulating optimization problems and classical techniques for single- and multi-variable optimization, including Lagrange multipliers and Kuhn-Tucker conditions.
- Unit 2: Linear programming — standard form, geometric interpretation, and the simplex algorithm.
- Unit 3: The transportation problem, including initial feasible solutions and optimality testing.
- Unit 4: Nonlinear programming for constrained and unconstrained cases, covering search methods and penalty-function approaches.
- Unit 5: Dynamic programming — multistage decision processes and the principle of optimality.
L-T-P: 2-0-0, 2 credits
Probability & Statistics
- Unit 1: Descriptive statistics for data science — central tendency, variability, skewness, and kurtosis.
- Unit 2: Correlation and regression, including linear, multiple, and curvilinear regression.
- Unit 3: Probability theory and standard discrete/continuous distributions (binomial, Poisson, normal).
- Unit 4: Sampling theory — sampling distributions, estimation, and the central limit theorem.
- Unit 5: Hypothesis testing — significance levels, error types, and tests for large and small samples.
L-T-P: 3-0-0, 3 credits
Machine Learning
- Unit 1: Introduction to machine learning — its evolution, learning paradigms, and the typical ML pipeline from data acquisition to model evaluation.
- Unit 2: Nearest-neighbour models — proximity measures, KNN classification and regression, and classifier performance evaluation.
- Unit 3: Decision-tree-based models and the Bayes classifier, including Naive Bayes and impurity measures.
- Unit 4: Linear discriminants — perceptrons, support vector machines, logistic regression, and multi-layer perceptrons trained via backpropagation.
- Unit 5: Clustering techniques — K-means, fuzzy C-means, rough clustering, expectation-maximization, and spectral clustering.
L-T-P: 3-0-0, 3 credits
Database Management Systems
- Unit 1: Database fundamentals, data models, schema architecture, and entity-relationship modelling.
- Unit 2: The relational model, constraints, relational algebra/calculus, and basic SQL DDL/DML.
- Unit 3: Advanced SQL querying — joins, nested queries, aggregation, and views.
- Unit 4: Normalization theory from 1NF through BCNF, 4NF, and 5NF.
- Unit 5: Transaction management — ACID properties, concurrency control, recovery, and indexing techniques including B+ trees and hashing.
L-T-P: 3-0-0, 3 credits
Digital Logic & Computer Organization
- Unit 1: Data representation and combinational logic circuits, including K-map minimization.
- Unit 2: Sequential circuits and the basic structural organization of a computer.
- Unit 3: Computer arithmetic and processor organization, including instruction execution and control unit design.
- Unit 4: Memory hierarchy — RAM, ROM, cache, and virtual memory considerations.
- Unit 5: I/O organization — interrupts, DMA, buses, and standard I/O interfaces.
L-T-P: 3-0-0, 3 credits
Machine Learning Lab
- Computing central tendency/dispersion measures and applying preprocessing techniques (attribute selection, missing-value handling, discretization, outlier removal).
- Implementing KNN, decision trees, and random forests for classification and regression.
- Applying Naive Bayes, support vector machines, and both simple linear and logistic regression.
- Building multi-layer perceptron classifiers.
- Implementing K-means, fuzzy C-means, and expectation-maximization clustering.
L-T-P: 0-0-3, 1.5 credits
Database Management Systems Lab
- DDL/DML/DCL practice — table creation, constraints, and basic queries.
- Nested queries, aggregate functions, grouping, and view management.
- PL/SQL programming — control structures, conversion/string/date functions.
- Building procedures, functions, cursors, and triggers.
- Database connectivity exercises using ODBC/JDBC.
L-T-P: 0-0-3, 1.5 credits
Full Stack Development-1
(Skill Enhancement Course)
- HTML fundamentals — lists, links, images, tables, forms, and frames.
- CSS styling — selector types, the box model, and color/font/text properties.
- JavaScript basics — input/output, type conversion, conditional statements, and loops.
- Working with predefined and user-defined JavaScript objects, functions, and events.
- An introduction to Node.js as a bridge toward server-side scripting.
L-T-P: 0-1-2, 2 credits
Design Thinking & Innovation
- Unit 1: Introduction to design thinking and its fundamental components and principles.
- Unit 2: The design thinking process from empathy through ideation.
- Unit 3: Innovation as a discipline and its role in product and service development.
- Unit 4: Product design, including specification-setting and prototyping activities.
- Unit 5: Applying design thinking to business processes.
L-T-P: 1-0-2, 2 credits
Semester total: 15-1-12 contact hours, 21 credits, continuing the mandatory community service project internship theme from the prior semester.
