Subjects Catalog

This course introduces computational thinking, computing environments, software development practices, version control, cybersecurity, artificial intelligence, and foundational system architecture.

Internet Programming covers HTML5, semantic HTML and forms, CSS-based responsive design, JavaScript, DOM manipulation, browser APIs, debugging, and deployment using GitHub Pages.

Basic Electrical and Electronics Engineering (ECE131) introduces students to the fundamentals of DC and AC circuits, electrical machines, semiconductor devices, filters, operational amplifiers, and embedded systems including IoT and cloud computing applications.

An introductory Python programming course covering core language constructs, data structures, and libraries such as NumPy, Pandas, Matplotlib, and OpenCV, culminating in real-world application projects, technical reporting, and presentations.

This course introduces Python programming fundamentals and covers conditional and iterative statements, functions and recursion, core data structures, object-oriented programming, file handling, exceptions, and regular expressions.

This course develops engineering mathematics skills in matrix methods and linear systems, differential and integral calculus, multivariable differentiation and integration, optimization, and Fourier series. Students apply these methods to solve mathematical and engineering problems involving systems of equations, derivatives, integrals, area, volume, and periodic functions.

This course covers matrix algebra, linear differential equations, Fourier series, multivariate calculus, and integral calculus. It develops students' ability to solve systems of linear equations, differential equations, and problems involving multi-variable derivatives, surface integrals, and volume integrals.

This course enables students to understand the basic principles of physics to lay the foundation for engineering courses, covering topics such as lasers, optical fibers, quantum mechanics, and the physics of solids.

Comprehensive course on computer programming in C covering fundamentals, control structures, functions, arrays, pointers, dynamic memory allocation, and derived types.

Orientation course providing foundational and practical insights into modern computing domains including Data Science, AI/ML, Cybersecurity, DevOps, Cloud Computing, and Full Stack Web Development with UI/UX design.

Comprehensive course on software engineering principles covering the software lifecycle, architectural design, object-oriented modeling with UML, testing methodologies, project estimation, maintenance, and modern DevOps practices.

Comprehensive engineering study of Database Management Systems (DBMS), spanning fundamental database concepts and file processing limitations, three-tier ANSI-SPARC architecture, data independence, relational data model, ER and Enhanced ER modeling, relational algebra and calculus operations, SQL (DDL, DML, DCL, TCL), integrity constraints, views, complex joins and subqueries, normalization theory (1NF, 2NF, 3NF, BCNF, 4NF, 5NF) based on functional and multivalued dependencies, indexing and hashing, transaction processing with ACID properties, concurrency control protocols (locking, timestamping, MVCC), crash recovery (WAL, checkpoints), PL/SQL programming constructs (stored procedures, functions, packages, triggers, cursors), and modern NoSQL distributed database paradigms (document, key-value, column-family, graph databases, CAP theorem, BASE properties).

This course focuses on the definitions, distinctions, and solution methods for various differential equations, including first-order and higher-order ordinary differential equations, linear differential equations, partial differential equations (heat, wave, and Laplace equations), and vector calculus encompassing gradient, divergence, curl, line, surface, and volume integrals, along with Green's, Stokes's, and Gauss divergence theorems.

This course on Discrete Mathematics focuses on employability and skill development, covering topics such as logic and proofs, combinatorics, recurrence relations, graph theory, and number theory.

Design Thinking (CSD233) introduces students to human-centered innovation and creative problem-solving, integrating AI tools across the design thinking stages of empathize, define, ideate, prototype, and test to build innovative products and services.

This course develops C++ programming and object-oriented programming skills through topics including classes, functions, pointers, constructors, file handling, operator overloading, inheritance, polymorphism, exception handling, templates, and the Standard Template Library. Students apply these concepts to solve programming problems and build software applications.

The subject covers the analysis and implementation of data structures and algorithms, including arrays, linked lists, stacks, queues, recursion, trees, heaps, hashing, graphs, searching, and sorting. It emphasizes complexity analysis, efficient data storage and retrieval, and applying appropriate data structures and algorithms to problem solving.

Computer Organization and Design covers digital systems and register transfer, HDLs and Verilog, computer organization and CPU architecture, input-output organization, memory systems, parallel processing, and modern computing architectures including GPU and quantum computing trends.

This subject covers computer network architectures, models, physical and data link layers, IP addressing, routing, transport and application layer protocols, congestion control, and wireless networking standards. It also develops practical skills in network configuration, subnetting, routing, IPv6, and server setup.

This course covers virtualization, distributed computing, cloud computing models and architecture, cloud migration, cloud economics, and AWS fundamentals. It also examines AWS compute, storage, database, identity, access management, and security services.

Signal and Systems (ECE220) covers continuous and discrete time signals, LTI systems, Fourier series and transforms, sampling, Laplace transforms, and Z-transforms, along with software simulation using Octave/Scilab/MATLAB/Python.

This course covers human-centered design frameworks, user research, problem identification, ideation, prototyping, validation, customer journey analysis, and iterative product redesign. Students apply design thinking methods to develop, evaluate, and present user-centered solutions.

Artificial Intelligence Essentials is a 4-credit course covering the foundations and applications of AI, state-space modeling and search, machine learning, deep learning and NLP, generative AI and prompt engineering, and AI workflows including MLOps.

The course is an introduction to discrete mathematics comprising essentials for computer science students, including counting and proof techniques.

This course covers the basics of organization and architecture of a digital computer, including concepts of instruction sets, microprocessor design, input-output units, and memory systems. It also analyzes modern computing architectures, Verilog HDL, and trends in computer architecture.

Course study material, comprehensive notes, subjective model answers, and practice MCQs for CSE273 - CSE273.

This course focuses on Java programming constructs, Object-Oriented Programming principles, inheritance, exception handling, multithreading, and the use of collections and generics. It aims to develop skills in creating efficient, reusable code and managing advanced Java applications involving database connectivity.

This course covers the fundamentals of operating systems, including OS structure, process management, CPU scheduling, synchronization, and deadlock handling. Students will also learn about memory management techniques like paging and segmentation, as well as disk scheduling methods.

This laboratory course covers practical operating systems concepts including Linux command-line tools, Bash shell scripting, low-level file and directory operations using OS system calls, process and thread management, and inter-process communication mechanisms.

This course covers the basic techniques of analyzing algorithms using space and time complexity, asymptotic notations, and various algorithm design techniques such as brute force, divide and conquer, dynamic programming, and greedy algorithms. Students will also learn string matching algorithms, backtracking, branch and bound techniques, and basic concepts of complexity classes.

This course covers the essentials of Artificial Intelligence, from foundational concepts and search algorithms to machine learning, deep learning, and generative AI. Students will learn to apply various AI techniques and tools to solve real-world problems, including model deployment and MLOps.

This subject provides an overview of random variables, probability distributions, correlation, regression, and statistical tests, enabling students to apply these concepts to engineering problems and analyze estimators.

This course aims to develop students' analytical skills by applying procedural fluency with number systems and mathematical operations to solve problems related to percentage, profit & loss, data series, ratio, proportion, permutation, combination, and reasoning.

The course focuses on developing advanced analytical skills, including numeration, percentage, ratio, counting methods, and logical reasoning, to prepare students for competitive examinations and enhance their employability.

A comprehensive course on Formal Languages and Automata Theory covering finite automata (DFA/NFA/e-NFA), regular expressions and regular languages, context-free grammars and languages, pushdown automata, parsing, Turing machines, decidability, and computational complexity.

The course develops skills in designing and analyzing efficient algorithms using complexity analysis, divide-and-conquer, dynamic programming, greedy methods, graph algorithms, transform-and-conquer, backtracking, branch and bound, and approximation techniques. It also covers string matching, computational geometry, advanced sequence-processing techniques, and computational complexity classes.

Database Essentials Toward Informatica covers business intelligence fundamentals, digital data structures, data integration architectures, data warehousing principles, multidimensional modeling, OLAP operations, ETL workflows with SSIS and data mining tools, and modern cloud databases with master data management (MDM).

Through this course students should be able to analyze foundational concepts and learning paradigms in machine learning, interpret classification techniques and probabilistic models, and understand regression and clustering algorithms for predictive modelling.

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This course focuses on employability, entrepreneurship, and skill development by teaching students to apply ethical principles in IT, understand intellectual property concepts, and analyze legal and security issues in the information technology sector.

This course provides comprehensive preparation for technical interviews by covering fundamental computer science concepts including data structures, algorithms, databases, operating systems, and computer networks. It emphasizes practical skills through whiteboard coding exercises, mock interviews, and behavioral scenarios to enhance problem-solving agility and communication.

This course enables students to develop professional soft skills including Digital Portfolio Design, Vocal Communication, Video CV creation, Interview preparedness, Group Discussions, and Workplace Ethics. Students will learn to demonstrate ethical conduct, professionalism, and responsibility in simulated workplace and placement scenarios.