Comprehensive Course Structure
| Semester | Course Code | Full Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-2-4 | - |
| 1 | CS102 | Mathematics for Computer Science | 3-0-2-4 | - |
| 1 | CS103 | Computer Organization and Architecture | 3-0-2-4 | - |
| 1 | CS104 | Lab: Introduction to Programming | 0-0-6-3 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-0-2-4 | CS101 |
| 2 | CS202 | Digital Logic Design | 3-0-2-4 | - |
| 2 | CS203 | Database Management Systems | 3-0-2-4 | CS101 |
| 2 | CS204 | Lab: Data Structures and Algorithms | 0-0-6-3 | CS101 |
| 3 | CS301 | Operating Systems | 3-0-2-4 | CS201, CS202 |
| 3 | CS302 | Computer Networks | 3-0-2-4 | CS201, CS202 |
| 3 | CS303 | Software Engineering | 3-0-2-4 | CS201 |
| 3 | CS304 | Lab: Operating Systems | 0-0-6-3 | CS201, CS202 |
| 4 | CS401 | Web Technologies | 3-0-2-4 | CS201 |
| 4 | CS402 | Mobile Application Development | 3-0-2-4 | CS201 |
| 4 | CS403 | Human-Computer Interaction | 3-0-2-4 | CS201 |
| 4 | CS404 | Lab: Web Technologies | 0-0-6-3 | CS201 |
| 5 | CS501 | Machine Learning | 3-0-2-4 | CS201, CS202 |
| 5 | CS502 | Cryptography and Network Security | 3-0-2-4 | CS201, CS202 |
| 5 | CS503 | Data Mining | 3-0-2-4 | CS201 |
| 5 | CS504 | Lab: Machine Learning | 0-0-6-3 | CS201, CS202 |
| 6 | CS601 | Big Data Analytics | 3-0-2-4 | CS501, CS503 |
| 6 | CS602 | Embedded Systems | 3-0-2-4 | CS201 |
| 6 | CS603 | Cloud Computing | 3-0-2-4 | CS301 |
| 6 | CS604 | Lab: Big Data Analytics | 0-0-6-3 | CS501, CS503 |
| 7 | CS701 | Advanced Algorithms | 3-0-2-4 | CS201 |
| 7 | CS702 | Research Methodology | 3-0-2-4 | - |
| 7 | CS703 | Internship | 0-0-12-6 | - |
| 8 | CS801 | Capstone Project | 3-0-12-9 | CS701, CS702 |
| 8 | CS802 | Final Year Thesis | 3-0-6-6 | CS701, CS702 |
Advanced Departmental Electives
The department offers a rich selection of advanced departmental electives that allow students to specialize in emerging areas of computer science. These courses are designed to be both theoretically rigorous and practically relevant, preparing students for leadership roles in industry or research.
Machine Learning: This course covers supervised and unsupervised learning techniques, neural networks, deep learning frameworks, and reinforcement learning. Students learn to implement algorithms using Python libraries such as scikit-learn, TensorFlow, and PyTorch.
Cryptography and Network Security: This elective delves into classical and modern cryptographic methods, including symmetric and asymmetric encryption, hash functions, digital signatures, and secure protocols. The course includes practical labs on network security tools and vulnerability assessments.
Data Mining: Students explore data preprocessing, clustering, classification, association rule mining, and anomaly detection. The course emphasizes real-world applications using datasets from domains such as healthcare, finance, and social media.
Big Data Analytics: This course introduces students to Hadoop, Spark, and other big data technologies. It covers data ingestion, processing, and visualization techniques, with emphasis on scalable solutions for large-scale datasets.
Embedded Systems: The curriculum includes microcontroller programming, real-time operating systems, hardware-software co-design, and IoT integration. Students build projects involving sensors, actuators, and communication modules.
Cloud Computing: This course explores cloud architectures, virtualization technologies, containerization using Docker, orchestration with Kubernetes, and cloud service models (IaaS, PaaS, SaaS). Practical labs involve deploying applications on platforms like AWS, Azure, and GCP.
Web Technologies: Students learn modern web development frameworks such as React, Angular, Node.js, and Express. The course covers RESTful APIs, database integration, authentication, and responsive design principles.
Mobile Application Development: This elective focuses on cross-platform mobile app development using Flutter and React Native. Students develop apps for both iOS and Android platforms with a focus on user experience and performance optimization.
Human-Computer Interaction: The course examines human cognitive processes, interface design principles, usability testing, and accessibility standards. Students conduct research projects involving user studies and prototype development.
Software Engineering: This course covers software development lifecycle, agile methodologies, code quality assurance, project management tools, and software architecture patterns. Students engage in group projects that simulate real-world development environments.
Project-Based Learning Philosophy
Our approach to project-based learning is grounded in the belief that students learn best when they are actively engaged in solving complex problems. The program includes mandatory mini-projects in early semesters and a final-year capstone project that spans the entire academic year.
The mini-projects, typically undertaken in the second and third years, are designed to reinforce core concepts through hands-on implementation. These projects are evaluated based on technical execution, creativity, documentation quality, and teamwork skills.
The final-year thesis/capstone project allows students to pursue independent research or collaborate with industry partners on real-world challenges. Students select their projects in consultation with faculty mentors, ensuring alignment with their interests and career goals.
Faculty mentors guide students throughout the process, providing technical expertise, feedback, and support. Regular progress reviews and milestone assessments ensure that students stay on track toward successful completion of their projects.