Course Structure Overview
The Computer Science curriculum is divided into eight semesters, structured to progressively build theoretical knowledge and practical skills. Each semester includes core courses, departmental electives, science electives, and laboratory components.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | CS101 | Engineering Mathematics I | 3-1-0-4 | - |
| I | CS102 | Physics for Computer Science | 3-1-0-4 | - |
| I | CS103 | Introduction to Programming | 3-1-0-4 | - |
| I | CS104 | Computer Organization and Architecture | 3-1-0-4 | - |
| I | CS105 | Engineering Graphics | 2-1-0-3 | - |
| I | CS106 | Basic Electrical and Electronics Engineering | 3-1-0-4 | - |
| I | CS107 | Programming Lab | 0-0-2-1 | - |
| I | CS108 | Computer Organization Lab | 0-0-2-1 | - |
| II | CS201 | Engineering Mathematics II | 3-1-0-4 | CS101 |
| II | CS202 | Data Structures and Algorithms | 3-1-0-4 | CS103 |
| II | CS203 | Digital Logic Design | 3-1-0-4 | - |
| II | CS204 | Object Oriented Programming | 3-1-0-4 | CS103 |
| II | CS205 | Discrete Mathematics | 3-1-0-4 | - |
| II | CS206 | Basic Electronics Lab | 0-0-2-1 | - |
| II | CS207 | Data Structures Lab | 0-0-2-1 | CS202 |
| II | CS208 | OOP Lab | 0-0-2-1 | CS204 |
| III | CS301 | Database Management Systems | 3-1-0-4 | CS202 |
| III | CS302 | Operating Systems | 3-1-0-4 | CS204 |
| III | CS303 | Computer Networks | 3-1-0-4 | CS203 |
| III | CS304 | Software Engineering | 3-1-0-4 | CS202 |
| III | CS305 | Probability and Statistics | 3-1-0-4 | CS101 |
| III | CS306 | DMS Lab | 0-0-2-1 | CS301 |
| III | CS307 | OS Lab | 0-0-2-1 | CS302 |
| III | CS308 | Networks Lab | 0-0-2-1 | CS303 |
| IV | CS401 | Compiler Design | 3-1-0-4 | CS301 |
| IV | CS402 | Distributed Systems | 3-1-0-4 | CS303 |
| IV | CS403 | Artificial Intelligence | 3-1-0-4 | CS305 |
| IV | CS404 | Cybersecurity Fundamentals | 3-1-0-4 | CS302 |
| IV | CS405 | Web Technologies | 3-1-0-4 | CS204 |
| IV | CS406 | Compiler Lab | 0-0-2-1 | CS401 |
| IV | CS407 | AI Lab | 0-0-2-1 | CS403 |
| IV | CS408 | Web Technologies Lab | 0-0-2-1 | CS405 |
| V | CS501 | Data Mining and Analytics | 3-1-0-4 | CS301 |
| V | CS502 | Machine Learning | 3-1-0-4 | CS305 |
| V | CS503 | Cloud Computing | 3-1-0-4 | CS303 |
| V | CS504 | Software Testing and Quality Assurance | 3-1-0-4 | CS304 |
| V | CS505 | User Interface Design | 3-1-0-4 | CS204 |
| V | CS506 | Data Mining Lab | 0-0-2-1 | CS501 |
| V | CS507 | ML Lab | 0-0-2-1 | CS502 |
| V | CS508 | Cloud Computing Lab | 0-0-2-1 | CS503 |
| VI | CS601 | Advanced Topics in AI | 3-1-0-4 | CS403 |
| VI | CS602 | Network Security | 3-1-0-4 | CS303 |
| VI | CS603 | Embedded Systems | 3-1-0-4 | CS203 |
| VI | CS604 | Internet of Things | 3-1-0-4 | CS203 |
| VI | CS605 | Game Development | 3-1-0-4 | CS204 |
| VI | CS606 | AI Research Project | 0-0-2-2 | CS502 |
| VI | CS607 | Security Lab | 0-0-2-1 | CS602 |
| VI | CS608 | IoT Lab | 0-0-2-1 | CS604 |
| VII | CS701 | Capstone Project I | 3-1-0-4 | - |
| VII | CS702 | Research Methodology | 3-1-0-4 | - |
| VII | CS703 | Specialized Elective I | 3-1-0-4 | - |
| VII | CS704 | Specialized Elective II | 3-1-0-4 | - |
| VII | CS705 | Capstone Lab I | 0-0-2-1 | - |
| VII | CS706 | Specialized Lab I | 0-0-2-1 | - |
| VII | CS707 | Specialized Lab II | 0-0-2-1 | - |
| VIII | CS801 | Capstone Project II | 3-1-0-4 | - |
| III | CS309 | Engineering Ethics | 2-0-0-2 | - |
| V | CS509 | Professional Development | 2-0-0-2 | - |
Advanced Departmental Electives
Advanced departmental electives are designed to provide depth in specialized areas:
- Advanced Machine Learning (CS502): This course delves into deep learning architectures, reinforcement learning, and advanced NLP techniques. Students engage with real-world datasets using frameworks like TensorFlow and PyTorch.
- Blockchain and Cryptocurrency Systems (CS602): Covers distributed ledger technologies, smart contracts, consensus algorithms, and applications in finance and supply chain management.
- Human-Computer Interaction (HCI) (CS505): Focuses on designing user-centric interfaces, usability testing, accessibility standards, and interaction design principles using tools like Figma and Sketch.
- Quantum Computing Fundamentals (CS601): Introduces quantum mechanics, qubits, quantum gates, and algorithms. Students implement basic quantum programs using Qiskit and Cirq.
- Computer Vision and Image Processing (CS403): Explores image segmentation, object detection, CNNs, and computer vision applications in autonomous vehicles and medical imaging.
- DevOps and Cloud Native Applications (CS503): Covers CI/CD pipelines, containerization using Docker, orchestration with Kubernetes, and cloud platforms like AWS and Azure.
- Natural Language Processing (NLP) (CS502): Focuses on text processing, sentiment analysis, language modeling, and transformer architectures for NLP tasks.
- Advanced Cybersecurity (CS602): Covers advanced topics like penetration testing, malware analysis, incident response, and secure coding practices.
- Data Mining and Big Data Analytics (CS501): Deals with data preprocessing, clustering, classification, association rule mining, and scalable analytics using Hadoop and Spark.
- Software Testing and Quality Assurance (CS504): Introduces testing methodologies, automation tools, software quality metrics, and compliance standards like ISO 9001.
Project-Based Learning Philosophy
The department believes that project-based learning is essential for developing practical skills and fostering innovation. Mini-projects are introduced in the third semester, where students work on small-scale applications using real-world datasets or simulated environments. These projects emphasize teamwork, communication, and iterative development.
Final-year capstone projects are undertaken under the guidance of faculty mentors and often involve collaboration with industry partners. Students select projects based on their interests and career goals, working closely with advisors to define scope, methodology, and deliverables.
Evaluation criteria for mini-projects include technical execution, documentation quality, presentation skills, and peer feedback. The capstone project is assessed through milestone reviews, final report submission, and live demonstrations to a panel of faculty members and industry experts.