Comprehensive Course Structure
The Computer Science program at Indira Gandhi Technological And Medical Science University Lower Subansiri is meticulously designed to provide a balanced mix of theoretical knowledge and practical skills. The curriculum spans 8 semesters, with each semester carrying a total credit load of approximately 16-18 credits.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | CS101 | Introduction to Programming | 3-0-0-3 | - |
| I | CS102 | Mathematics for Computer Science | 4-0-0-4 | - |
| I | CS103 | Physics for Engineers | 3-0-0-3 | - |
| I | CS104 | Chemistry for Engineers | 3-0-0-3 | - |
| I | CS105 | Engineering Graphics and Design | 2-0-0-2 | - |
| I | CS106 | English for Engineers | 2-0-0-2 | - |
| II | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| II | CS202 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| II | CS203 | Discrete Mathematics | 4-0-0-4 | CS102 |
| II | CS204 | Digital Logic and Computer Organization | 3-0-0-3 | - |
| II | CS205 | Calculus for Engineers | 4-0-0-4 | - |
| III | CS301 | Database Management Systems | 3-0-0-3 | CS201 |
| III | CS302 | Operating Systems | 3-0-0-3 | CS201 |
| III | CS303 | Computer Networks | 3-0-0-3 | CS204 |
| III | CS304 | Software Engineering | 3-0-0-3 | CS202 |
| III | CS305 | Probability and Statistics | 4-0-0-4 | CS102 |
| IV | CS401 | Artificial Intelligence | 3-0-0-3 | CS301 |
| IV | CS402 | Cybersecurity Fundamentals | 3-0-0-3 | CS303 |
| IV | CS403 | Data Mining and Analytics | 3-0-0-3 | CS305 |
| IV | CS404 | Distributed Systems | 3-0-0-3 | CS303 |
| IV | CS405 | Human-Computer Interaction | 3-0-0-3 | CS201 |
| V | CS501 | Machine Learning | 3-0-0-3 | CS401 |
| V | CS502 | Cloud Computing | 3-0-0-3 | CS404 |
| V | CS503 | Advanced Cybersecurity | 3-0-0-3 | CS402 |
| V | CS504 | Big Data Technologies | 3-0-0-3 | CS301 |
| V | CS505 | Research Methodology | 2-0-0-2 | - |
| VI | CS601 | Deep Learning | 3-0-0-3 | CS501 |
| VI | CS602 | Internet of Things (IoT) | 3-0-0-3 | CS403 |
| VI | CS603 | Blockchain Technologies | 3-0-0-3 | CS503 |
| VI | CS604 | Mobile Application Development | 3-0-0-3 | CS202 |
| VI | CS605 | Project Planning and Management | 2-0-0-2 | - |
| VII | CS701 | Capstone Project I | 4-0-0-4 | CS505 |
| VIII | CS801 | Capstone Project II | 6-0-0-6 | CS701 |
Detailed Departmental Elective Courses
Advanced departmental electives are offered to allow students to specialize further in their chosen fields:
- Machine Learning (CS501): This course delves into supervised and unsupervised learning techniques, neural networks, reinforcement learning, and deep learning architectures. Students gain hands-on experience using frameworks like TensorFlow and PyTorch.
- Cloud Computing (CS502): Explores cloud service models (IaaS, PaaS, SaaS), virtualization technologies, and deployment strategies. Students implement cloud-native applications using AWS, Azure, and Google Cloud Platform.
- Advanced Cybersecurity (CS503): Covers advanced topics in cryptography, network security, ethical hacking, incident response, and compliance frameworks. Practical labs involve penetration testing and vulnerability assessments.
- Big Data Technologies (CS504): Introduces students to Hadoop, Spark, NoSQL databases, and streaming analytics. Projects focus on processing large datasets for real-world applications.
- Research Methodology (CS505): Teaches research design, data collection methods, statistical analysis, and academic writing. Prepares students for thesis development and publication opportunities.
Project-Based Learning Philosophy
The department strongly believes in experiential learning through project-based education. From the second year onwards, students are introduced to mini-projects that reinforce theoretical concepts. These projects are typically completed in teams of 3-5 members and involve mentorship from faculty members.
For the final-year capstone project, students select a domain-specific problem and work closely with a faculty advisor to design, implement, and present a solution. The evaluation criteria include innovation, technical depth, documentation quality, and presentation skills.