Course Structure Overview
The Computer Science Engineering program at BTKIT is meticulously structured across eight semesters to ensure a progressive and comprehensive learning experience. Each semester combines core theoretical subjects with practical laboratory sessions and project-based learning components designed to bridge academic knowledge with industry relevance.
| Year | Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|---|
| First Year | I | CS101 | Engineering Mathematics I | 3-1-0-4 | None |
| CS102 | Physics for Computer Science | 3-1-0-4 | None | ||
| CS103 | Introduction to Programming | 3-1-0-4 | None | ||
| II | CS104 | Engineering Mathematics II | 3-1-0-4 | CS101 | |
| CS105 | Chemistry for Computer Science | 3-1-0-4 | None | ||
| CS106 | Data Structures and Algorithms | 3-1-0-4 | CS103 | ||
| III | CS107 | Object-Oriented Programming with Java | 3-1-0-4 | CS103 | |
| CS108 | Digital Logic and Computer Organization | 3-1-0-4 | CS106 | ||
| CS109 | Database Management Systems | 3-1-0-4 | CS107 | ||
| CS110 | Computer Networks | 3-1-0-4 | CS108 | ||
| CS111 | Software Engineering | 3-1-0-4 | CS109 | ||
| CS112 | Operating Systems | 3-1-0-4 | CS110 | ||
| Second Year | IV | CS201 | Advanced Data Structures and Algorithms | 3-1-0-4 | CS106 |
| CS202 | Discrete Mathematics | 3-1-0-4 | CS101 | ||
| CS203 | Microprocessor and Embedded Systems | 3-1-0-4 | CS108 | ||
| V | CS204 | Machine Learning Fundamentals | 3-1-0-4 | CS201 | |
| CS205 | Cryptography and Network Security | 3-1-0-4 | CS110 | ||
| CS206 | Web Technologies | 3-1-0-4 | CS107 | ||
| VI | CS207 | Big Data Analytics | 3-1-0-4 | CS201 | |
| CS208 | Computer Graphics and Visualization | 3-1-0-4 | CS107 | ||
| CS209 | Distributed Systems | 3-1-0-4 | CS110 | ||
| CS210 | Cloud Computing | 3-1-0-4 | CS206 | ||
| CS211 | Human Computer Interaction | 3-1-0-4 | CS206 | ||
| CS212 | Compiler Design | 3-1-0-4 | CS110 | ||
| Third Year | VII | CS301 | Artificial Intelligence and Neural Networks | 3-1-0-4 | CS204 |
| CS302 | Internet of Things | 3-1-0-4 | CS203 | ||
| CS303 | Software Testing and Quality Assurance | 3-1-0-4 | CS111 | ||
| VIII | CS304 | Advanced Machine Learning | 3-1-0-4 | CS204 | |
| CS305 | Mobile Application Development | 3-1-0-4 | CS206 | ||
| CS306 | DevOps and Continuous Integration | 3-1-0-4 | CS210 | ||
| IX | CS307 | Big Data and Analytics | 3-1-0-4 | CS207 | |
| CS308 | Blockchain Technologies | 3-1-0-4 | CS205 | ||
| CS309 | Quantitative Finance and Risk Modeling | 3-1-0-4 | CS201 | ||
| CS310 | Research Methodology | 3-1-0-4 | None | ||
| CS311 | Project Management | 3-1-0-4 | CS206 | ||
| CS312 | Capstone Project | 3-1-0-4 | CS301, CS305, CS307 |
The department also offers a range of advanced departmental electives that allow students to tailor their learning based on interests and career goals. These include:
- Deep Learning with TensorFlow: Focuses on implementing neural networks using industry-standard frameworks, covering convolutional, recurrent, and transformer architectures.
- Game Development Using Unity: Covers game design principles, scripting in C#, physics simulation, and optimization techniques for interactive media.
- Quantum Computing Fundamentals: Introduces quantum algorithms and the physical principles behind quantum computation, preparing students for future developments in the field.
- Computer Vision and Image Processing: Covers image analysis, object detection, feature extraction, and computer vision applications in autonomous vehicles and medical diagnostics.
- Neural Architecture Search: Explores automated design of neural network architectures through reinforcement learning and evolutionary algorithms.
- Autonomous Robotics: Integrates AI with robotics, covering sensor integration, control systems, and path planning for intelligent autonomous machines.
- Privacy-Preserving Machine Learning: Examines techniques for training ML models while protecting sensitive data, focusing on federated learning and differential privacy methods.
- Natural Language Generation: Focuses on generating human-like text using transformer-based models, including summarization, translation, and dialogue systems.
- Edge Computing and IoT Security: Addresses challenges in deploying secure computing solutions at the edge of networks, integrating hardware and software security mechanisms.
- Augmented Reality for Education: Applies AR technologies to educational content creation, enhancing student engagement through immersive learning experiences.
The department's philosophy on project-based learning is deeply rooted in experiential education. Students are encouraged to work on real-world projects that reflect current industry challenges and societal needs. The curriculum emphasizes iterative development cycles, collaboration, and continuous feedback from faculty and industry mentors.
Mini-projects begin in the second year, with students working in small teams to solve practical problems under faculty supervision. These projects are evaluated based on technical feasibility, innovation, presentation quality, and teamwork skills. The final-year thesis or capstone project is a significant component of the program, where students conduct independent research or develop an end-to-end solution for a complex problem.
Project selection involves a mentorship system where students propose topics aligned with faculty expertise and industry trends. Regular milestones ensure progress tracking, and final presentations are judged by both internal faculty and external industry experts.