Comprehensive Course Structure
The Computer Applications program at Roorkee College Of Management And Computer Applications Roorkee is structured over 8 semesters, with a carefully curated mix of core subjects, departmental electives, science electives, and laboratory sessions. This structure ensures that students gain both foundational knowledge and specialized expertise.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MATH101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | PHYS101 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CHEM101 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | CS101 | Introduction to Programming | 3-1-0-4 | - |
| 1 | ENG101 | English for Engineers | 2-0-0-2 | - |
| 1 | PHY101 | Physics Lab | 0-0-3-1 | - |
| 1 | CHEM101 | Chemistry Lab | 0-0-3-1 | - |
| 2 | MATH201 | Engineering Mathematics II | 3-1-0-4 | MATH101 |
| 2 | CS201 | Data Structures and Algorithms | 3-1-0-4 | CS101 |
| 2 | CS202 | Object-Oriented Programming | 3-1-0-4 | CS101 |
| 2 | CS203 | Database Management Systems | 3-1-0-4 | CS101 |
| 2 | CS204 | Computer Organization | 3-1-0-4 | - |
| 2 | CS205 | Discrete Mathematics | 3-1-0-4 | MATH101 |
| 2 | CS206 | Lab: Data Structures and Algorithms | 0-0-3-1 | CS101 |
| 3 | CS301 | Operating Systems | 3-1-0-4 | CS201 |
| 3 | CS302 | Computer Networks | 3-1-0-4 | CS204 |
| 3 | CS303 | Software Engineering | 3-1-0-4 | CS201 |
| 3 | CS304 | Artificial Intelligence | 3-1-0-4 | CS201 |
| 3 | CS305 | Machine Learning | 3-1-0-4 | CS201 |
| 3 | CS306 | Lab: Software Engineering | 0-0-3-1 | CS201 |
| 4 | CS401 | Cybersecurity | 3-1-0-4 | CS201 |
| 4 | CS402 | Big Data Technologies | 3-1-0-4 | CS301 |
| 4 | CS403 | Cloud Computing | 3-1-0-4 | CS201 |
| 4 | CS404 | Mobile Application Development | 3-1-0-4 | CS201 |
| 4 | CS405 | Internet of Things | 3-1-0-4 | CS201 |
| 4 | CS406 | Lab: Cloud Computing | 0-0-3-1 | CS201 |
| 5 | CS501 | Advanced Machine Learning | 3-1-0-4 | CS305 |
| 5 | CS502 | Deep Learning | 3-1-0-4 | CS305 |
| 5 | CS503 | Data Visualization | 3-1-0-4 | CS305 |
| 5 | CS504 | Network Security | 3-1-0-4 | CS202 |
| 5 | CS505 | Reinforcement Learning | 3-1-0-4 | CS305 |
| 5 | CS506 | Lab: Deep Learning | 0-0-3-1 | CS305 |
| 6 | CS601 | Advanced Cybersecurity | 3-1-0-4 | CS401 |
| 6 | CS602 | Blockchain Technologies | 3-1-0-4 | CS305 |
| 6 | CS603 | Computer Vision | 3-1-0-4 | CS305 |
| 6 | CS604 | Natural Language Processing | 3-1-0-4 | CS305 |
| 6 | CS605 | Human-Computer Interaction | 3-1-0-4 | CS201 |
| 6 | CS606 | Lab: Natural Language Processing | 0-0-3-1 | CS305 |
| 7 | CS701 | Research Methodology | 3-1-0-4 | - |
| 7 | CS702 | Capstone Project I | 3-1-0-4 | CS305 |
| 7 | CS703 | Advanced Software Design | 3-1-0-4 | CS303 |
| 7 | CS704 | Quantitative Finance | 3-1-0-4 | MATH201 |
| 7 | CS705 | Special Topics in AI | 3-1-0-4 | CS305 |
| 7 | CS706 | Lab: Capstone Project I | 0-0-3-1 | - |
| 8 | CS801 | Capstone Project II | 3-1-0-4 | CS702 |
| 8 | CS802 | Internship | 3-1-0-4 | - |
| 8 | CS803 | Entrepreneurship | 3-1-0-4 | - |
| 8 | CS804 | Professional Ethics | 2-0-0-2 | - |
| 8 | CS805 | Final Project Presentation | 3-1-0-4 | CS702 |
| 8 | CS806 | Lab: Final Project | 0-0-3-1 | - |
Advanced Departmental Elective Courses
Departmental electives play a crucial role in allowing students to explore specialized areas within Computer Applications. These courses are designed to provide in-depth knowledge and practical skills relevant to emerging technologies and industry trends.
One such course is Advanced Machine Learning, which delves into advanced topics such as reinforcement learning, deep generative models, and neural architecture search. Students learn how to implement complex algorithms using frameworks like TensorFlow and PyTorch, gaining hands-on experience in building scalable machine learning systems.
Another elective is Deep Learning, where students study various architectures including CNNs, RNNs, and Transformers. The course includes practical sessions on image recognition, sequence modeling, and natural language understanding. Students work on real-world datasets to apply theoretical concepts and develop innovative solutions.
Data Visualization is an essential skill for data scientists and analysts. This course teaches students how to create compelling visualizations using tools like Tableau, D3.js, and Plotly. Through hands-on projects, students learn to communicate complex data insights effectively, making them valuable assets in any organization.
Network Security explores modern threats and defense mechanisms in networked environments. Students study concepts like firewalls, intrusion detection systems, and secure protocols. The course includes practical labs where students simulate attacks and defend against them using industry-standard tools like Wireshark and Metasploit.
Reinforcement Learning introduces students to decision-making processes in complex environments. They learn algorithms such as Q-learning, policy gradients, and actor-critic methods. Real-world applications include robotics control, game playing, and autonomous systems.
Computer Vision focuses on image processing techniques and object detection algorithms. Students learn about convolutional neural networks, feature extraction, and image segmentation. Projects involve building systems for facial recognition, medical imaging analysis, and autonomous vehicle navigation.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is rooted in the belief that hands-on experience enhances conceptual understanding and develops practical skills essential for professional success. The curriculum includes mandatory mini-projects and a final-year thesis/capstone project to ensure students gain comprehensive experience.
Mini-projects are integrated into core courses throughout the program, allowing students to apply theoretical knowledge in real-world scenarios. These projects are evaluated based on innovation, implementation quality, and presentation skills. Students often collaborate with peers from different disciplines, fostering interdisciplinary problem-solving abilities.
The final-year capstone project is a significant component of the program, requiring students to work under the guidance of experienced faculty mentors. Projects are selected based on student interests, industry relevance, and available resources. The evaluation criteria include project scope, technical depth, originality, and overall impact. Students must present their projects to a panel of experts, including faculty members and industry professionals.
Faculty mentors are chosen based on their expertise in relevant areas and availability to guide students through the project lifecycle. The mentorship process involves regular meetings, feedback sessions, and progress reviews. This ensures that students receive continuous support and guidance throughout their project journey.