Curriculum Overview for B.Tech Computer Science
The Computer Science curriculum at Shivalik College Of Engineering is designed to provide a balanced mix of foundational knowledge and advanced specialization, preparing students for both industry roles and higher education. The program spans four years (eight semesters), with each semester comprising core courses, departmental electives, science electives, and laboratory sessions.
| Year | Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|---|
| First Year | I | CS101 | Introduction to Programming | 3-0-0-2 | - |
| CS102 | Mathematics for Computing | 3-0-0-2 | - | ||
| CS103 | Computer Organization | 3-0-0-2 | - | ||
| CS104 | Introduction to Data Structures | 3-0-0-2 | - | ||
| First Year | II | CS105 | Object-Oriented Programming | 3-0-0-2 | CS101 |
| CS106 | Calculus and Differential Equations | 3-0-0-2 | - | ||
| CS107 | Digital Logic Design | 3-0-0-2 | - | ||
| CS108 | Database Management Systems | 3-0-0-2 | - | ||
| Second Year | III | CS201 | Data Structures and Algorithms | 3-0-0-2 | CS104 |
| CS202 | Operating Systems | 3-0-0-2 | CS105 | ||
| CS203 | Computer Networks | 3-0-0-2 | CS107 | ||
| CS204 | Software Engineering | 3-0-0-2 | CS105 | ||
| Second Year | IV | CS205 | Computer Architecture | 3-0-0-2 | CS107 |
| CS206 | Probability and Statistics | 3-0-0-2 | - | ||
| CS207 | Web Technologies | 3-0-0-2 | CS105 | ||
| CS208 | Human-Computer Interaction | 3-0-0-2 | - | ||
| Third Year | V | CS301 | Machine Learning | 3-0-0-2 | CS201, CS206 |
| CS302 | Cryptography and Network Security | 3-0-0-2 | CS203 | ||
| CS303 | Data Mining and Analytics | 3-0-0-2 | CS206 | ||
| CS304 | Advanced Computer Architecture | 3-0-0-2 | CS205 | ||
| Third Year | VI | CS305 | Cloud Computing | 3-0-0-2 | CS203 |
| CS306 | DevOps and Continuous Integration | 3-0-0-2 | CS201 | ||
| CS307 | Mobile Application Development | 3-0-0-2 | CS105 | ||
| CS308 | Internet of Things (IoT) | 3-0-0-2 | CS107 | ||
| Fourth Year | VII | CS401 | Capstone Project I | 0-0-3-3 | - |
| CS402 | Research Methodology | 3-0-0-2 | - | ||
| CS403 | Advanced Topics in AI | 3-0-0-2 | CS301 | ||
| CS404 | Specialized Electives | 3-0-0-2 | - | ||
| Fourth Year | VIII | CS405 | Capstone Project II | 0-0-6-6 | CS401 |
| CS406 | Internship | 0-0-0-3 | - | ||
| CS407 | Professional Ethics | 3-0-0-2 | - | ||
| CS408 | Advanced Software Engineering | 3-0-0-2 | CS204 |
The department places significant emphasis on project-based learning, which is integrated throughout the curriculum. Students begin with small-scale projects in their first year and progress to larger, more complex endeavors by the final year. Mini-projects are typically completed during the second and third years, while the final-year capstone project spans both semesters VII and VIII.
Advanced Departmental Electives
Departmental electives offer students the opportunity to explore specialized areas within computer science in greater depth. These courses are designed to complement core subjects and provide students with niche skills that enhance their employability and research potential.
- Deep Learning and Neural Networks: This course covers convolutional neural networks, recurrent neural networks, transformers, and reinforcement learning. Students learn to implement models using TensorFlow and PyTorch and apply them to image classification, natural language processing, and game-playing scenarios.
- Blockchain Technology and Smart Contracts: The course explores the principles of blockchain technology, cryptographic hashing, distributed consensus mechanisms, and smart contract development. Students work on building decentralized applications (dApps) using Ethereum and Hyperledger frameworks.
- Computer Vision and Image Processing: This elective introduces students to image segmentation, object detection, facial recognition, and medical imaging techniques. It includes hands-on labs with OpenCV and MATLAB.
- Quantum Computing Fundamentals: Students learn the basics of quantum mechanics, qubits, superposition, entanglement, and quantum algorithms. The course includes simulations using IBM Qiskit and Google Cirq.
- Robotics and Autonomous Systems: This course combines hardware and software aspects of robotics, including sensor integration, control systems, path planning, and autonomous navigation. Students build and program robots using ROS (Robot Operating System).
- Network Security and Penetration Testing: The course focuses on identifying vulnerabilities in network infrastructure and conducting ethical hacking exercises. Students gain experience with tools like Nmap, Metasploit, Wireshark, and Burp Suite.
- Human-Centered Design for Digital Products: This elective emphasizes user research, prototyping, usability testing, and iterative design processes. Students work on real-world projects in collaboration with industry partners.
- Big Data Technologies: The course covers Hadoop ecosystem, Spark, Kafka, and NoSQL databases. Students learn to process and analyze large datasets using distributed computing frameworks.
Project-Based Learning Philosophy
The department believes that project-based learning is essential for developing practical skills and reinforcing theoretical knowledge. Projects are structured to encourage teamwork, innovation, and real-world application. Mini-projects in the second year focus on building small applications or solving specific problems using available tools and techniques.
By the third year, students are expected to work on more complex projects that involve multiple technologies and domains. These projects often result in patents, publications, or startup ideas. The final-year capstone project is a comprehensive endeavor that requires students to identify a problem, design a solution, implement it, and present findings to faculty and industry experts.
Students select their projects in consultation with faculty mentors based on personal interest, available resources, and relevance to current industry trends. Each student is assigned a dedicated mentor who guides them through the research process, provides feedback, and ensures that they meet academic and professional standards.