Comprehensive Course Structure
| Semester | Course Code | Course Title | Credits (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 | Engineering Graphics and Design | 2-0-0-2 | - |
| I | CS104 | Physics for Computer Science | 3-0-0-3 | - |
| I | CS105 | Chemistry for Computer Science | 3-0-0-3 | - |
| I | CS106 | English for Technical Communication | 2-0-0-2 | - |
| I | CS107 | Introduction to Data Structures | 3-0-0-3 | CS101 |
| I | CS108 | Basics of Computer Organization | 3-0-0-3 | - |
| I | CS109 | Programming Lab | 0-0-4-2 | CS101 |
| I | CS110 | Data Structures Lab | 0-0-4-2 | CS107 |
| II | CS201 | Discrete Mathematics | 3-0-0-3 | CS102 |
| II | CS202 | Algorithms and Complexity | 3-0-0-3 | CS107 |
| II | CS203 | Database Management Systems | 3-0-0-3 | CS107 |
| II | CS204 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| II | CS205 | Computer Networks | 3-0-0-3 | CS108 |
| II | CS206 | Operating Systems | 3-0-0-3 | CS108 |
| II | CS207 | Computer Architecture | 3-0-0-3 | CS108 |
| II | CS208 | Software Engineering | 3-0-0-3 | CS104 |
| II | CS209 | Object-Oriented Programming Lab | 0-0-4-2 | CS204 |
| II | CS210 | Database Lab | 0-0-4-2 | CS203 |
| III | CS301 | Design and Analysis of Algorithms | 3-0-0-3 | CS202 |
| III | CS302 | Artificial Intelligence and Machine Learning | 3-0-0-3 | CS202 |
| III | CS303 | Cybersecurity Fundamentals | 3-0-0-3 | CS205 |
| III | CS304 | Data Mining and Warehousing | 3-0-0-3 | CS203 |
| III | CS305 | Web Technologies and Applications | 3-0-0-3 | CS204 |
| III | CS306 | Mobile Computing | 3-0-0-3 | CS205 |
| III | CS307 | Human Computer Interaction | 3-0-0-3 | CS208 |
| III | CS308 | Database Systems Lab | 0-0-4-2 | CS304 |
| III | CS309 | AI and ML Lab | 0-0-4-2 | CS302 |
| IV | CS401 | Advanced Software Engineering | 3-0-0-3 | CS208 |
| IV | CS402 | Distributed Systems | 3-0-0-3 | CS205 |
| IV | CS403 | Cloud Computing | 3-0-0-3 | CS206 |
| IV | CS404 | Computer Vision and Image Processing | 3-0-0-3 | CS302 |
| IV | CS405 | Internet of Things (IoT) | 3-0-0-3 | CS206 |
| IV | CS406 | Game Development | 3-0-0-3 | CS205 |
| IV | CS407 | Blockchain Technology | 3-0-0-3 | CS205 |
| IV | CS408 | Quantitative Finance and Algorithmic Trading | 3-0-0-3 | CS304 |
| IV | CS409 | Distributed Systems Lab | 0-0-4-2 | CS402 |
| IV | CS410 | Capstone Project Lab | 0-0-6-3 | All previous courses |
Advanced Departmental Electives
Advanced departmental electives are designed to provide specialized knowledge and practical skills in emerging areas of computer science. These courses allow students to tailor their education according to personal interests and career goals.
Artificial Intelligence and Machine Learning
This course explores the fundamentals of AI and ML, covering supervised learning, unsupervised learning, neural networks, deep learning architectures, natural language processing, computer vision, reinforcement learning, and ethical considerations in AI development. Students will implement real-world applications using Python frameworks like TensorFlow and PyTorch.
Cybersecurity Fundamentals
Students learn about network security threats, cryptographic protocols, penetration testing, incident response, digital forensics, and risk management strategies. The course emphasizes hands-on labs with tools like Wireshark, Metasploit, and Kali Linux for practical experience.
Data Mining and Warehousing
This course introduces techniques for extracting patterns from large datasets, including clustering, classification, association rule mining, anomaly detection, and data warehousing concepts. Students gain proficiency in SQL, Python, and machine learning libraries for data analysis.
Web Technologies and Applications
The curriculum covers modern web development frameworks like React, Angular, Node.js, and Express, along with database integration, RESTful APIs, authentication mechanisms, and responsive design principles. Students build full-stack applications during lab sessions.
Mobile Computing
Students explore mobile platform development using Android Studio and iOS Swift frameworks. Topics include mobile app architecture, user interface design, location-based services, cloud integration, and cross-platform development using React Native or Flutter.
Human Computer Interaction
This course focuses on designing interfaces that enhance usability and accessibility. Students learn about user research methods, prototyping techniques, usability testing, interaction design principles, and emerging technologies like VR/AR interfaces for immersive experiences.
Computer Vision and Image Processing
Students study image processing algorithms, object detection, facial recognition, segmentation techniques, and convolutional neural networks. Practical labs involve using OpenCV and deep learning frameworks to build computer vision systems.
Internet of Things (IoT)
This course delves into IoT architecture, sensor technologies, embedded programming, wireless communication protocols, cloud integration, edge computing, and smart city applications. Students work with Raspberry Pi and Arduino platforms in lab settings.
Game Development
Students learn game design principles, 3D modeling, animation techniques, physics simulation, and engine architecture using Unity or Unreal Engine. Labs focus on creating interactive experiences across multiple platforms.
Blockchain Technology
This course covers blockchain fundamentals, smart contracts, consensus mechanisms, cryptocurrency systems, decentralized applications (dApps), and enterprise blockchain implementations. Students implement blockchain solutions using Ethereum and Hyperledger frameworks.
Quantitative Finance and Algorithmic Trading
Students study financial modeling, portfolio optimization, derivatives pricing, quantitative trading strategies, risk management, and algorithmic execution. The course integrates Python libraries for financial data analysis and backtesting trading algorithms.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is rooted in the belief that active engagement with real-world problems enhances learning outcomes. Projects are structured to mirror industry challenges, encouraging creativity, teamwork, and innovation.
Mini-projects begin in the second year, allowing students to apply theoretical concepts in practical scenarios. These projects involve small teams working under faculty supervision to develop prototypes or solve specific technical issues.
The final-year capstone project is a comprehensive endeavor that integrates all learned knowledge. Students select topics aligned with their specializations and collaborate closely with faculty mentors throughout the process.
Evaluation criteria include technical execution, creativity, presentation quality, documentation standards, and demonstration of problem-solving capabilities. Projects are assessed through peer reviews, mentor evaluations, and public presentations.
Faculty mentors are assigned based on student interests and project scope. Mentors provide guidance on methodology, timeline management, and resource allocation to ensure successful completion of projects.