Comprehensive Course Structure
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1st Semester | IT101 | Introduction to Programming | 3-0-0-3 | None |
| IT102 | Calculus and Analytical Geometry | 4-0-0-4 | None | |
| IT103 | Physics for Information Technology | 3-0-0-3 | None | |
| IT104 | Chemistry for IT Students | 3-0-0-3 | None | |
| IT105 | English Communication Skills | 2-0-0-2 | None | |
| IT106 | Introduction to Computer Science | 3-0-0-3 | None | |
| IT107 | Computer Lab I | 0-0-2-1 | None | |
| IT108 | Programming Lab I | 0-0-2-1 | None | |
| 2nd Semester | IT201 | Data Structures and Algorithms | 3-0-0-3 | IT101 |
| IT202 | Linear Algebra and Probability | 4-0-0-4 | IT102 | |
| IT203 | Object-Oriented Programming in Java | 3-0-0-3 | IT101 | |
| IT204 | Database Management Systems | 3-0-0-3 | IT101 | |
| IT205 | Computer Organization and Architecture | 3-0-0-3 | IT106 | |
| IT206 | Operating Systems | 3-0-0-3 | IT205 | |
| IT207 | Web Technologies | 3-0-0-3 | IT101 | |
| IT208 | Lab Sessions II | 0-0-2-1 | IT107, IT108 | |
| 3rd Semester | IT301 | Artificial Intelligence and Machine Learning | 3-0-0-3 | IT201, IT202 |
| IT302 | Network Security | 3-0-0-3 | IT205 | |
| IT303 | Software Engineering | 3-0-0-3 | IT201, IT203 | |
| IT304 | Embedded Systems | 3-0-0-3 | IT205 | |
| IT305 | Cloud Computing | 3-0-0-3 | IT206 | |
| IT306 | Internet of Things (IoT) | 3-0-0-3 | IT205, IT204 | |
| IT307 | Data Science and Analytics | 3-0-0-3 | IT202 | |
| IT308 | Lab Sessions III | 0-0-2-1 | IT208 | |
| 4th Semester | IT401 | Advanced Machine Learning | 3-0-0-3 | IT301 |
| IT402 | Cryptography and Network Security | 3-0-0-3 | IT302 | |
| IT403 | DevOps and Continuous Integration | 3-0-0-3 | IT303 | |
| IT404 | Mobile App Development | 3-0-0-3 | IT207 | |
| IT405 | Big Data Technologies | 3-0-0-3 | IT307 | |
| IT406 | User Experience Design | 3-0-0-3 | IT207 | |
| IT407 | Human-Computer Interaction | 3-0-0-3 | IT406 | |
| IT408 | Lab Sessions IV | 0-0-2-1 | IT308 | |
| 5th Semester | IT501 | Reinforcement Learning | 3-0-0-3 | IT401 |
| IT502 | Blockchain Technologies | 3-0-0-3 | IT302, IT305 | |
| IT503 | Agile Software Development | 3-0-0-3 | IT303 | |
| IT504 | Smart City Solutions | 3-0-0-3 | IT306, IT404 | |
| IT505 | Quantitative Finance | 3-0-0-3 | IT307 | |
| IT506 | Computer Vision | 3-0-0-3 | IT401 | |
| IT507 | Natural Language Processing | 3-0-0-3 | IT401 | |
| IT508 | Lab Sessions V | 0-0-2-1 | IT408 | |
| 6th Semester | IT601 | Advanced Cybersecurity | 3-0-0-3 | IT502 |
| IT602 | Edge Computing | 3-0-0-3 | IT306 | |
| IT603 | Software Architecture and Design Patterns | 3-0-0-3 | IT503 | |
| IT604 | Robotics and Automation | 3-0-0-3 | IT404 | |
| IT605 | Data Visualization Techniques | 3-0-0-3 | IT505 | |
| IT606 | Machine Learning in Practice | 3-0-0-3 | IT501 | |
| IT607 | Quantitative Risk Analysis | 3-0-0-3 | IT505 | |
| IT608 | Lab Sessions VI | 0-0-2-1 | IT508 | |
| 7th Semester | IT701 | Research Methodology | 2-0-0-2 | None |
| IT702 | Capstone Project I | 3-0-0-3 | IT601, IT605 | |
| IT703 | Internship Preparation | 2-0-0-2 | None | |
| IT704 | Advanced Topics in IT | 3-0-0-3 | IT606 | |
| IT705 | Project Management | 3-0-0-3 | IT503 | |
| IT706 | Professional Ethics in IT | 2-0-0-2 | None | |
| IT707 | Entrepreneurship in Technology | 3-0-0-3 | None | |
| IT708 | Lab Sessions VII | 0-0-2-1 | IT608 | |
| 8th Semester | IT801 | Capstone Project II | 3-0-0-3 | IT702 |
| IT802 | Industry Internship | 0-0-6-6 | IT703 | |
| IT803 | Final Thesis Proposal | 2-0-0-2 | IT701 | |
| IT804 | Thesis Writing and Presentation | 2-0-0-2 | IT803 | |
| IT805 | Recruitment Preparation | 2-0-0-2 | None | |
| IT806 | Placement and Interview Training | 2-0-0-2 | IT805 | |
| IT807 | Final Project Defense | 3-0-0-3 | IT801 | |
| IT808 | Graduation Ceremony and Alumni Networking | 0-0-0-0 | None |
Detailed Departmental Elective Courses
Advanced courses in the department are designed to provide students with in-depth knowledge and practical skills in specialized areas of Information Technology. Each course is carefully structured to meet current industry standards while encouraging innovation and critical thinking.
1. Advanced Machine Learning
This course delves into advanced topics in machine learning, including deep learning architectures, neural networks, reinforcement learning, and generative models. Students learn to implement complex algorithms using frameworks like TensorFlow, PyTorch, and Keras. The curriculum includes hands-on projects involving image recognition, natural language processing, and recommendation systems.
2. Cryptography and Network Security
This course explores modern cryptographic techniques and network security protocols used to protect digital assets. Topics include symmetric and asymmetric encryption, hash functions, digital signatures, SSL/TLS protocols, and intrusion detection systems. Students engage in lab sessions simulating real-world cyberattacks and defensive strategies.
3. DevOps and Continuous Integration
This course covers the principles and practices of DevOps culture, automation tools, containerization technologies (Docker, Kubernetes), microservices architecture, and CI/CD pipelines. Students gain experience with platforms like Jenkins, GitLab CI, GitHub Actions, and AWS CodePipeline.
4. Mobile App Development
This course focuses on developing cross-platform mobile applications using modern frameworks like Flutter, React Native, and Xamarin. Students learn UI/UX design principles, backend integration, app deployment, and testing strategies for iOS and Android platforms.
5. Big Data Technologies
This course introduces students to big data ecosystems including Hadoop, Spark, Hive, Pig, and Kafka. Topics include data ingestion, processing, storage, and visualization using tools like Tableau, Power BI, and D3.js. Students work on projects involving real-world datasets from social media, e-commerce, and financial sectors.
6. User Experience Design
This course emphasizes user-centered design principles and methods for creating intuitive digital products. Students learn to conduct usability studies, prototype interfaces, evaluate designs using heuristic evaluation, and implement accessibility standards. The curriculum includes workshops on Figma, Sketch, Adobe XD, and InVision.
7. Computer Vision
This course explores image processing techniques, object detection, facial recognition, and computer vision applications in robotics and augmented reality. Students use libraries like OpenCV, scikit-image, and TensorFlow to build real-time visual recognition systems.
8. Natural Language Processing
This course covers text mining, sentiment analysis, named entity recognition, machine translation, and conversational AI systems. Students work with NLP libraries like NLTK, spaCy, Hugging Face Transformers, and BERT models to develop intelligent language understanding applications.
9. Blockchain Technologies
This course examines blockchain architecture, smart contracts, consensus mechanisms, and decentralized applications (dApps). Students learn to build and deploy Ethereum-based dApps using Solidity, Truffle, and Remix IDEs.
10. Reinforcement Learning
This course introduces students to reinforcement learning algorithms, Markov Decision Processes (MDPs), Q-learning, policy gradients, and actor-critic methods. Students implement agents that learn optimal behaviors in simulated environments using OpenAI Gym and Stable Baselines3.
11. Edge Computing
This course explores edge computing architectures, fog computing platforms, and distributed systems for low-latency applications. Students experiment with Raspberry Pi, NVIDIA Jetson Nano, and other edge devices to build IoT applications that process data locally.
12. Robotics and Automation
This course combines hardware and software aspects of robotics, including sensor integration, control systems, path planning, and autonomous navigation. Students work with ROS (Robot Operating System) and Arduino platforms to design and program robotic systems for industrial automation.
13. Quantitative Finance
This course applies mathematical and computational methods to financial modeling, risk analysis, algorithmic trading, and derivatives pricing. Students use Python libraries like QuantLib, Pyfolio, and Zipline to simulate trading strategies and evaluate portfolio performance.
14. Data Visualization Techniques
This course teaches advanced data visualization principles using tools like D3.js, Plotly, Bokeh, and Tableau. Students learn to create interactive dashboards, animated visualizations, and storytelling with data to communicate insights effectively.
15. Machine Learning in Practice
This course bridges the gap between theory and practice by exposing students to real-world machine learning workflows. Topics include model selection, hyperparameter tuning, cross-validation, and deployment considerations. Students work on Kaggle competitions and industry-sponsored projects.
Project-Based Learning Philosophy
The department's approach to project-based learning is rooted in experiential education principles that emphasize active engagement with real-world challenges. Projects are designed to integrate theoretical knowledge with practical application, fostering critical thinking, problem-solving abilities, and teamwork skills.
Mini-Projects (First Year)
In the first year, students work on mini-projects involving basic algorithm implementation, data structures, web development, or database design. These projects are typically completed in groups of 2-3 students and serve as foundational experiences for more complex tasks ahead.
Capstone Project (Final Year)
The capstone project is a significant component of the final year curriculum, requiring students to propose, develop, and present an original solution to a real-world problem. The project must demonstrate mastery in their chosen specialization track and showcase interdisciplinary collaboration.
Project Selection Process
Students select projects based on their interests, faculty expertise, and available resources. Faculty mentors guide students through the research process, ensuring alignment with academic standards and industry relevance. Projects are evaluated based on innovation, feasibility, impact, and presentation quality.
Evaluation Criteria
Projects are assessed using rubrics that emphasize technical proficiency, creativity, documentation, teamwork, and oral presentations. Final submissions include detailed reports, code repositories, video demonstrations, and peer evaluations.