Comprehensive Course Structure
| Semester | Course Code | Full Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | CS102 | Mathematics for Computer Applications | 4-0-0-4 | - |
| 1 | CS103 | Computer Organization and Architecture | 3-0-0-3 | - |
| 1 | CS104 | Problem Solving using Algorithms | 3-0-0-3 | - |
| 1 | CS105 | Introduction to Data Structures | 3-0-0-3 | - |
| 1 | CS106 | Computer Lab I | 0-0-2-1 | - |
| 2 | CS201 | Object-Oriented Programming with Java | 3-0-0-3 | CS101 |
| 2 | CS202 | Calculus and Linear Algebra | 4-0-0-4 | - |
| 2 | CS203 | Database Management Systems | 3-0-0-3 | CS105 |
| 2 | CS204 | Digital Logic and Computer Design | 3-0-0-3 | CS103 |
| 2 | CS205 | Operating Systems | 3-0-0-3 | CS103 |
| 2 | CS206 | Computer Lab II | 0-0-2-1 | CS106 |
| 3 | CS301 | Data Structures and Algorithms | 3-0-0-3 | CS205 |
| 3 | CS302 | Software Engineering | 3-0-0-3 | CS201 |
| 3 | CS303 | Computer Networks | 3-0-0-3 | CS204 |
| 3 | CS304 | Web Technologies | 3-0-0-3 | CS201 |
| 3 | CS305 | Mobile Computing | 3-0-0-3 | CS201 |
| 3 | CS306 | Computer Lab III | 0-0-2-1 | CS206 |
| 4 | CS401 | Advanced Algorithms | 3-0-0-3 | CS301 |
| 4 | CS402 | Artificial Intelligence | 3-0-0-3 | CS301 |
| 4 | CS403 | Cybersecurity Fundamentals | 3-0-0-3 | CS205 |
| 4 | CS404 | Database Systems | 3-0-0-3 | CS203 |
| 4 | CS405 | Cloud Computing | 3-0-0-3 | CS303 |
| 4 | CS406 | Computer Lab IV | 0-0-2-1 | CS306 |
| 5 | CS501 | Machine Learning | 3-0-0-3 | CS402 |
| 5 | CS502 | Data Mining and Analytics | 3-0-0-3 | CS404 |
| 5 | CS503 | Human Computer Interaction | 3-0-0-3 | CS304 |
| 5 | CS504 | Internet of Things | 3-0-0-3 | CS305 |
| 5 | CS505 | Research Methodology | 2-0-0-2 | - |
| 5 | CS506 | Mini Project I | 0-0-4-2 | - |
| 6 | CS601 | Advanced Web Development | 3-0-0-3 | CS405 |
| 6 | CS602 | Big Data Technologies | 3-0-0-3 | CS502 |
| 6 | CS603 | Network Security | 3-0-0-3 | CS403 |
| 6 | CS604 | Embedded Systems | 3-0-0-3 | CS504 |
| 6 | CS605 | Mini Project II | 0-0-4-2 | CS506 |
| 7 | CS701 | Capstone Project | 0-0-8-4 | - |
| 7 | CS702 | Elective I | 3-0-0-3 | - |
| 7 | CS703 | Elective II | 3-0-0-3 | - |
| 7 | CS704 | Elective III | 3-0-0-3 | - |
| 8 | CS801 | Internship | 0-0-6-3 | - |
| 8 | CS802 | Final Year Project | 0-0-10-6 | - |
Advanced Departmental Elective Courses
The department offers a range of advanced elective courses that enable students to specialize in emerging areas of technology:
Machine Learning
This course provides an in-depth exploration of machine learning algorithms and their applications. Students learn supervised, unsupervised, and reinforcement learning techniques through theoretical lectures and practical implementations using Python and TensorFlow. The course emphasizes real-world case studies involving image recognition, natural language processing, and predictive analytics.
Data Mining and Analytics
Focused on extracting valuable insights from large datasets, this course covers data preprocessing, clustering, classification, association rule mining, and anomaly detection. Students gain hands-on experience with tools like Apache Spark and Hadoop to process big data efficiently.
Human Computer Interaction
This elective explores the design and evaluation of user interfaces and experiences. Students study cognitive psychology principles, usability testing methodologies, prototyping techniques, and accessibility standards. The course includes projects where students design and test interfaces for various domains including healthcare, education, and entertainment.
Internet of Things
Students explore IoT architectures, sensor networks, embedded systems programming, wireless communication protocols, and smart city applications. Through lab sessions, they develop IoT-based solutions using platforms like Arduino, Raspberry Pi, and NodeMCU.
Advanced Web Development
This course builds upon foundational web technologies to explore modern frameworks like React.js, Angular, Vue.js, and server-side rendering techniques. Students learn full-stack development practices, RESTful APIs, and deployment strategies for scalable web applications.
Big Data Technologies
Students are introduced to distributed computing frameworks such as Hadoop, Spark, Kafka, and NoSQL databases. The course covers data streaming, real-time processing, and cloud-based big data solutions using AWS and Google Cloud Platform services.
Network Security
This advanced elective delves into network security threats, cryptographic techniques, firewall configurations, intrusion detection systems, and secure network design principles. Students conduct penetration testing exercises in controlled environments to understand vulnerabilities.
Embedded Systems
Students study microcontroller architectures, real-time operating systems, embedded C programming, hardware-software co-design, and IoT applications. Projects involve designing embedded systems for robotics, automation, and sensor-based monitoring.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a means to bridge the gap between theory and practice. The curriculum includes mandatory mini-projects throughout the program, culminating in a comprehensive final-year thesis or capstone project.
Mini Projects
Mini projects are assigned in the fifth semester, allowing students to apply concepts learned in earlier semesters. These projects typically last 6 weeks and require students to form teams of 3-5 members. Each team selects a domain-specific problem from industry partners or faculty research areas.
Final Year Thesis/Capstone Project
The final year project is a significant component where students work under the guidance of a faculty mentor on an original research topic or an industry-sponsored problem. The project spans 10 weeks and involves extensive literature review, design phase, implementation, testing, and documentation.
Project Selection Process
Students can propose their own project ideas or choose from a list of faculty-approved topics. The selection process involves submitting a proposal outlining objectives, methodology, timeline, and expected outcomes. Faculty mentors are matched based on expertise alignment and student interest.
Evaluation Criteria
Projects are evaluated based on innovation, technical complexity, documentation quality, presentation skills, peer feedback, and final deliverables. The evaluation includes interim reviews, milestone assessments, and a final demonstration session where students present their work to an expert panel.