Comprehensive Course Listing Across All Semesters
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Computing | 3-0-0-3 | - |
| 1 | CS102 | Programming in C | 3-0-0-3 | - |
| 1 | MAT101 | Calculus and Analytical Geometry | 4-0-0-4 | - |
| 1 | MAT102 | Linear Algebra | 3-0-0-3 | - |
| 1 | PHY101 | Physics for Engineers | 3-0-0-3 | - |
| 1 | CHM101 | Chemistry for Engineers | 3-0-0-3 | - |
| 1 | ENG101 | English Communication | 2-0-0-2 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS102 |
| 2 | CS202 | Object Oriented Programming | 3-0-0-3 | CS102 |
| 2 | MAT201 | Differential Equations | 3-0-0-3 | MAT101 |
| 2 | PHY201 | Modern Physics | 3-0-0-3 | PHY101 |
| 2 | CHM201 | Organic Chemistry | 3-0-0-3 | CHM101 |
| 2 | ENG201 | Technical Writing | 2-0-0-2 | - |
| 3 | CS301 | Database Management Systems | 3-0-0-3 | CS201 |
| 3 | CS302 | Operating Systems | 3-0-0-3 | CS202 |
| 3 | MAT301 | Probability and Statistics | 3-0-0-3 | MAT201 |
| 3 | CS303 | Computer Architecture | 3-0-0-3 | CS201 |
| 3 | CS304 | Software Engineering | 3-0-0-3 | CS202 |
| 3 | CS305 | Computer Networks | 3-0-0-3 | CS301 |
| 4 | CS401 | Artificial Intelligence | 3-0-0-3 | CS301 |
| 4 | CS402 | Cybersecurity | 3-0-0-3 | CS305 |
| 4 | CS403 | Web Development | 3-0-0-3 | CS202 |
| 4 | CS404 | Mobile App Development | 3-0-0-3 | CS202 |
| 4 | CS405 | Data Science and Analytics | 3-0-0-3 | CS301 |
| 4 | CS406 | Cloud Computing | 3-0-0-3 | CS305 |
| 5 | CS501 | Advanced Algorithms | 3-0-0-3 | CS201 |
| 5 | CS502 | Machine Learning | 3-0-0-3 | CS401 |
| 5 | CS503 | Network Security | 3-0-0-3 | CS402 |
| 5 | CS504 | Software Testing and Quality Assurance | 3-0-0-3 | CS404 |
| 5 | CS505 | Big Data Technologies | 3-0-0-3 | CS405 |
| 6 | CS601 | Research Methodology | 2-0-0-2 | - |
| 6 | CS602 | Capstone Project | 4-0-0-4 | CS501 |
| 7 | CS701 | Special Topics in Computer Science | 3-0-0-3 | - |
| 7 | CS702 | Internship | 4-0-0-4 | - |
| 8 | CS801 | Project Management | 3-0-0-3 | - |
| 8 | CS802 | Seminar and Thesis | 4-0-0-4 | CS602 |
Detailed Descriptions of Advanced Departmental Electives
The department offers a rich selection of advanced elective courses designed to provide depth in specialized areas. One such course is Artificial Intelligence (CS401), which covers machine learning algorithms, neural networks, and deep learning frameworks. Students learn how to design intelligent systems capable of solving complex problems using AI techniques.
Another important elective is Cybersecurity (CS402), which explores network security protocols, cryptography, intrusion detection systems, and ethical hacking. This course prepares students for careers in information security, where they can protect organizations from cyber threats.
The Web Development (CS403) elective introduces students to modern web technologies including HTML5, CSS3, JavaScript frameworks like React and Angular, RESTful APIs, and responsive design principles. Students gain hands-on experience building dynamic and scalable web applications.
Mobile App Development (CS404) focuses on creating mobile applications for iOS and Android platforms using tools like Swift, Kotlin, Flutter, and React Native. The course emphasizes user interface design, app functionality, and deployment strategies.
The Data Science and Analytics (CS405) course provides students with a comprehensive understanding of data analysis techniques, statistical modeling, and machine learning algorithms used in business intelligence and scientific research.
Cloud Computing (CS406) explores cloud infrastructure, virtualization technologies, containerization with Docker, Kubernetes orchestration, and platform services like AWS, Azure, and Google Cloud Platform. Students learn to deploy scalable applications in cloud environments.
Advanced Algorithms (CS501) delves into advanced algorithmic design and analysis, covering topics such as approximation algorithms, randomized algorithms, online algorithms, and computational complexity theory.
The Machine Learning (CS502) course builds upon foundational knowledge to explore supervised and unsupervised learning, reinforcement learning, natural language processing, computer vision, and neural network architectures.
Network Security (CS503) covers advanced security mechanisms in wired and wireless networks, including firewall configurations, VPN technologies, secure routing protocols, and network forensics.
Software Testing and Quality Assurance (CS504) introduces students to software testing methodologies, automated testing tools, quality metrics, defect tracking systems, and continuous integration pipelines.
Big Data Technologies (CS505) focuses on distributed computing frameworks like Hadoop, Spark, NoSQL databases, stream processing, and data warehousing solutions. Students learn to process large-scale datasets efficiently and effectively.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a cornerstone of engineering education. Projects are structured to mirror real-world scenarios, encouraging students to apply theoretical knowledge in practical settings. Mini-projects begin in the second year and progressively increase in complexity and scope.
Mini-projects typically involve teams of 3-5 students working under faculty supervision. These projects are evaluated based on technical execution, innovation, documentation, presentation quality, and peer collaboration. Students receive feedback from mentors throughout the project lifecycle to refine their skills.
The final-year capstone project is a significant component of the program. Students select topics aligned with current industry trends or research interests. The project involves extensive literature review, system design, implementation, testing, and documentation. Faculty members serve as advisors, ensuring that students meet academic standards while exploring creative solutions.
Students can propose projects related to any area within computer applications, provided they align with departmental guidelines. The selection process includes proposal submission, faculty evaluation, and approval from the academic committee. This ensures that all projects contribute meaningfully to student development and program outcomes.