Comprehensive Course Structure
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| I | CS101 | Introduction to Programming | 3-0-0-3 | - |
| I | CS102 | Mathematics for Computer Science | 4-0-0-4 | - |
| I | CS103 | Physics for Engineers | 3-0-0-3 | - |
| I | CS104 | English Communication Skills | 2-0-0-2 | - |
| I | CS105 | Introduction to Computing | 2-0-0-2 | - |
| I | CS106 | Lab: Introduction to Programming | 0-0-3-1 | - |
| II | CS201 | Data Structures and Algorithms | 4-0-0-4 | CS101 |
| II | CS202 | Discrete Mathematics | 3-0-0-3 | - |
| II | CS203 | Database Management Systems | 3-0-0-3 | CS101 |
| II | CS204 | Computer Organization and Architecture | 3-0-0-3 | - |
| II | CS205 | Object-Oriented Programming | 2-0-0-2 | CS101 |
| II | CS206 | Lab: Data Structures and Algorithms | 0-0-3-1 | CS101 |
| III | CS301 | Operating Systems | 4-0-0-4 | CS201, CS204 |
| III | CS302 | Software Engineering | 3-0-0-3 | CS201 |
| III | CS303 | Theory of Computation | 3-0-0-3 | CS202 |
| III | CS304 | Computer Networks | 3-0-0-3 | CS201 |
| III | CS305 | Web Technologies | 2-0-0-2 | CS201 |
| III | CS306 | Lab: Operating Systems | 0-0-3-1 | CS201, CS204 |
| IV | CS401 | Machine Learning | 3-0-0-3 | CS201, CS202 |
| IV | CS402 | Cybersecurity Fundamentals | 3-0-0-3 | CS201 |
| IV | CS403 | Data Science and Analytics | 3-0-0-3 | CS201, CS202 |
| IV | CS404 | Human-Computer Interaction | 2-0-0-2 | CS201 |
| IV | CS405 | Mobile Application Development | 2-0-0-2 | CS201 |
| IV | CS406 | Lab: Machine Learning | 0-0-3-1 | CS201, CS202 |
| V | CS501 | Advanced Algorithms | 3-0-0-3 | CS201 |
| V | CS502 | Distributed Systems | 3-0-0-3 | CS301, CS304 |
| V | CS503 | Database Systems | 3-0-0-3 | CS203 |
| V | CS504 | Cloud Computing | 3-0-0-3 | CS301, CS304 |
| V | CS505 | Internet of Things (IoT) | 2-0-0-2 | CS201 |
| V | CS506 | Lab: Cloud Computing | 0-0-3-1 | CS301, CS304 |
| VI | CS601 | Research Methodology | 2-0-0-2 | - |
| VI | CS602 | Capstone Project I | 4-0-0-4 | - |
| VI | CS603 | Special Topics in Computer Science | 2-0-0-2 | - |
| VI | CS604 | Professional Ethics and Communication | 2-0-0-2 | - |
| VI | CS605 | Internship Preparation | 1-0-0-1 | - |
| VI | CS606 | Lab: Capstone Project I | 0-0-3-1 | - |
| VII | CS701 | Capstone Project II | 4-0-0-4 | CS602 |
| VII | CS702 | Advanced Topics in AI | 3-0-0-3 | CS401 |
| VII | CS703 | Security Architecture and Policy | 3-0-0-3 | CS402 |
| VII | CS704 | Data Mining Techniques | 3-0-0-3 | CS403 |
| VII | CS705 | Human-Centered Design | 2-0-0-2 | CS404 |
| VII | CS706 | Lab: Capstone Project II | 0-0-3-1 | CS602 |
| VIII | CS801 | Industry Internship | 4-0-0-4 | - |
| VIII | CS802 | Final Project Presentation | 2-0-0-2 | - |
| VIII | CS803 | Entrepreneurship and Innovation | 2-0-0-2 | - |
| VIII | CS804 | Graduation Thesis | 6-0-0-6 | - |
| VIII | CS805 | Professional Development Workshop | 1-0-0-1 | - |
| VIII | CS806 | Lab: Graduation Thesis | 0-0-3-1 | - |
The advanced departmental elective courses offered in the Computer Applications program are designed to deepen student expertise and prepare them for specialized roles in industry or research. Courses like 'Advanced Algorithms' delve into complex algorithmic design patterns, while 'Distributed Systems' explores scalable system architectures used by leading tech companies.
'Security Architecture and Policy' provides students with insights into cybersecurity governance frameworks and compliance standards essential for working in regulated industries. In 'Data Mining Techniques', students learn to extract meaningful patterns from large datasets using tools like Python's scikit-learn and Apache Spark.
The course 'Human-Centered Design' focuses on user research methods, prototyping techniques, and iterative design processes that are crucial for product development teams at companies like Apple, Google, and Microsoft. 'Cloud Computing' introduces students to cloud platforms such as AWS, Azure, and GCP, enabling them to build scalable applications.
'Advanced Topics in AI' covers reinforcement learning, computer vision, and natural language processing using TensorFlow and PyTorch frameworks. These advanced courses are taught by faculty members who are actively involved in research projects funded by government agencies and private investors.
Project-based learning is central to our philosophy at Indrashil University Mehsana. Students begin working on mini-projects in their second year, choosing topics related to emerging technologies or societal challenges. These projects are evaluated based on innovation, feasibility, and impact potential.
The final-year thesis/capstone project allows students to apply everything they have learned in a real-world scenario. Projects can be individual or team-based and often involve collaboration with industry partners or research institutions. Students select their projects through a formal proposal process where they present their ideas to faculty mentors who guide them throughout the development cycle.