Curriculum Overview
The Computer Applications curriculum at Alakh Prakash Goyal Shimla University Shimla is meticulously structured to provide a holistic blend of theoretical knowledge and practical application. It spans eight semesters, integrating core technical subjects, departmental electives, science electives, and laboratory-based learning experiences.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-2-4 | None |
| 1 | CS102 | Mathematics I | 3-0-2-4 | None |
| 1 | CS103 | Computer Fundamentals | 3-0-2-4 | None |
| 1 | CS104 | English for Technical Communication | 3-0-2-4 | None |
| 1 | CS105 | Introduction to Data Structures | 3-0-2-4 | CS101 |
| 1 | CS106 | Lab: Programming Fundamentals | 0-0-4-2 | CS101 |
| 2 | CS201 | Data Structures & Algorithms | 3-0-2-4 | CS105 |
| 2 | CS202 | Mathematics II | 3-0-2-4 | CS102 |
| 2 | CS203 | Digital Logic Design | 3-0-2-4 | None |
| 2 | CS204 | Object-Oriented Programming | 3-0-2-4 | CS101 |
| 2 | CS205 | Database Management Systems | 3-0-2-4 | CS105 |
| 2 | CS206 | Lab: Data Structures & Algorithms | 0-0-4-2 | CS201 |
| 3 | CS301 | Operating Systems | 3-0-2-4 | CS203 |
| 3 | CS302 | Software Engineering | 3-0-2-4 | CS204 |
| 3 | CS303 | Computer Networks | 3-0-2-4 | CS203 |
| 3 | CS304 | Mathematics III | 3-0-2-4 | CS202 |
| 3 | CS305 | Web Technologies | 3-0-2-4 | CS204 |
| 3 | CS306 | Lab: Software Engineering | 0-0-4-2 | CS302 |
| 4 | CS401 | Artificial Intelligence | 3-0-2-4 | CS301 |
| 4 | CS402 | Cybersecurity Fundamentals | 3-0-2-4 | CS303 |
| 4 | CS403 | Data Science | 3-0-2-4 | CS205 |
| 4 | CS404 | Mobile Application Development | 3-0-2-4 | CS205 |
| 4 | CS405 | Project Management | 3-0-2-4 | None |
| 4 | CS406 | Lab: Mobile App Development | 0-0-4-2 | CS404 |
| 5 | CS501 | Machine Learning | 3-0-2-4 | CS401 |
| 5 | CS502 | Advanced Cybersecurity | 3-0-2-4 | CS402 |
| 5 | CS503 | Big Data Analytics | 3-0-2-4 | CS403 |
| 5 | CS504 | Human-Computer Interaction | 3-0-2-4 | CS305 |
| 5 | CS505 | Internet of Things | 3-0-2-4 | CS303 |
| 5 | CS506 | Lab: IoT Applications | 0-0-4-2 | CS505 |
| 6 | CS601 | Deep Learning | 3-0-2-4 | CS501 |
| 6 | CS602 | Cloud Computing | 3-0-2-4 | CS303 |
| 6 | CS603 | Financial Engineering | 3-0-2-4 | CS403 |
| 6 | CS604 | DevOps Practices | 3-0-2-4 | CS302 |
| 6 | CS605 | Research Methodology | 3-0-2-4 | None |
| 6 | CS606 | Lab: DevOps Implementation | 0-0-4-2 | CS604 |
| 7 | CS701 | Capstone Project I | 3-0-2-4 | CS501, CS502, CS503 |
| 7 | CS702 | Advanced Algorithms | 3-0-2-4 | CS201 |
| 7 | CS703 | Quantitative Risk Management | 3-0-2-4 | CS603 |
| 7 | CS704 | Entrepreneurship in Tech | 3-0-2-4 | None |
| 7 | CS705 | Internship | 0-0-8-0 | CS701 |
| 7 | CS706 | Lab: Capstone Project I | 0-0-4-2 | CS701 |
| 8 | CS801 | Capstone Project II | 3-0-2-4 | CS701 |
| 8 | CS802 | Advanced Topics in AI | 3-0-2-4 | CS601 |
| 8 | CS803 | Ethical Implications of Technology | 3-0-2-4 | None |
| 8 | CS804 | Leadership in Tech | 3-0-2-4 | None |
| 8 | CS805 | Final Presentation | 0-0-4-2 | CS801 |
| 8 | CS806 | Lab: Final Capstone Project | 0-0-4-2 | CS801 |
Advanced Departmental Elective Courses:
- Introduction to Artificial Intelligence: This course introduces students to fundamental concepts of AI including search algorithms, knowledge representation, reasoning systems, and machine learning basics. Students will gain hands-on experience with popular frameworks like TensorFlow and PyTorch.
- Advanced Machine Learning: Focuses on advanced topics in ML such as deep neural networks, reinforcement learning, ensemble methods, and transfer learning. Students will implement complex models using Python and build end-to-end ML pipelines.
- Cybersecurity and Ethical Hacking: Covers network security protocols, cryptography, penetration testing, digital forensics, and ethical hacking techniques. Includes lab sessions on tools like Kali Linux, Wireshark, and Metasploit.
- Data Science and Analytics: Students learn statistical analysis, data visualization, predictive modeling, and big data technologies like Hadoop and Spark. Emphasis is placed on applying these concepts to real-world datasets.
- Software Testing and Quality Assurance: Explores various testing methodologies, automation tools, test design techniques, and quality assurance practices in software development lifecycle.
- Cloud Computing Technologies: Introduces cloud architectures, virtualization, distributed systems, and services offered by major providers like AWS, Azure, and GCP. Students will deploy applications on cloud platforms using DevOps practices.
- Internet of Things (IoT) Applications: Examines IoT architecture, sensor networks, embedded systems, communication protocols, and smart city solutions. Hands-on labs involve building IoT projects using Raspberry Pi and Arduino boards.
- Mobile App Development: Covers both native and cross-platform mobile app development using frameworks like React Native, Flutter, and Xamarin. Students will develop apps for Android and iOS platforms.
- Human-Computer Interaction: Focuses on user-centered design principles, usability evaluation techniques, prototyping tools, and accessibility standards. Includes projects involving interaction design and user testing.
- DevOps Practices: Introduces continuous integration, deployment automation, containerization with Docker, orchestration with Kubernetes, and monitoring systems for scalable software delivery.
Project-Based Learning Philosophy
Our department believes that project-based learning is crucial for developing practical skills and fostering innovation among students. The curriculum incorporates both mini-projects and a final-year capstone project to ensure students apply theoretical knowledge in real-world scenarios.
Mini Projects: These are smaller-scale projects undertaken during the second and third years of study. They focus on specific technical domains such as web development, database design, algorithm implementation, or software testing. Each mini-project is assigned a mentor from faculty and evaluated based on project documentation, presentation quality, and code clarity.
Final-Year Capstone Project: This comprehensive project spans the entire eighth semester and serves as the culmination of the undergraduate experience. Students form teams to work on a significant technological challenge related to their chosen specialization track. The project must demonstrate advanced technical proficiency, innovation, and practical relevance.
Project Selection Process: Students are encouraged to propose projects aligned with their interests or identified by faculty members. Proposals undergo review by the departmental advisory committee to ensure feasibility, relevance, and academic rigor. Once approved, students receive mentorship from experienced faculty members who guide them through the implementation phase.
Evaluation Criteria: Projects are assessed based on several factors including technical execution, innovation, documentation quality, presentation effectiveness, teamwork, and adherence to deadlines. Peer reviews, faculty evaluations, and external assessments by industry experts may also be incorporated into the grading process.