Comprehensive Course Structure
The curriculum for the Computer Applications program at Ims Unison University Dehradun is designed to provide a balanced mix of foundational science, core engineering principles, and advanced specializations. The program spans 8 semesters over four years, with each semester building upon the previous one to ensure a progressive learning experience.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Computing | 3-0-0-3 | - |
| 1 | PH101 | Physics for Computer Applications | 3-0-0-3 | - |
| 1 | CH101 | Chemistry for Engineering | 3-0-0-3 | - |
| 1 | MA101 | Mathematics I | 4-0-0-4 | - |
| 1 | EE101 | Basic Electrical Engineering | 3-0-0-3 | - |
| 1 | CS102 | Programming in C | 3-0-2-4 | - |
| 1 | CS103 | Computer Organization | 3-0-0-3 | - |
| 1 | PH102 | Physics Lab | 0-0-2-2 | - |
| 1 | CH102 | Chemistry Lab | 0-0-2-2 | - |
| 1 | MA102 | Mathematics II | 4-0-0-4 | MA101 |
| 1 | CS104 | Computer Lab I | 0-0-2-2 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS102 |
| 2 | CS202 | Database Management Systems | 3-0-0-3 | CS101 |
| 2 | CS203 | Computer Networks | 3-0-0-3 | CS101 |
| 2 | CS204 | Operating Systems | 3-0-0-3 | CS101 |
| 2 | CS205 | Web Technologies | 3-0-0-3 | CS102 |
| 2 | CS206 | Mathematics III | 4-0-0-4 | MA102 |
| 2 | CS207 | Software Engineering | 3-0-0-3 | - |
| 2 | CS208 | Computer Lab II | 0-0-2-2 | CS104 |
| 2 | MA201 | Probability and Statistics | 3-0-0-3 | MA102 |
| 3 | CS301 | Artificial Intelligence | 3-0-0-3 | CS201 |
| 3 | CS302 | Cybersecurity Fundamentals | 3-0-0-3 | CS203 |
| 3 | CS303 | Cloud Computing | 3-0-0-3 | CS201 |
| 3 | CS304 | Data Mining and Big Data Analytics | 3-0-0-3 | MA201 |
| 3 | CS305 | Internet of Things | 3-0-0-3 | CS203 |
| 3 | CS306 | Mobile Application Development | 3-0-0-3 | CS205 |
| 3 | CS307 | User Interface Design | 3-0-0-3 | CS201 |
| 3 | CS308 | Computer Lab III | 0-0-2-2 | CS208 |
| 4 | CS401 | Machine Learning | 3-0-0-3 | CS301 |
| 4 | CS402 | Digital Forensics | 3-0-0-3 | CS302 |
| 4 | CS403 | DevOps and CI/CD | 3-0-0-3 | CS303 |
| 4 | CS404 | Advanced Data Analytics | 3-0-0-3 | CS304 |
| 4 | CS405 | Embedded Systems | 3-0-0-3 | CS305 |
| 4 | CS406 | Human-Machine Interaction | 3-0-0-3 | CS307 |
| 4 | CS407 | Software Project Management | 3-0-0-3 | CS207 |
| 4 | CS408 | Capstone Project | 0-0-6-6 | - |
| 5 | CS501 | Deep Learning Architectures | 3-0-0-3 | CS401 |
| 5 | CS502 | Network Security and Penetration Testing | 3-0-0-3 | CS402 |
| 5 | CS503 | Blockchain Technology | 3-0-0-3 | CS303 |
| 5 | CS504 | Natural Language Processing | 3-0-0-3 | CS401 |
| 5 | CS505 | Advanced IoT Applications | 3-0-0-3 | CS405 |
| 5 | CS506 | Mobile App Security | 3-0-0-3 | CS306 |
| 5 | CS507 | UX Research and Prototyping | 3-0-0-3 | CS406 |
| 5 | CS508 | Research Methodology | 0-0-2-2 | - |
| 6 | CS601 | Reinforcement Learning | 3-0-0-3 | CS501 |
| 6 | CS602 | Cryptography and Network Security | 3-0-0-3 | CS502 |
| 6 | CS603 | Cloud Native Applications | 3-0-0-3 | CS503 |
| 6 | CS604 | Big Data Visualization | 3-0-0-3 | CS504 |
| 6 | CS605 | Smart Cities and IoT | 3-0-0-3 | CS505 |
| 6 | CS606 | Advanced Mobile Development | 3-0-0-3 | CS506 |
| 6 | CS607 | User Experience Testing | 3-0-0-3 | CS507 |
| 6 | CS608 | Capstone Project Lab | 0-0-2-2 | CS408 |
| 7 | CS701 | Quantum Computing Fundamentals | 3-0-0-3 | CS601 |
| 7 | CS702 | Security Policy and Governance | 3-0-0-3 | CS602 |
| 7 | CS703 | Advanced Cloud Security | 3-0-0-3 | CS603 |
| 7 | CS704 | AI Ethics and Responsible AI | 3-0-0-3 | CS604 |
| 7 | CS705 | Edge Computing Applications | 3-0-0-3 | CS605 |
| 7 | CS706 | Mobile Application Performance Optimization | 3-0-0-3 | CS606 |
| 7 | CS707 | Design Thinking for Product Innovation | 3-0-0-3 | CS607 |
| 7 | CS708 | Internship Preparation | 0-0-2-2 | - |
| 8 | CS801 | Final Year Project | 0-0-6-6 | CS708 |
| 8 | CS802 | Industry Internship | 0-0-4-4 | - |
| 8 | CS803 | Capstone Presentation | 0-0-2-2 | CS801 |
| 8 | CS804 | Final Thesis Writing | 0-0-2-2 | CS708 |
Advanced Departmental Elective Courses
The department offers a wide range of advanced elective courses that allow students to specialize and deepen their knowledge in specific areas. These courses are designed to align with current industry trends and research advancements.
- Deep Learning Architectures: This course explores the architecture and implementation of deep neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students learn to design and train models for image recognition, natural language processing, and generative modeling.
- Network Security and Penetration Testing: This course focuses on identifying vulnerabilities in network infrastructure and implementing robust security measures. It covers topics such as firewall configuration, intrusion detection systems (IDS), penetration testing tools, and compliance frameworks.
- Blockchain Technology: Students learn about blockchain fundamentals, smart contracts, decentralized applications (dApps), consensus mechanisms, and cryptocurrency integration. The course includes hands-on labs using Ethereum, Hyperledger Fabric, and other platforms.
- Natural Language Processing: This elective delves into language modeling, sentiment analysis, machine translation, and text generation. It covers state-of-the-art NLP techniques like BERT, GPT, and T5, along with practical applications in chatbots, voice assistants, and content moderation.
- Advanced IoT Applications: The course explores real-world deployment scenarios for IoT systems, including sensor fusion, edge computing integration, and secure communication protocols. Students work on projects involving smart agriculture, industrial automation, and healthcare monitoring.
- Mobile App Security: This course examines the security challenges specific to mobile platforms, including app store security, runtime protection, data encryption, and vulnerability assessment tools. It includes practical labs on Android and iOS security testing.
- User Experience Testing: Students learn to conduct usability studies, perform heuristic evaluations, gather user feedback, and iterate designs based on data-driven insights. The course emphasizes accessibility standards and inclusive design principles.
- Quantum Computing Fundamentals: Introduces quantum algorithms, qubit manipulation, superposition, entanglement, and quantum error correction. It includes simulations using IBM Qiskit and Microsoft Q# environments to explore quantum applications in optimization and cryptography.
- Security Policy and Governance: Focuses on developing comprehensive security policies, regulatory compliance frameworks, risk assessment methodologies, and governance structures for enterprise environments. Students study frameworks like ISO 27001, NIST Cybersecurity Framework, and GDPR.
- Advanced Cloud Security: Covers cloud-native security architectures, identity and access management (IAM), container security, and multi-cloud deployment strategies. It includes labs on securing AWS, Azure, and GCP environments using industry best practices.
Project-Based Learning Philosophy
The department emphasizes project-based learning as a core component of the curriculum. This approach ensures that students apply theoretical knowledge to real-world problems, fostering innovation, teamwork, and critical thinking skills.
Mini-Projects (Years 1–3)
In the early years, students undertake mini-projects to reinforce concepts learned in class. These projects are typically completed within a semester and involve small teams working on assigned tasks under faculty supervision. Topics vary by semester and include web development, data analysis, algorithm implementation, and system design.
Final-Year Thesis/Capstone Project
The capstone project is the culmination of a student's academic journey. Students select a topic aligned with their interests and career goals, often collaborating with industry partners or research groups. The project involves extensive literature review, experimental design, implementation, testing, documentation, and presentation. Faculty mentors guide students throughout the process, ensuring they meet academic standards while exploring innovative solutions.
Project Selection Process
Students can propose their own project ideas or choose from a list of faculty-generated topics. Proposals are reviewed by departmental committees, which ensure alignment with curriculum objectives and resource availability. Once selected, students are paired with suitable mentors who provide guidance on scope, methodology, timeline, and evaluation criteria.