Course Structure Overview
The Computer Science program at Arunodaya University Papum Pare spans four years, divided into eight semesters. Each semester carries a specific set of core courses, departmental electives, science electives, and laboratory sessions designed to build both theoretical knowledge and practical skills.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | CS102 | Mathematics for Computer Science | 4-0-0-4 | - |
| 1 | CS103 | Physics for Engineers | 3-0-0-3 | - |
| 1 | CS104 | Chemistry & Biology for Engineers | 3-0-0-3 | - |
| 1 | CS105 | Communication Skills | 2-0-0-2 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | CS202 | Database Management Systems | 3-0-0-3 | CS101 |
| 2 | CS203 | Operating Systems | 3-0-0-3 | CS201 |
| 2 | CS204 | Computer Networks | 3-0-0-3 | CS101 |
| 2 | CS205 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| 3 | CS301 | Machine Learning Fundamentals | 3-0-0-3 | CS201, CS202 |
| 3 | CS302 | Cryptography and Network Security | 3-0-0-3 | CS204 |
| 3 | CS303 | Data Mining and Analytics | 3-0-0-3 | CS202 |
| 3 | CS304 | Software Architecture and Design Patterns | 3-0-0-3 | CS205 |
| 3 | CS305 | User Experience Design | 3-0-0-3 | CS201 |
| 4 | CS401 | Natural Language Processing | 3-0-0-3 | CS301 |
| 4 | CS402 | Advanced Cybersecurity Techniques | 3-0-0-3 | CS302 |
| 4 | CS403 | Big Data Technologies | 3-0-0-3 | CS303 |
| 4 | CS404 | Software Testing and Quality Assurance | 3-0-0-3 | CS304 |
| 4 | CS405 | Human-Computer Interaction Research | 3-0-0-3 | CS305 |
| 5 | CS501 | Deep Learning Architectures | 3-0-0-3 | CS401 |
| 5 | CS502 | Cybersecurity Policy and Governance | 3-0-0-3 | CS402 |
| 5 | CS503 | Statistical Modeling for Data Science | 3-0-0-3 | CS403 |
| 5 | CS504 | Enterprise Software Development | 3-0-0-3 | CS404 |
| 5 | CS505 | Mobile Application Development | 3-0-0-3 | CS405 |
| 6 | CS601 | Reinforcement Learning | 3-0-0-3 | CS501 |
| 6 | CS602 | Security Incident Response | 3-0-0-3 | CS502 |
| 6 | CS603 | Advanced Data Visualization | 3-0-0-3 | CS503 |
| 6 | CS604 | Agile Software Development | 3-0-0-3 | CS504 |
| 6 | CS605 | Augmented Reality Applications | 3-0-0-3 | CS505 |
| 7 | CS701 | Generative AI Models | 3-0-0-3 | CS601 |
| 7 | CS702 | Blockchain Security | 3-0-0-3 | CS602 |
| 7 | CS703 | Time Series Analysis | 3-0-0-3 | CS603 |
| 7 | CS704 | DevOps and CI/CD Pipelines | 3-0-0-3 | CS604 |
| 7 | CS705 | Human-Centered AI Design | 3-0-0-3 | CS605 |
| 8 | CS801 | Capstone Project | 4-0-0-4 | All previous courses |
| 8 | CS802 | Research Seminar | 2-0-0-2 | CS801 |
Advanced Departmental Electives
The following are advanced departmental elective courses offered in the program:
- Natural Language Processing: This course explores the computational methods for processing and generating human language. Students learn about linguistic theories, statistical models, neural architectures, and applications such as machine translation, sentiment analysis, and chatbots.
- Deep Learning Architectures: Students study advanced neural network models including convolutional networks, recurrent networks, transformer models, and generative adversarial networks. The course emphasizes practical implementation using TensorFlow and PyTorch frameworks.
- Reinforcement Learning: This course introduces the principles of reinforcement learning, including Markov Decision Processes, Q-learning, policy gradients, and actor-critic methods. Students apply these concepts to real-world problems such as game playing and robotics control.
- Cybersecurity Policy and Governance: Focused on regulatory frameworks, compliance standards, and organizational security strategies, this course prepares students for roles in corporate cybersecurity leadership and policy development.
- Statistical Modeling for Data Science: Students learn advanced statistical techniques used in data science, including Bayesian inference, regression modeling, time series analysis, and experimental design. The course integrates R and Python for practical applications.
- Mobile Application Development: Covers mobile app development for iOS and Android platforms using native and cross-platform tools. Topics include UI/UX design, user interaction, backend integration, and deployment strategies.
- Augmented Reality Applications: Explores the principles and techniques behind augmented reality systems, including computer vision, spatial mapping, and interactive design. Students build AR applications using Unity and ARKit/ARCore.
- Human-Centered AI Design: This course focuses on designing AI systems that are intuitive, ethical, and inclusive. It covers topics such as bias mitigation, explainable AI, accessibility, and user research methodologies in AI development.
- Generative AI Models: Students study generative models including GANs, VAEs, diffusion models, and transformer-based text and image generation techniques. Emphasis is placed on ethical considerations and real-world applications.
- Blockchain Security: Examines the security aspects of blockchain technologies, including consensus mechanisms, smart contract vulnerabilities, cryptographic protocols, and decentralized identity systems.
Project-Based Learning Philosophy
Our department strongly advocates for project-based learning as a core pedagogical approach. The program integrates mandatory mini-projects and a final-year capstone project to ensure students gain hands-on experience with real-world challenges.
The Mini-Projects are assigned during the second and third years, allowing students to apply theoretical concepts in practical settings. These projects typically involve small teams of 3-5 students working under faculty supervision. Students select projects based on their interests and career aspirations, often aligning with ongoing research initiatives or industry partnerships.
The Final-Year Capstone Project is a comprehensive endeavor that spans the entire final year. Students work in multidisciplinary teams to develop innovative solutions addressing societal or business needs. This project culminates in a presentation to industry experts, faculty members, and potential investors. Successful capstone projects may be further developed into startup ventures or submitted for patent applications.
Evaluation criteria include:
- Technical Execution
- Problem-Solving Approach
- Team Collaboration
- Presentation Skills
- Innovation and Creativity
- Impact Assessment
Faculty mentors guide students throughout the project lifecycle, providing academic support, feedback, and industry insights. The department also organizes regular review sessions, milestone assessments, and progress reports to ensure successful completion.