Comprehensive Course List Across All 8 Semesters
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Engineering Mathematics I | 3-0-0-3 | None |
| 1 | CS102 | Physics for Computer Science | 3-0-0-3 | None |
| 1 | CS103 | Introduction to Programming | 3-0-0-3 | None |
| 1 | CS104 | Computer Fundamentals and Logic Design | 3-0-0-3 | None |
| 1 | CS105 | English Communication Skills | 2-0-0-2 | None |
| 1 | CS106 | Introduction to Lab | 0-0-3-1 | None |
| 2 | CS201 | Engineering Mathematics II | 3-0-0-3 | CS101 |
| 2 | CS202 | Object-Oriented Programming with Java | 3-0-0-3 | CS103 |
| 2 | CS203 | Data Structures and Algorithms | 3-0-0-3 | CS103 |
| 2 | CS204 | Database Management Systems | 3-0-0-3 | CS103 |
| 2 | CS205 | Computer Organization and Architecture | 3-0-0-3 | CS104 |
| 2 | CS206 | Lab: Data Structures & Algorithms | 0-0-3-1 | CS203 |
| 3 | CS301 | Probability and Statistics | 3-0-0-3 | CS101 |
| 3 | CS302 | Software Engineering | 3-0-0-3 | CS202 |
| 3 | CS303 | Operating Systems | 3-0-0-3 | CS205 |
| 3 | CS304 | Computer Networks | 3-0-0-3 | CS205 |
| 3 | CS305 | Artificial Intelligence | 3-0-0-3 | CS203 |
| 3 | CS306 | Lab: Software Engineering | 0-0-3-1 | CS302 |
| 4 | CS401 | Machine Learning | 3-0-0-3 | CS301 |
| 4 | CS402 | Cybersecurity | 3-0-0-3 | CS304 |
| 4 | CS403 | Distributed Systems | 3-0-0-3 | CS303 |
| 4 | CS404 | Data Mining and Warehousing | 3-0-0-3 | CS301 |
| 4 | CS405 | Web Technologies | 3-0-0-3 | CS202 |
| 4 | CS406 | Lab: Machine Learning | 0-0-3-1 | CS401 |
| 5 | CS501 | Big Data Analytics | 3-0-0-3 | CS404 |
| 5 | CS502 | Mobile Application Development | 3-0-0-3 | CS202 |
| 5 | CS503 | Human-Computer Interaction | 3-0-0-3 | CS302 |
| 5 | CS504 | Internet of Things | 3-0-0-3 | CS205 |
| 5 | CS505 | Compiler Design | 3-0-0-3 | CS303 |
| 5 | CS506 | Lab: Mobile Development | 0-0-3-1 | CS502 |
| 6 | CS601 | Cloud Computing | 3-0-0-3 | CS403 |
| 6 | CS602 | Blockchain Technology | 3-0-0-3 | CS304 |
| 6 | CS603 | Game Development | 3-0-0-3 | CS202 |
| 6 | CS604 | Project Management | 3-0-0-3 | CS302 |
| 6 | CS605 | Advanced Algorithms | 3-0-0-3 | CS203 |
| 6 | CS606 | Lab: Cloud Computing | 0-0-3-1 | CS601 |
| 7 | CS701 | Capstone Project I | 0-0-6-3 | CS501 |
| 7 | CS702 | Internship | 0-0-0-6 | CS401 |
| 8 | CS801 | Capstone Project II | 0-0-6-3 | CS701 |
| 8 | CS802 | Research Methodology | 3-0-0-3 | CS501 |
Detailed Descriptions of Advanced Departmental Electives
Advanced departmental elective courses in the Computer Science program at G M University Davanagere are designed to deepen students' expertise and prepare them for specialized roles in emerging fields. These courses cover cutting-edge technologies and methodologies that are shaping the future of computing.
The Machine Learning course (CS401) explores various algorithms used in artificial intelligence, including supervised learning, unsupervised learning, reinforcement learning, neural networks, and deep learning frameworks. Students learn to implement models using Python libraries like TensorFlow and PyTorch and apply them to real-world datasets.
The Cybersecurity course (CS402) focuses on protecting digital assets from threats such as hacking, malware, and data breaches. Topics include network security protocols, cryptography, ethical hacking, penetration testing, and incident response strategies. Students gain hands-on experience through simulated attacks and defensive exercises.
The Distributed Systems course (CS403) examines the design and implementation of systems that span multiple computers. Key concepts include fault tolerance, consensus algorithms, distributed databases, cloud computing, and microservices architecture. The course emphasizes practical applications in modern web services and enterprise environments.
The Data Mining and Warehousing course (CS404) introduces students to techniques for extracting insights from large datasets. It covers data preprocessing, clustering, classification, association rules, and data visualization. Students work with tools like Apache Spark and SQL to analyze real-world data sets.
The Web Technologies course (CS405) delves into the development of dynamic web applications using modern frameworks such as React, Node.js, and Express. The curriculum includes responsive design, API development, database integration, and deployment strategies for scalable web platforms.
The Big Data Analytics course (CS501) focuses on processing and analyzing massive volumes of data using tools like Hadoop, Spark, and Kafka. Students learn to extract actionable insights from unstructured and semi-structured data sources and apply statistical methods to predictive modeling.
The Mobile Application Development course (CS502) teaches students how to build cross-platform applications for iOS and Android using frameworks like Flutter and React Native. The course covers UI/UX design principles, app architecture, integration with backend services, and monetization strategies.
The Human-Computer Interaction course (CS503) explores the psychological and social factors that influence how users interact with technology. It includes usability testing, user research, prototyping, accessibility standards, and design thinking methodologies. Students conduct experiments and evaluate interfaces for inclusivity and effectiveness.
The Internet of Things course (CS504) examines the integration of physical devices into digital networks. Topics include sensor technologies, embedded systems, wireless communication protocols, edge computing, and smart city applications. Students build prototype IoT systems using Raspberry Pi and Arduino boards.
The Compiler Design course (CS505) provides a deep understanding of how programming languages are translated into executable code. It covers lexical analysis, parsing, semantic analysis, code generation, and optimization techniques. Students implement compilers for simple programming languages.
The Cloud Computing course (CS601) introduces students to cloud infrastructure, service models, deployment strategies, and platform security. Topics include virtualization, containerization, serverless computing, and hybrid cloud architectures. Students gain experience with AWS, Azure, and GCP services.
The Blockchain Technology course (CS602) explores the underlying principles of blockchain systems, including cryptographic hashing, consensus mechanisms, smart contracts, and decentralized applications. Students learn to develop blockchain-based solutions using platforms like Ethereum and Hyperledger Fabric.
The Game Development course (CS603) teaches students how to create interactive entertainment experiences using game engines like Unity and Unreal Engine. It covers game mechanics, level design, animation, audio integration, and performance optimization. Students develop complete games from concept to release.
The Project Management course (CS604) provides an overview of project lifecycle management, risk assessment, resource planning, and stakeholder communication. It includes agile methodologies, Scrum frameworks, and tools for tracking progress and delivering value in software development projects.
The Advanced Algorithms course (CS605) focuses on algorithmic problem-solving techniques and complexity analysis. Students study advanced topics such as approximation algorithms, online algorithms, graph algorithms, and combinatorial optimization. The course prepares students for competitive programming and research-oriented roles.
Project-Based Learning Philosophy
The department at G M University Davanagere adopts a robust project-based learning model that emphasizes hands-on experience, critical thinking, and collaborative problem-solving. This approach ensures that students develop both technical competencies and practical skills necessary for success in industry or academia.
Mini-projects are introduced in the second year and gradually increase in complexity and scope. These projects typically last 6–8 weeks and involve small teams of 3–5 students working under faculty supervision. The evaluation criteria include code quality, documentation, presentation, and peer feedback.
The final-year capstone project or thesis is a significant component of the program's curriculum. Students select a topic aligned with their specialization or personal interest and work closely with a faculty mentor for 12–16 weeks. This experience allows students to demonstrate their mastery of core concepts while contributing original research or practical innovations.
Project selection is facilitated through an online portal where students can propose topics, browse available projects, and match with suitable mentors based on interest and expertise. The department also encourages participation in industry-sponsored challenges, hackathons, and open-source initiatives to broaden students' exposure to real-world scenarios.