Comprehensive Course Structure Across 8 Semesters
This table outlines all core, departmental elective, science elective, and lab courses across the eight semesters of the Computer Science program at Eklavya University Damoh.
| Semester | Course Code | Full Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-2 | None |
| 1 | CS102 | Mathematics I | 3-0-0-2 | None |
| 1 | CS103 | Computer Fundamentals | 3-0-0-2 | None |
| 1 | CS104 | Engineering Graphics & Design | 3-0-0-2 | None |
| 1 | CS105 | English for Technical Communication | 3-0-0-2 | None |
| 1 | CS106 | Lab: Programming Concepts | 0-0-3-2 | CS101 |
| 2 | CS201 | Data Structures & Algorithms | 3-0-0-2 | CS101 |
| 2 | CS202 | Mathematics II | 3-0-0-2 | CS102 |
| 2 | CS203 | Object-Oriented Programming | 3-0-0-2 | CS101 |
| 2 | CS204 | Database Systems | 3-0-0-2 | CS103 |
| 2 | CS205 | Computer Organization | 3-0-0-2 | CS103 |
| 2 | CS206 | Lab: Data Structures & Algorithms | 0-0-3-2 | CS201 |
| 3 | CS301 | Operating Systems | 3-0-0-2 | CS205 |
| 3 | CS302 | Software Engineering | 3-0-0-2 | CS203 |
| 3 | CS303 | Computer Networks | 3-0-0-2 | CS204 |
| 3 | CS304 | Mathematics III | 3-0-0-2 | CS202 |
| 3 | CS305 | Probability & Statistics | 3-0-0-2 | CS202 |
| 3 | CS306 | Lab: Operating Systems | 0-0-3-2 | CS301 |
| 4 | CS401 | Design & Analysis of Algorithms | 3-0-0-2 | CS201 |
| 4 | CS402 | Artificial Intelligence | 3-0-0-2 | CS201 |
| 4 | CS403 | Cybersecurity Fundamentals | 3-0-0-2 | CS303 |
| 4 | CS404 | Web Technologies | 3-0-0-2 | CS203 |
| 4 | CS405 | Mini Project I | 0-0-0-4 | CS201 |
| 4 | CS406 | Lab: Web Technologies | 0-0-3-2 | CS404 |
| 5 | CS501 | Machine Learning | 3-0-0-2 | CS402 |
| 5 | CS502 | Data Mining & Warehousing | 3-0-0-2 | CS405 |
| 5 | CS503 | Advanced Computer Architecture | 3-0-0-2 | CS301 |
| 5 | CS504 | Mobile Application Development | 3-0-0-2 | CS404 |
| 5 | CS505 | Mini Project II | 0-0-0-4 | CS405 |
| 5 | CS506 | Lab: Machine Learning | 0-0-3-2 | CS501 |
| 6 | CS601 | Deep Learning | 3-0-0-2 | CS501 |
| 6 | CS602 | Cloud Computing | 3-0-0-2 | CS303 |
| 6 | CS603 | Blockchain Technology | 3-0-0-2 | CS403 |
| 6 | CS604 | Natural Language Processing | 3-0-0-2 | CS501 |
| 6 | CS605 | Final Year Project / Thesis | 0-0-0-8 | CS505 |
| 6 | CS606 | Lab: Deep Learning | 0-0-3-2 | CS601 |
Detailed Departmental Elective Courses
Departmental electives provide students with the flexibility to explore advanced topics aligned with their interests and career aspirations. Here are descriptions of several key advanced departmental electives:
1. Advanced Machine Learning
This course delves into modern machine learning techniques, including deep reinforcement learning, ensemble methods, generative models, and neural architecture search. Students learn to implement and optimize complex ML pipelines using TensorFlow, PyTorch, and Scikit-learn. The course includes hands-on labs on real-world datasets from domains such as healthcare, finance, and autonomous systems.
2. Cybersecurity Engineering
This course explores advanced cybersecurity concepts including network intrusion detection, digital forensics, malware analysis, secure coding practices, and cryptography. It combines theory with practical exercises involving penetration testing tools like Metasploit and Wireshark, ensuring students gain real-world experience in defending against cyber threats.
3. Big Data Analytics
Students are introduced to frameworks like Apache Spark, Hadoop, and Kafka for processing large-scale datasets. The course covers data warehousing, ETL processes, graph analytics, and streaming data models. Real-time case studies from companies like Netflix, Uber, and Amazon illustrate how big data transforms business strategies.
4. Software Architecture & Design Patterns
This elective teaches students how to design scalable software systems using industry-standard patterns such as MVC, Microservices, and Service-Oriented Architecture. Through group projects, students implement architectures for enterprise applications, gaining insights into cloud deployment strategies and DevOps integration.
5. Internet of Things (IoT) Systems
The course covers IoT device development, sensor integration, wireless communication protocols, edge computing, and smart city infrastructure. Students build end-to-end IoT solutions using platforms like Arduino, Raspberry Pi, and AWS IoT Core.
6. Game Development & 3D Modeling
This course introduces students to game engines like Unity and Unreal, covering 3D modeling, animation, physics simulation, and interactive storytelling. Projects include building mobile games, VR experiences, and educational simulations.
7. Human-Computer Interaction (HCI)
Students learn about user-centered design principles, usability testing, accessibility standards, and prototyping techniques. The course emphasizes creating intuitive interfaces that enhance user experience across devices and platforms.
8. Quantum Computing
This emerging field explores quantum algorithms, qubit manipulation, error correction, and quantum software development. Students use simulators like Qiskit and Cirq to experiment with quantum circuits and solve optimization problems.
9. Data Visualization & Storytelling
Using tools like Tableau, Power BI, D3.js, and Python libraries, students learn how to transform complex data into compelling visual narratives. Case studies from news outlets, government agencies, and research institutions demonstrate the impact of effective data storytelling.
10. Mobile App Development
This course focuses on developing cross-platform mobile apps using frameworks like React Native and Flutter. Students learn about app architecture, user interface design, API integration, and deployment strategies for both iOS and Android platforms.
Project-Based Learning Philosophy
Eklavya University Damoh’s approach to project-based learning is designed to bridge the gap between theory and practice. From the first year onwards, students engage in mini-projects that reinforce classroom learning and build foundational skills. These projects are carefully scaffolded to gradually increase complexity and depth.
Mini-Projects
In the second year, students undertake a two-part mini-project involving problem identification, research, design, implementation, and documentation. The projects are assigned by faculty members or based on industry challenges. Each project is evaluated through peer review, mentor feedback, and presentation sessions.
Final-Year Thesis/Capstone Project
The final-year capstone project allows students to apply their accumulated knowledge in a significant, independent research or development initiative. Students select projects aligned with their interests or industry needs, often in collaboration with corporate sponsors or research labs. Projects are supervised by faculty mentors and culminate in an oral defense and a detailed written report.
Project Selection & Mentorship
Students choose project topics during the fourth semester, guided by faculty advisors who assess alignment with student interests, academic strengths, and market relevance. Mentors provide ongoing support, ensuring that students navigate challenges effectively while maintaining quality output. Regular progress reviews ensure timely completion and academic integrity.