Comprehensive Course Structure Table
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | ENG102 | Physics for Engineers | 3-1-0-4 | None |
| 1 | ENG103 | Chemistry for Engineers | 3-1-0-4 | None |
| 1 | ENG104 | Introduction to Engineering | 2-0-2-3 | None |
| 1 | ENG105 | Programming and Problem Solving | 2-0-2-3 | None |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Electrical Engineering Fundamentals | 3-1-0-4 | ENG102 |
| 2 | ENG203 | Materials Science | 3-1-0-4 | ENG103 |
| 2 | ENG204 | Computer Programming | 2-0-2-3 | ENG105 |
| 2 | ENG205 | Engineering Graphics and Design | 2-0-2-3 | None |
| 3 | ENG301 | Engineering Mathematics III | 3-1-0-4 | ENG201 |
| 3 | ENG302 | Thermodynamics | 3-1-0-4 | ENG202 |
| 3 | ENG303 | Fluid Mechanics | 3-1-0-4 | ENG202 |
| 3 | ENG304 | Digital Electronics | 3-1-0-4 | ENG202 |
| 3 | ENG305 | Database Management Systems | 2-0-2-3 | ENG205 |
| 4 | ENG401 | Engineering Mathematics IV | 3-1-0-4 | ENG301 |
| 4 | ENG402 | Control Systems | 3-1-0-4 | ENG301 |
| 4 | ENG403 | Signals and Systems | 3-1-0-4 | ENG301 |
| 4 | ENG404 | Microprocessors and Microcontrollers | 3-1-0-4 | ENG304 |
| 4 | ENG405 | Web Technologies | 2-0-2-3 | ENG305 |
| 5 | ENG501 | Machine Design | 3-1-0-4 | ENG302 |
| 5 | ENG502 | Heat Transfer | 3-1-0-4 | ENG302 |
| 5 | ENG503 | Power Systems | 3-1-0-4 | ENG202 |
| 5 | ENG504 | Artificial Intelligence | 3-1-0-4 | ENG401 |
| 5 | ENG505 | Software Engineering | 2-0-2-3 | ENG405 |
| 6 | ENG601 | Advanced Control Systems | 3-1-0-4 | ENG402 |
| 6 | ENG602 | Renewable Energy Systems | 3-1-0-4 | ENG503 |
| 6 | ENG603 | Advanced Machine Learning | 3-1-0-4 | ENG504 |
| 6 | ENG604 | Embedded Systems | 3-1-0-4 | ENG404 |
| 6 | ENG605 | Project Management | 2-0-2-3 | ENG505 |
| 7 | ENG701 | Research Methodology | 2-0-2-3 | ENG605 |
| 7 | ENG702 | Advanced Data Structures | 3-1-0-4 | ENG504 |
| 7 | ENG703 | Advanced Computer Networks | 3-1-0-4 | ENG405 |
| 7 | ENG704 | Advanced Cybersecurity | 3-1-0-4 | ENG504 |
| 7 | ENG705 | Capstone Project I | 4-0-0-4 | ENG605 |
| 8 | ENG801 | Capstone Project II | 4-0-0-4 | ENG705 |
| 8 | ENG802 | Industrial Training | 0-0-0-6 | ENG705 |
Detailed Overview of Advanced Departmental Electives
Advanced departmental electives are designed to provide students with in-depth knowledge and practical skills in specialized areas of engineering. These courses are offered in the later semesters and are tailored to align with industry trends and research advancements.
Artificial Intelligence and Machine Learning
This course explores the fundamental concepts of artificial intelligence and machine learning, including supervised and unsupervised learning, neural networks, deep learning architectures, and reinforcement learning. Students will gain hands-on experience with popular frameworks such as TensorFlow and PyTorch. The course emphasizes practical implementation and real-world applications, preparing students for careers in AI research and development.
Cybersecurity
The Cybersecurity course covers essential topics such as network security, cryptography, system security, and ethical hacking. Students will learn to identify vulnerabilities, develop security protocols, and implement protective measures against cyber threats. The course includes practical labs and simulations to enhance students' understanding of real-world security challenges.
Data Science
This course introduces students to data analysis, statistical modeling, and data visualization techniques. Students will learn to use tools like Python, R, and SQL to extract insights from large datasets. The course emphasizes the application of data science in various domains such as business intelligence, healthcare, and finance.
Software Engineering
The Software Engineering course covers the entire software development lifecycle, from requirements analysis to testing and deployment. Students will learn about software architecture, design patterns, version control, and agile methodologies. The course includes group projects to simulate real-world software development environments.
Embedded Systems
This course focuses on the design and development of embedded systems, which are specialized computing systems embedded in larger devices. Students will learn about microcontrollers, real-time operating systems, and hardware-software co-design. The course includes hands-on labs using development kits and simulation tools.
Power Systems
The Power Systems course covers the generation, transmission, and distribution of electrical power. Students will study power system analysis, stability, and control, as well as renewable energy integration. The course includes practical sessions on power system simulation using software tools like MATLAB and Simulink.
Control Systems
This course explores the principles of control systems, including feedback control, stability analysis, and controller design. Students will learn to model and analyze dynamic systems using mathematical tools and simulate control systems using software tools. The course emphasizes practical applications in industrial automation and robotics.
Signal Processing
The Signal Processing course covers the analysis and manipulation of signals in both time and frequency domains. Students will learn about digital signal processing, filter design, and spectral analysis. The course includes practical sessions on using tools like MATLAB for signal processing applications.
VLSI Design
This course focuses on the design and implementation of very large-scale integration (VLSI) circuits. Students will learn about digital design, logic synthesis, and physical design of integrated circuits. The course includes hands-on labs using industry-standard tools for VLSI design and verification.
Structural Engineering
The Structural Engineering course covers the principles of structural analysis and design. Students will study load analysis, structural behavior, and design of various structural elements. The course includes practical sessions on structural modeling and analysis using software tools.
Project-Based Learning Philosophy
Our department strongly believes in project-based learning as a core component of engineering education. This approach allows students to apply theoretical concepts to real-world problems, fostering critical thinking, creativity, and collaboration. Students begin working on projects from their first year, with increasing complexity and scope as they progress through the program.
Mini-Projects
Mini-projects are assigned in the second and third years to reinforce learning and develop practical skills. These projects are typically completed in small groups and are evaluated based on technical execution, presentation, and peer feedback. Mini-projects are designed to be manageable yet challenging, providing students with a sense of accomplishment and confidence.
Final-Year Thesis/Capstone Project
The final-year thesis or capstone project is a comprehensive, independent research project that students undertake under the guidance of a faculty mentor. The project is typically completed in teams and involves identifying a real-world problem, proposing a solution, and implementing it. The project is evaluated based on innovation, technical depth, presentation, and documentation.
Project Selection and Mentorship
Students select their projects based on their interests and career goals, with guidance from faculty mentors. The selection process involves a proposal submission, followed by a review by the faculty committee. Faculty mentors are assigned based on the project topic and the mentor's expertise. The mentorship system ensures that students receive continuous support throughout their project journey.