Course Structure Across 8 Semesters
| Semester | Course Code | Course Title | 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 | Programming & Problem Solving | 2-0-2-3 | None |
| 1 | ENG105 | Engineering Drawing & Graphics | 1-0-2-2 | None |
| 1 | ENG106 | Basic Electrical Engineering | 3-1-0-4 | None |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Strength of Materials | 3-1-0-4 | ENG106 |
| 2 | ENG203 | Thermodynamics | 3-1-0-4 | ENG102 |
| 2 | ENG204 | Electrical Circuits & Networks | 3-1-0-4 | ENG106 |
| 2 | ENG205 | Fluid Mechanics | 3-1-0-4 | ENG102 |
| 2 | ENG206 | Engineering Economics & Management | 3-0-0-3 | None |
| 3 | ENG301 | Design & Analysis of Algorithms | 3-1-0-4 | ENG104 |
| 3 | ENG302 | Database Management Systems | 3-1-0-4 | ENG104 |
| 3 | ENG303 | Signals & Systems | 3-1-0-4 | ENG201 |
| 3 | ENG304 | Control Systems | 3-1-0-4 | ENG204 |
| 3 | ENG305 | Computer Architecture | 3-1-0-4 | ENG106 |
| 3 | ENG306 | Environmental Studies | 3-0-0-3 | None |
| 4 | ENG401 | Machine Learning & Data Mining | 3-1-0-4 | ENG301 |
| 4 | ENG402 | Advanced Computer Networks | 3-1-0-4 | ENG305 |
| 4 | ENG403 | Power Generation & Distribution | 3-1-0-4 | ENG204 |
| 4 | ENG404 | Structural Analysis | 3-1-0-4 | ENG202 |
| 4 | ENG405 | Renewable Energy Systems | 3-1-0-4 | ENG203 |
| 4 | ENG406 | Project Management & Entrepreneurship | 3-0-0-3 | ENG206 |
| 5 | ENG501 | Advanced Artificial Intelligence | 3-1-0-4 | ENG401 |
| 5 | ENG502 | Cybersecurity & Ethical Hacking | 3-1-0-4 | ENG302 |
| 5 | ENG503 | Advanced Power Electronics | 3-1-0-4 | ENG303 |
| 5 | ENG504 | Geotechnical Engineering | 3-1-0-4 | ENG202 |
| 5 | ENG505 | Sustainable Transportation Systems | 3-1-0-4 | ENG205 |
| 5 | ENG506 | Biomedical Instrumentation | 3-1-0-4 | ENG303 |
| 6 | ENG601 | Capstone Project I | 2-0-4-5 | ENG501 |
| 6 | ENG602 | Capstone Project II | 2-0-4-5 | ENG601 |
| 6 | ENG603 | Advanced Materials Science | 3-1-0-4 | ENG202 |
| 6 | ENG604 | Intelligent Transportation Systems | 3-1-0-4 | ENG505 |
| 6 | ENG605 | Advanced Computational Methods | 3-1-0-4 | ENG301 |
| 6 | ENG606 | Research Methodology & Ethics | 3-0-0-3 | None |
| 7 | ENG701 | Internship - Semester I | 0-0-0-6 | ENG501 |
| 8 | ENG801 | Internship - Semester II | 0-0-0-6 | ENG701 |
Advanced Departmental Electives
Our advanced departmental electives are designed to provide students with specialized knowledge and practical skills relevant to their chosen career paths. These courses are taught by internationally recognized faculty members who bring both academic rigor and industry experience.
Artificial Intelligence & Machine Learning (AI/ML)
This course introduces students to the fundamentals of AI and ML, covering supervised and unsupervised learning techniques, neural networks, deep learning architectures, and natural language processing. Students learn to implement algorithms using Python and TensorFlow frameworks.
Cybersecurity
The course covers network security protocols, cryptography, ethical hacking, and risk management strategies. It includes hands-on labs on penetration testing, vulnerability assessment, and secure coding practices.
Power Systems & Renewable Energy
This elective explores power generation technologies, grid stability, and renewable energy integration. Students work on real-time simulations using MATLAB/Simulink to model and analyze electrical systems.
Structural Engineering
The course focuses on structural analysis, design principles, and seismic considerations. Students engage in laboratory experiments involving load testing and material behavior studies.
Biomedical Engineering
This elective bridges engineering with healthcare by introducing students to medical device development, biomechanics, and bioinformatics. Practical sessions involve lab work on prosthetics and diagnostic equipment.
Data Science & Analytics
Students gain proficiency in statistical modeling, data visualization, predictive analytics, and big data technologies like Hadoop and Spark. The course emphasizes real-world applications through industry datasets.
Advanced Computer Networks
This course delves into network architecture, protocols, security mechanisms, and performance optimization. Students conduct research projects on wireless networks and internet scalability.
Sustainable Transportation Systems
Exploring modern transportation technologies, this course covers electric vehicles, smart traffic systems, urban mobility planning, and environmental impact assessments.
Embedded Systems Design
Students learn microcontroller programming, real-time operating systems, IoT applications, and embedded software development using ARM-based platforms.
Quantitative Finance
This course combines financial theory with computational tools to model market behavior, price derivatives, and evaluate investment strategies. It includes practical sessions in MATLAB and Python.
Project-Based Learning Framework
At Pdm University, project-based learning is central to the engineering education experience. Students begin working on mini-projects in their third semester, gaining exposure to real-world challenges under faculty supervision.
Mini-projects are typically completed within a period of 6 weeks and involve designing, prototyping, testing, and presenting solutions. Evaluation criteria include creativity, technical soundness, teamwork, and presentation skills.
The final-year capstone project is an extended endeavor spanning two semesters. Students form teams to tackle complex, interdisciplinary problems that require innovative thinking and advanced engineering techniques. Faculty mentors guide students throughout the process, ensuring alignment with industry standards and best practices.