Course Structure Overview
The engineering program at Dr D Y Patil Dnyan Prasad Pune is structured over eight semesters to ensure a comprehensive and progressive learning experience. The curriculum is divided into core courses, science electives, departmental electives, and laboratory sessions designed to build both theoretical knowledge and practical skills.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | ENG102 | Engineering Physics | 3-1-0-4 | - |
| 1 | ENG103 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | ENG104 | Introduction to Programming | 2-0-2-3 | - |
| 1 | ENG105 | Engineering Graphics | 2-1-0-3 | - |
| 1 | ENG106 | Communication Skills | 2-0-0-2 | - |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Electrical Circuits | 3-1-0-4 | - |
| 2 | ENG203 | Mechanics of Materials | 3-1-0-4 | - |
| 2 | ENG204 | Thermodynamics | 3-1-0-4 | - |
| 2 | ENG205 | Engineering Materials | 3-1-0-4 | - |
| 2 | ENG206 | Basic Electronics | 2-1-0-3 | - |
| 3 | ENG301 | Signals & Systems | 3-1-0-4 | ENG201 |
| 3 | ENG302 | Control Systems | 3-1-0-4 | - |
| 3 | ENG303 | Computer Architecture | 3-1-0-4 | - |
| 3 | ENG304 | Digital Logic Design | 2-1-0-3 | - |
| 3 | ENG305 | Structural Analysis | 3-1-0-4 | - |
| 3 | ENG306 | Fluid Mechanics | 3-1-0-4 | - |
| 4 | ENG401 | Power Electronics | 3-1-0-4 | - |
| 4 | ENG402 | Machine Learning | 3-1-0-4 | - |
| 4 | ENG403 | Embedded Systems | 3-1-0-4 | - |
| 4 | ENG404 | Robotics & Automation | 3-1-0-4 | - |
| 4 | ENG405 | Biomedical Engineering | 3-1-0-4 | - |
| 4 | ENG406 | Renewable Energy Systems | 3-1-0-4 | - |
Advanced Departmental Electives
These courses are offered in the later semesters and provide students with deeper insights into specialized areas of engineering:
- Deep Learning: This course explores advanced architectures of neural networks, including convolutional, recurrent, and transformer-based models. Students learn to apply these techniques to image recognition, NLP, and speech processing.
- Advanced Control Systems: Focuses on nonlinear systems, state-space representation, optimal control, and robust control design. Ideal for students aiming to pursue research or roles in aerospace or industrial automation.
- Image Processing and Computer Vision: Covers algorithms for image enhancement, segmentation, feature extraction, and object detection. Applications include autonomous vehicles, medical imaging, and robotics.
- Microprocessor & Microcontroller Systems: Delves into the architecture of microcontrollers, assembly language programming, and embedded software development. Practical sessions involve interfacing sensors and actuators.
- Sustainable Urban Design: Integrates engineering principles with environmental sustainability to design efficient urban infrastructures such as smart grids, water systems, and green buildings.
- Reinforcement Learning: Introduces the mathematical foundations of reinforcement learning, including Markov Decision Processes, Q-learning, and policy gradients. Students implement agents that learn optimal behaviors through interaction with environments.
- Quantum Computing Fundamentals: Explores quantum algorithms, qubits, entanglement, and superposition. Prepares students for emerging roles in quantum software development and research.
- Advanced Robotics: Covers advanced robotic kinematics, sensor fusion, motion planning, and control systems. Students work on building autonomous robots capable of complex tasks.
- Data Science & Big Data Analytics: Focuses on statistical modeling, machine learning pipelines, data visualization, and big data tools like Hadoop and Spark.
- Advanced Materials for Engineering: Studies advanced materials such as nanomaterials, composites, and smart materials with applications in aerospace, biomedical devices, and electronics.
Project-Based Learning Philosophy
The program places a strong emphasis on project-based learning, recognizing that real-world problem-solving requires hands-on experience. Students engage in both mini-projects during their second and third years and a capstone project in their final year.
Mini-projects are designed to reinforce classroom learning while allowing students to explore specific areas of interest under faculty supervision. These projects are typically completed within a semester and contribute significantly to the student’s understanding of engineering processes.
The final-year thesis or capstone project is a multi-month endeavor that culminates in a comprehensive solution to a real-world engineering challenge. Students are paired with faculty mentors based on their interests, and the projects often involve collaboration with industry partners. The evaluation process includes peer review, mentor feedback, and public presentation.