Curriculum Overview
The curriculum for the Engineering program at Shridhar University Pilani is designed to provide a comprehensive and rigorous education that combines theoretical knowledge with practical application. The program is structured over 8 semesters, with each semester comprising core courses, departmental electives, science electives, and laboratory sessions. The curriculum emphasizes project-based learning, research, and industry relevance to ensure that students are well-prepared for their professional careers.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Graphics | 2-0-2-3 | - |
| 1 | PHY101 | Physics for Engineers | 3-0-0-3 | - |
| 1 | MAT101 | Calculus and Differential Equations | 4-0-0-4 | - |
| 1 | CSE101 | Introduction to Programming | 2-0-2-3 | - |
| 1 | CHM101 | Chemistry for Engineers | 3-0-0-3 | - |
| 1 | ENG102 | Basic Electrical Engineering | 3-0-0-3 | - |
| 2 | MAT201 | Linear Algebra and Probability | 3-0-0-3 | MAT101 |
| 2 | PHY201 | Electromagnetic Fields | 3-0-0-3 | PHY101 |
| 2 | CHM201 | Organic Chemistry | 3-0-0-3 | CHM101 |
| 2 | CSE201 | Data Structures and Algorithms | 3-0-0-3 | CSE101 |
| 2 | ENG201 | Strength of Materials | 3-0-0-3 | ENG102 |
| 2 | MAT202 | Statistics and Numerical Methods | 3-0-0-3 | MAT101 |
| 3 | ENG301 | Thermodynamics | 3-0-0-3 | PHY201 |
| 3 | ENG302 | Fluid Mechanics | 3-0-0-3 | PHY201 |
| 3 | CSE301 | Database Management Systems | 3-0-0-3 | CSE201 |
| 3 | ENG303 | Electrical Circuits | 3-0-0-3 | ENG102 |
| 3 | MAT301 | Complex Analysis | 3-0-0-3 | MAT201 |
| 3 | CHM301 | Physical Chemistry | 3-0-0-3 | CHM201 |
| 4 | ENG401 | Heat Transfer | 3-0-0-3 | ENG301 |
| 4 | ENG402 | Manufacturing Processes | 3-0-0-3 | ENG301 |
| 4 | CSE401 | Machine Learning | 3-0-0-3 | CSE201 |
| 4 | ENG403 | Control Systems | 3-0-0-3 | ENG303 |
| 4 | MAT401 | Partial Differential Equations | 3-0-0-3 | MAT301 |
| 4 | CHM401 | Chemical Engineering Principles | 3-0-0-3 | CHM301 |
| 5 | ENG501 | Signals and Systems | 3-0-0-3 | ENG303 |
| 5 | ENG502 | Structural Analysis | 3-0-0-3 | ENG301 |
| 5 | CSE501 | Computer Networks | 3-0-0-3 | CSE201 |
| 5 | ENG503 | Power Systems | 3-0-0-3 | ENG303 |
| 5 | MAT501 | Advanced Mathematics | 3-0-0-3 | MAT401 |
| 5 | CHM501 | Environmental Chemistry | 3-0-0-3 | CHM401 |
| 6 | ENG601 | Robotics and Automation | 3-0-0-3 | ENG501 |
| 6 | ENG602 | Geotechnical Engineering | 3-0-0-3 | ENG301 |
| 6 | CSE601 | Deep Learning | 3-0-0-3 | CSE401 |
| 6 | ENG603 | Renewable Energy Systems | 3-0-0-3 | ENG301 |
| 6 | MAT601 | Operations Research | 3-0-0-3 | MAT501 |
| 6 | CHM601 | Biochemistry | 3-0-0-3 | CHM501 |
| 7 | ENG701 | Advanced Control Systems | 3-0-0-3 | ENG501 |
| 7 | ENG702 | Advanced Materials | 3-0-0-3 | ENG602 |
| 7 | CSE701 | Software Engineering | 3-0-0-3 | CSE601 |
| 7 | ENG703 | Smart Grids | 3-0-0-3 | ENG503 |
| 7 | MAT701 | Mathematical Modeling | 3-0-0-3 | MAT601 |
| 7 | CHM701 | Industrial Chemistry | 3-0-0-3 | CHM601 |
| 8 | ENG801 | Capstone Project | 4-0-0-4 | ENG701 |
| 8 | ENG802 | Advanced Topics in Engineering | 3-0-0-3 | ENG701 |
| 8 | CSE801 | Research Methodology | 3-0-0-3 | CSE701 |
| 8 | ENG803 | Project Management | 3-0-0-3 | ENG701 |
| 8 | MAT801 | Advanced Numerical Methods | 3-0-0-3 | MAT701 |
| 8 | CHM801 | Chemical Process Design | 3-0-0-3 | CHM701 |
Advanced Departmental Elective Courses
The department offers several advanced elective courses that allow students to explore specialized areas of interest. These courses are designed to provide in-depth knowledge and practical skills in emerging fields. Below are detailed descriptions of some of the advanced elective courses:
Machine Learning
This course focuses on the principles and applications of machine learning algorithms. Students will learn about supervised and unsupervised learning, neural networks, deep learning, and reinforcement learning. The course includes hands-on projects using Python and TensorFlow, providing students with practical experience in developing and deploying machine learning models.
Deep Learning
This course explores the theory and practice of deep learning, including convolutional neural networks, recurrent neural networks, and transformer models. Students will gain experience in building and training deep learning models using frameworks such as PyTorch and TensorFlow. The course also covers applications in computer vision, natural language processing, and speech recognition.
Computer Vision
This course introduces students to the fundamentals of computer vision and image processing. Topics include image filtering, feature detection, object recognition, and image segmentation. Students will work on projects involving real-world applications such as autonomous vehicles and medical image analysis.
Database Management Systems
This course covers the design and implementation of database systems. Students will learn about relational databases, SQL, normalization, and transaction management. The course includes hands-on experience with popular database management systems such as MySQL and PostgreSQL.
Software Engineering
This course provides an overview of software engineering principles and practices. Students will learn about software development life cycles, design patterns, testing, and project management. The course includes group projects that simulate real-world software development environments.
Computer Networks
This course covers the fundamentals of computer networking, including network protocols, architectures, and security. Students will learn about TCP/IP, routing, and wireless networks. The course includes hands-on experience with network simulation tools and practical networking exercises.
Robotics and Automation
This course introduces students to robotics and automation systems. Topics include robot kinematics, control systems, sensor integration, and autonomous navigation. Students will work on projects involving robotic platforms and automation technologies.
Renewable Energy Systems
This course explores the design and implementation of renewable energy systems. Students will learn about solar, wind, and hydroelectric power generation. The course includes hands-on projects involving renewable energy systems and energy storage technologies.
Advanced Control Systems
This course covers advanced topics in control systems, including state-space methods, optimal control, and robust control. Students will learn to design and analyze control systems for complex engineering applications. The course includes practical experience with control system simulation tools.
Smart Grids
This course focuses on the design and operation of smart grids. Students will learn about power system integration, energy management, and grid stability. The course includes hands-on experience with smart grid simulation tools and real-world case studies.
Project-Based Learning Philosophy
The department's philosophy on project-based learning emphasizes hands-on experience, real-world problem-solving, and collaboration. Students are encouraged to work on projects that address real-world challenges, applying their knowledge and skills in practical contexts.
Mini-Projects
Mini-projects are undertaken in the second and third years of the program. These projects are designed to reinforce theoretical concepts and provide students with practical experience. Students work in small groups and are mentored by faculty members. The projects are evaluated based on technical execution, presentation, and teamwork.
Final-Year Thesis/Capstone Project
The final-year thesis or capstone project is a significant component of the program. Students work on a substantial project that integrates their knowledge and skills. The project is typically sponsored by industry partners or conducted in collaboration with research labs. Students are assigned faculty mentors who guide them through the research and development process.
Project Selection and Mentorship
Students select their projects based on their interests and career goals. The selection process involves discussions with faculty mentors and industry partners. Faculty mentors are assigned based on their expertise and the relevance of their research to the student's project. The mentorship system ensures that students receive guidance and support throughout their project journey.