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Fees
₹6,50,000
Placement
94.5%
Avg Package
₹7,50,000
Highest Package
₹18,00,000
Fees
₹6,50,000
Placement
94.5%
Avg Package
₹7,50,000
Highest Package
₹18,00,000
Seats
300
Students
1,200
Seats
300
Students
1,200
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| Semester I | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| PHY101 | Physics for Engineers | 3-1-0-4 | - | |
| CHE101 | Chemistry for Engineers | 3-1-0-4 | - | |
| MAT101 | Mathematics for Engineers | 3-1-0-4 | - | |
| CSE101 | Introduction to Programming | 2-0-2-4 | - | |
| ENG102 | Engineering Graphics | 2-0-2-4 | - | |
| ENG103 | Basic Electrical and Electronics | 3-1-0-4 | - | |
| ENG104 | Engineering Mechanics | 3-1-0-4 | - | |
| ENG105 | Workshop Practice | 0-0-2-2 | - | |
| ENG106 | Communication Skills | 2-0-0-2 | - | |
| ENG107 | Introduction to Engineering | 2-0-0-2 | - | |
| ENG108 | Physical Education | 0-0-0-2 | - | |
| Semester II | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| PHY201 | Modern Physics and Optics | 3-1-0-4 | PHY101 | |
| CHE201 | Organic Chemistry | 3-1-0-4 | CHE101 | |
| MAT201 | Statistics and Probability | 3-1-0-4 | MAT101 | |
| CSE201 | Data Structures and Algorithms | 2-0-2-4 | CSE101 | |
| ENG202 | Fluid Mechanics | 3-1-0-4 | ENG104 | |
| ENG203 | Materials Science | 3-1-0-4 | - | |
| ENG204 | Circuit Analysis | 3-1-0-4 | ENG103 | |
| ENG205 | Computer Organization | 2-0-2-4 | CSE101 | |
| ENG206 | Engineering Ethics | 2-0-0-2 | - | |
| ENG207 | Leadership and Team Management | 2-0-0-2 | - | |
| ENG208 | Environmental Science | 2-0-0-2 | - | |
| Semester III | ENG301 | Engineering Mathematics III | 3-1-0-4 | ENG201 |
| PHY301 | Electromagnetic Fields and Waves | 3-1-0-4 | PHY201 | |
| CHE301 | Inorganic Chemistry | 3-1-0-4 | CHE201 | |
| MAT301 | Linear Algebra and Differential Equations | 3-1-0-4 | MAT201 | |
| CSE301 | Database Management Systems | 2-0-2-4 | CSE201 | |
| ENG302 | Strength of Materials | 3-1-0-4 | ENG204 | |
| ENG303 | Thermodynamics | 3-1-0-4 | ENG202 | |
| ENG304 | Signals and Systems | 3-1-0-4 | ENG204 | |
| ENG305 | Digital Electronics | 2-0-2-4 | ENG204 | |
| ENG306 | Engineering Design | 2-0-2-4 | - | |
| ENG307 | Project Management | 2-0-0-2 | - | |
| ENG308 | Industrial Training I | 0-0-2-2 | - | |
| Semester IV | ENG401 | Engineering Mathematics IV | 3-1-0-4 | ENG301 |
| PHY401 | Quantum Physics | 3-1-0-4 | PHY301 | |
| CHE401 | Physical Chemistry | 3-1-0-4 | CHE301 | |
| MAT401 | Numerical Methods | 3-1-0-4 | MAT301 | |
| CSE401 | Operating Systems | 2-0-2-4 | CSE301 | |
| ENG402 | Heat Transfer | 3-1-0-4 | ENG303 | |
| ENG403 | Control Systems | 3-1-0-4 | ENG304 | |
| ENG404 | Electrical Machines | 3-1-0-4 | ENG204 | |
| ENG405 | Mechanics of Solids | 3-1-0-4 | ENG302 | |
| ENG406 | Microprocessors and Microcontrollers | 2-0-2-4 | ENG305 | |
| ENG407 | Technical Writing and Communication | 2-0-0-2 | - | |
| ENG408 | Industrial Training II | 0-0-2-2 | - | |
| Semester V | ENG501 | Engineering Mathematics V | 3-1-0-4 | ENG401 |
| PHY501 | Nuclear Physics and Applications | 3-1-0-4 | PHY401 | |
| CHE501 | Chemical Engineering Principles | 3-1-0-4 | CHE401 | |
| MAT501 | Complex Variables and Transform Methods | 3-1-0-4 | MAT401 | |
| CSE501 | Machine Learning | 2-0-2-4 | CSE401 | |
| ENG502 | Advanced Thermodynamics | 3-1-0-4 | ENG402 | |
| ENG503 | Power Systems Analysis | 3-1-0-4 | ENG404 | |
| ENG504 | Robotics and Automation | 3-1-0-4 | ENG403 | |
| ENG505 | Finite Element Methods | 3-1-0-4 | ENG501 | |
| ENG506 | Advanced Computer Architecture | 2-0-2-4 | CSE401 | |
| ENG507 | Research Methodology | 2-0-0-2 | - | |
| ENG508 | Capstone Project I | 0-0-4-6 | - | |
| Semester VI | ENG601 | Engineering Mathematics VI | 3-1-0-4 | ENG501 |
| PHY601 | Optics and Laser Applications | 3-1-0-4 | PHY501 | |
| CHE601 | Biochemistry and Biotechnology | 3-1-0-4 | CHE501 | |
| MAT601 | Applied Mathematics | 3-1-0-4 | MAT501 | |
| CSE601 | Deep Learning | 2-0-2-4 | CSE501 | |
| ENG602 | Energy Conversion Systems | 3-1-0-4 | ENG502 | |
| ENG603 | Smart Grid Technologies | 3-1-0-4 | ENG503 | |
| ENG604 | Advanced Control Systems | 3-1-0-4 | ENG504 | |
| ENG605 | Computational Mechanics | 3-1-0-4 | ENG505 | |
| ENG606 | Embedded Systems | 2-0-2-4 | CSE501 | |
| ENG607 | Entrepreneurship and Innovation | 2-0-0-2 | - | |
| ENG608 | Capstone Project II | 0-0-4-6 | ENG508 | |
| Semester VII | ENG701 | Engineering Mathematics VII | 3-1-0-4 | ENG601 |
| PHY701 | Advanced Electromagnetic Fields | 3-1-0-4 | PHY601 | |
| CHE701 | Environmental Engineering | 3-1-0-4 | CHE601 | |
| MAT701 | Mathematical Modeling and Simulation | 3-1-0-4 | MAT601 | |
| CSE701 | Computer Vision | 2-0-2-4 | CSE601 | |
| ENG702 | Renewable Energy Technologies | 3-1-0-4 | ENG602 | |
| ENG703 | Power Electronics | 3-1-0-4 | ENG603 | |
| ENG704 | Advanced Robotics | 3-1-0-4 | ENG604 | |
| ENG705 | Numerical Analysis | 3-1-0-4 | ENG605 | |
| ENG706 | Advanced Software Engineering | 2-0-2-4 | CSE601 | |
| ENG707 | Leadership in Engineering | 2-0-0-2 | - | |
| ENG708 | Internship | 0-0-6-12 | - | |
| Semester VIII | ENG801 | Engineering Mathematics VIII | 3-1-0-4 | ENG701 |
| PHY801 | Condensed Matter Physics | 3-1-0-4 | PHY701 | |
| CHE801 | Industrial Chemistry | 3-1-0-4 | CHE701 | |
| MAT801 | Advanced Statistical Methods | 3-1-0-4 | MAT701 | |
| CSE801 | Natural Language Processing | 2-0-2-4 | CSE701 | |
| ENG802 | Sustainable Engineering | 3-1-0-4 | ENG702 | |
| ENG803 | Advanced Power Systems | 3-1-0-4 | ENG703 | |
| ENG804 | Autonomous Systems | 3-1-0-4 | ENG704 | |
| ENG805 | Computational Fluid Dynamics | 3-1-0-4 | ENG705 | |
| ENG806 | Software Testing and Quality Assurance | 2-0-2-4 | CSE701 | |
| ENG807 | Global Engineering Challenges | 2-0-0-2 | - | |
| ENG808 | Final Year Project | 0-0-6-12 | ENG708 |
These advanced courses are designed to deepen students' expertise in specialized areas, preparing them for cutting-edge roles in engineering and research. Each course is tailored to provide both theoretical knowledge and practical application through lab work and real-world case studies.
This course delves into the mathematical foundations of machine learning algorithms, including supervised and unsupervised learning techniques. Students explore deep learning architectures, neural networks, and their applications in computer vision and natural language processing. Through hands-on labs using Python libraries like TensorFlow and PyTorch, students gain experience building predictive models for complex datasets.
Building upon foundational knowledge in machine learning, this course focuses on advanced neural network architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students learn to implement large-scale models using distributed computing frameworks like Apache Spark and Kubernetes.
This course explores the principles and techniques used in analyzing and interpreting visual information from digital images and videos. Topics include image segmentation, object detection, feature extraction, and real-time tracking algorithms. Students implement projects using OpenCV and YOLO frameworks.
Focused on the intersection of linguistics and artificial intelligence, this course covers text classification, sentiment analysis, language modeling, and machine translation. Students work with large language models (LLMs) and develop applications for chatbots, summarization tools, and speech recognition systems.
This course provides a comprehensive understanding of cybersecurity threats and defense mechanisms. Students learn penetration testing, network security protocols, cryptography, and secure coding practices. The curriculum includes real-world scenarios such as defending against ransomware attacks and securing cloud infrastructure.
This advanced course examines the integration of renewable energy sources into electrical grids using smart technologies. Students explore grid stability, demand response systems, energy storage solutions, and power quality management. Labs involve simulating smart grid operations using software like MATLAB/Simulink.
Building on basic control theory, this course covers modern control design techniques including state-space methods, optimal control, and robust control. Students apply these concepts to real-time systems such as autonomous vehicles and industrial automation processes.
This course introduces students to designing and programming embedded systems using microcontrollers and real-time operating systems. Topics include hardware-software co-design, interrupt handling, and communication protocols. Projects involve developing IoT devices and robotic controllers.
Focused on sustainable energy solutions, this course covers solar, wind, hydroelectric, and geothermal power generation technologies. Students evaluate energy conversion efficiency, perform techno-economic analyses, and design hybrid systems for rural electrification.
This course explores cutting-edge manufacturing techniques such as 3D printing, laser cutting, and precision machining. Students gain experience using CAD/CAM software, industrial automation tools, and quality control methods in advanced manufacturing environments.
Navrachana University Vadodara places significant emphasis on project-based learning (PBL) to enhance students' practical skills and real-world application capabilities. The approach encourages collaboration, innovation, and critical thinking through structured projects that span multiple semesters.
Mini-projects are undertaken in the second and third years of study, allowing students to apply theoretical concepts in hands-on experiments and simulations. These projects typically last 8–12 weeks and involve small teams of 3–5 students working under faculty guidance.
The final-year project is a significant component of the program, spanning one full semester. Students select from industry-sponsored projects or propose their own research initiatives. Projects are evaluated based on innovation, technical depth, presentation quality, and impact potential.
Students begin selecting their project topics in the fifth semester, with faculty mentors assigned based on student interests and expertise availability. The selection process involves submitting a proposal, attending interviews, and receiving feedback from academic advisors.
Projects are assessed using rubrics that evaluate:
This rigorous evaluation framework ensures that students develop a comprehensive understanding of engineering principles while building professional competencies essential for future careers.
