Curriculum Overview
The curriculum at Maganbhai Adenwala Mahagujarat University Nadiad is meticulously designed to align with global standards while incorporating local industry needs. It ensures a balanced progression from foundational knowledge to advanced specialization, enabling students to develop both technical proficiency and critical thinking skills.
Each semester is structured around core subjects, departmental electives, science electives, and laboratory sessions. The curriculum emphasizes not only academic excellence but also practical application through real-world projects and industry exposure. This holistic approach prepares graduates for immediate contribution in their chosen fields.
Course Structure
The engineering program spans eight semesters, with each semester comprising core courses, departmental electives, science electives, and laboratory sessions. The following table outlines the course structure:
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | PHYS101 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CHEM101 | Chemistry for Engineering | 3-1-0-4 | - |
| 1 | ES101 | Engineering Graphics | 2-1-0-3 | - |
| 1 | ECE101 | Basic Electrical Circuits | 3-1-0-4 | - |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | MATH201 | Statistics and Probability | 3-1-0-4 | - |
| 2 | PHYS201 | Thermodynamics | 3-1-0-4 | PHYS101 |
| 2 | CIV201 | Mechanics of Materials | 3-1-0-4 | - |
| 2 | ECE201 | Electrical Circuits and Networks | 3-1-0-4 | ECE101 |
| 3 | ENG301 | Signals and Systems | 3-1-0-4 | ENG201 |
| 3 | MATH301 | Differential Equations | 3-1-0-4 | ENG201 |
| 3 | CIV301 | Structural Analysis | 3-1-0-4 | CIV201 |
| 3 | ECE301 | Control Systems | 3-1-0-4 | ECE201 |
| 3 | MECH301 | Machine Design | 3-1-0-4 | - |
| 4 | ENG401 | Probability and Statistics for Engineers | 3-1-0-4 | MATH201 |
| 4 | CIV401 | Transportation Engineering | 3-1-0-4 | CIV301 |
| 4 | ECE401 | Digital Signal Processing | 3-1-0-4 | ENG301 |
| 4 | MECH401 | Thermal Engineering | 3-1-0-4 | - |
| 4 | CS401 | Data Structures and Algorithms | 3-1-0-4 | - |
| 5 | CS501 | Artificial Intelligence | 3-1-0-4 | CS401 |
| 5 | MECH501 | Robotics and Automation | 3-1-0-4 | MECH401 |
| 5 | CIV501 | Environmental Engineering | 3-1-0-4 | CIV401 |
| 5 | ECE501 | Wireless Communication | 3-1-0-4 | ECE401 |
| 6 | CS601 | Machine Learning | 3-1-0-4 | CS501 |
| 6 | MECH601 | Manufacturing Technology | 3-1-0-4 | - |
| 6 | CIV601 | Construction Management | 3-1-0-4 | - |
| 6 | ECE601 | Embedded Systems | 3-1-0-4 | ECE501 |
| 7 | CS701 | Big Data Analytics | 3-1-0-4 | CS601 |
| 7 | MECH701 | Advanced Thermodynamics | 3-1-0-4 | - |
| 7 | CIV701 | Geotechnical Engineering | 3-1-0-4 | - |
| 7 | ECE701 | VLSI Design | 3-1-0-4 | ECE601 |
| 8 | CS801 | Capstone Project | 2-0-6-8 | - |
| 8 | MECH801 | Final Year Thesis | 2-0-6-8 | - |
| 8 | CIV801 | Project Management | 3-1-0-4 | - |
| 8 | ECE801 | Internet of Things | 3-1-0-4 | ECE701 |
Advanced Departmental Electives
The advanced departmental elective courses provide students with specialized knowledge and skills relevant to their chosen career paths. These courses are taught by faculty members who are experts in their respective fields.
- Machine Learning (CS601): This course covers supervised and unsupervised learning algorithms, neural networks, deep learning frameworks, and applications in computer vision and natural language processing. Students will work on projects involving real datasets to apply theoretical concepts practically.
- Big Data Analytics (CS701): Students learn about data mining techniques, Hadoop ecosystems, Spark programming, and real-time analytics for business intelligence. The course includes hands-on labs using industry-standard tools such as Apache Spark and Hadoop.
- Robotics and Automation (MECH501): The course explores robot kinematics, control systems, sensor integration, and automation in manufacturing environments. Students will build and program robots to perform specific tasks, gaining practical experience in robotics engineering.
- VLSI Design (ECE701): This elective introduces students to digital design principles, ASIC development, FPGA implementation, and hardware description languages like Verilog and VHDL. Projects involve designing and simulating integrated circuits using industry-standard tools.
- Environmental Engineering (CIV501): Focused on pollution control, waste management, water treatment systems, and sustainable engineering practices for environmental impact mitigation. Students will analyze case studies and propose solutions to environmental challenges.
- Digital Signal Processing (ECE401): Covers discrete-time signal processing techniques, filter design, frequency domain analysis, and DSP applications in audio and image processing. Labs involve implementing algorithms using MATLAB and Python.
- Advanced Thermodynamics (MECH701): Students study thermodynamic cycles, phase equilibrium, energy conversion systems, and advanced topics in heat transfer. The course includes laboratory experiments to validate theoretical concepts.
- Artificial Intelligence (CS501): This course delves into AI fundamentals, problem-solving techniques, knowledge representation, and agent-based systems. Students will develop intelligent agents and explore applications in game theory and robotics.
- Construction Management (CIV601): Provides insights into project planning, resource allocation, risk management, and quality control in construction projects. Case studies from real-world construction sites enhance learning outcomes.
- Embedded Systems (ECE601): Students explore microcontroller architectures, embedded programming, real-time operating systems, and IoT device development. Labs involve building embedded systems using Arduino and Raspberry Pi platforms.
Project-Based Learning Philosophy
The department strongly advocates for a project-based learning approach that integrates theoretical knowledge with practical skills. From the first year, students engage in mini-projects that reinforce classroom learning through hands-on experimentation.
Mini-projects in early semesters focus on basic design tasks such as circuit simulation, simple mechanical designs, or data analysis using spreadsheets and programming tools. As students progress, the complexity increases, with advanced mini-projects involving software development, prototype building, and system integration.
The final-year capstone project is a comprehensive endeavor that spans the entire semester. Students select projects aligned with their interests or industry needs, working closely with faculty mentors who provide guidance and feedback throughout the process. Projects are evaluated based on innovation, technical depth, feasibility, and presentation quality.