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Fees
₹7,80,000
Placement
93.0%
Avg Package
₹7,50,000
Highest Package
₹18,00,000
Fees
₹7,80,000
Placement
93.0%
Avg Package
₹7,50,000
Highest Package
₹18,00,000
Seats
180
Students
1,200
Seats
180
Students
1,200
The curriculum of the Engineering program at Mata Gujri University Kishangunj is designed to provide a balanced blend of theoretical knowledge and practical skills, preparing students for dynamic careers in engineering and technology. The program spans eight semesters, with each semester building upon the previous one to ensure a progressive learning experience.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | ENG102 | Physics for Engineers | 3-1-0-4 | - |
| 1 | ENG103 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | ENG104 | Introduction to Programming | 2-1-0-3 | - |
| 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 and Networks | 3-1-0-4 | ENG102 |
| 2 | ENG203 | Thermodynamics | 3-1-0-4 | ENG102 |
| 2 | ENG204 | Materials Science | 3-1-0-4 | ENG103 |
| 2 | ENG205 | Computer Programming Lab | 0-0-3-1 | ENG104 |
| 2 | ENG206 | Engineering Workshop | 0-0-3-1 | - |
| 3 | ENG301 | Fluid Mechanics | 3-1-0-4 | ENG203 |
| 3 | ENG302 | Signals and Systems | 3-1-0-4 | ENG201 |
| 3 | ENG303 | Electronic Devices and Circuits | 3-1-0-4 | ENG202 |
| 3 | ENG304 | Structural Analysis | 3-1-0-4 | ENG201 |
| 3 | ENG305 | Data Structures and Algorithms | 3-1-0-4 | ENG104 |
| 3 | ENG306 | Engineering Economics | 2-1-0-3 | ENG201 |
| 4 | ENG401 | Control Systems | 3-1-0-4 | ENG302 |
| 4 | ENG402 | Power Generation and Distribution | 3-1-0-4 | ENG202 |
| 4 | ENG403 | Heat Transfer | 3-1-0-4 | ENG203 |
| 4 | ENG404 | Machine Design | 3-1-0-4 | ENG201 |
| 4 | ENG405 | Software Engineering | 3-1-0-4 | ENG305 |
| 4 | ENG406 | Project Management | 2-1-0-3 | - |
| 5 | ENG501 | Advanced Mathematics | 3-1-0-4 | ENG201 |
| 5 | ENG502 | Operations Research | 3-1-0-4 | ENG201 |
| 5 | ENG503 | Neural Networks | 3-1-0-4 | ENG305 |
| 5 | ENG504 | Advanced Structural Analysis | 3-1-0-4 | ENG304 |
| 5 | ENG505 | Cybersecurity Fundamentals | 3-1-0-4 | ENG305 |
| 5 | ENG506 | Renewable Energy Systems | 3-1-0-4 | ENG203 |
| 6 | ENG601 | Advanced Control Systems | 3-1-0-4 | ENG401 |
| 6 | ENG602 | Power Electronics | 3-1-0-4 | ENG202 |
| 6 | ENG603 | Advanced Manufacturing Processes | 3-1-0-4 | ENG404 |
| 6 | ENG604 | Embedded Systems | 3-1-0-4 | ENG303 |
| 6 | ENG605 | Machine Learning | 3-1-0-4 | ENG503 |
| 6 | ENG606 | Sustainable Development | 2-1-0-3 | - |
| 7 | ENG701 | Capstone Project I | 0-0-6-3 | ENG501, ENG604 |
| 7 | ENG702 | Advanced Research Methods | 2-1-0-3 | - |
| 7 | ENG703 | Project Evaluation and Presentation | 0-0-3-1 | - |
| 8 | ENG801 | Capstone Project II | 0-0-6-3 | ENG701 |
| 8 | ENG802 | Final Research Thesis | 0-0-6-3 | - |
| 8 | ENG803 | Internship | 0-0-6-3 | - |
The department offers a wide array of advanced departmental elective courses designed to cater to diverse interests and career aspirations. These courses are taught by experienced faculty members who are leaders in their respective fields.
This course provides students with an in-depth understanding of neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students learn to implement models using frameworks like TensorFlow and PyTorch, and apply them to real-world problems such as image classification, natural language processing, and speech recognition.
This course covers essential concepts in cybersecurity, including cryptography, network security protocols, penetration testing, and incident response. Students gain hands-on experience with security tools like Wireshark, Metasploit, and Nmap, preparing them for roles in information security and risk management.
This course explores the principles and applications of renewable energy systems, including solar photovoltaics, wind turbines, hydroelectric plants, and geothermal systems. Students study the design, installation, and maintenance of these technologies, gaining insights into current trends and future developments in sustainable energy.
This course delves into advanced topics in control theory, including state-space representation, optimal control, and robust control. Students learn to design controllers for complex systems using MATLAB/Simulink and apply these concepts to industrial applications such as robotics, aerospace, and process control.
This course focuses on the principles of software architecture, including design patterns, microservices, and scalability. Students learn to create scalable, maintainable, and secure software systems using modern frameworks and methodologies like Agile and DevOps.
This course builds upon foundational knowledge of data structures and algorithms, introducing students to advanced topics such as graph algorithms, dynamic programming, and computational complexity. Students develop problem-solving skills through practical coding exercises and competitive programming challenges.
This course covers the design and implementation of embedded systems using microcontrollers and real-time operating systems. Students gain experience with hardware-software integration, sensor interfacing, and low-level programming, preparing them for careers in IoT, automotive, and industrial automation.
This course explores the structure, properties, and applications of advanced materials such as composites, nanomaterials, and smart materials. Students learn to characterize materials using techniques like X-ray diffraction, scanning electron microscopy, and spectroscopy.
This course focuses on the design and analysis of power electronic converters and motor drives. Students study topics such as rectifiers, inverters, choppers, and variable frequency drives, gaining practical experience through laboratory experiments and simulations.
This course combines robotics with machine learning to develop intelligent robotic systems. Students learn to program robots using ROS (Robot Operating System) and implement machine learning algorithms for perception, navigation, and manipulation tasks.
The department places a strong emphasis on project-based learning as a core component of the curriculum. Projects are designed to simulate real-world engineering challenges and encourage students to apply their theoretical knowledge in practical settings.
The mandatory mini-projects in the second and fourth semesters allow students to work in teams, developing solutions to specific problems under the guidance of faculty mentors. These projects help students develop critical thinking, communication, and collaboration skills while reinforcing classroom concepts.
The final-year thesis/capstone project provides students with an opportunity to conduct independent research or develop a comprehensive solution to a complex engineering problem. Students are paired with faculty members who serve as mentors throughout the project lifecycle, offering guidance on methodology, implementation, and documentation.
Project selection is based on student interests, faculty expertise, and industry relevance. Students can propose their own ideas or choose from a list of pre-approved projects provided by faculty members. The evaluation criteria include project proposal, progress reports, final presentation, and written documentation.
