Comprehensive Curriculum Structure

The Engineering program at Manav Rachna University Faridabad is structured over eight semesters, with a balanced blend of foundational subjects, core engineering principles, departmental electives, and practical experiences designed to prepare students for professional success.

Semester-wise Course Structure

Semester	Course Code	Course Title	Credit (L-T-P-C)	Prerequisites
1	ENG101	Engineering Mathematics I	3-1-0-4	None
1	PHY101	Physics for Engineers	3-1-0-4	None
1	CHE101	Chemistry of Materials	3-1-0-4	None
1	ENG102	Introduction to Engineering	2-0-0-2	None
1	CS101	Basic Programming Concepts	2-0-2-4	None
2	ENG103	Engineering Mathematics II	3-1-0-4	ENG101
2	ECE101	Basic Electrical Circuits	3-1-0-4	PHY101
2	MAT101	Mechanics of Solids	3-1-0-4	ENG101
2	CS102	Data Structures & Algorithms	3-1-0-4	CS101
2	ENG104	Introduction to Materials Science	3-1-0-4	CHE101
3	ENG201	Thermodynamics	3-1-0-4	ENG103
3	ECE201	Electronics Devices & Circuits	3-1-0-4	ECE101
3	MAT201	Fluid Mechanics	3-1-0-4	MAT101
3	CS201	Database Management Systems	3-1-0-4	CS102
3	ENG202	Control Systems	3-1-0-4	ENG103
4	ENG203	Signals & Systems	3-1-0-4	ENG103
4	ECE202	Microprocessor Architecture	3-1-0-4	ECE201
4	MAT202	Advanced Mechanics of Materials	3-1-0-4	MAT101
4	CS202	Computer Networks	3-1-0-4	CS201
4	ENG204	Digital Signal Processing	3-1-0-4	ENG203
5	ENG301	Machine Learning	3-1-0-4	CS202
5	ECE301	Embedded Systems	3-1-0-4	ECE202
5	MAT301	Heat Transfer	3-1-0-4	ENG201
5	CS301	Software Engineering	3-1-0-4	CS202
5	ENG302	Finite Element Analysis	3-1-0-4	MAT202
6	ENG303	Advanced Control Systems	3-1-0-4	ENG202
6	ECE302	Power Electronics	3-1-0-4	ECE201
6	MAT302	Computational Fluid Dynamics	3-1-0-4	MAT201
6	CS302	Cloud Computing	3-1-0-4	CS202
6	ENG304	Advanced Materials Science	3-1-0-4	MAT201
7	ENG401	Capstone Project I	0-0-6-6	All previous courses
7	ENG402	Capstone Project II	0-0-6-6	All previous courses
7	ENG403	Internship	0-0-0-6	All previous courses

Detailed Departmental Elective Courses

The departmental elective courses offered in the program are designed to provide students with advanced knowledge and specialized skills in their chosen field of interest. These courses are taught by experienced faculty members who are experts in their respective domains.

Machine Learning (CS301)

This course delves into the fundamentals of machine learning algorithms, including supervised and unsupervised learning techniques. Students learn to implement models using Python libraries like Scikit-learn and TensorFlow. The course covers topics such as regression analysis, classification, clustering, neural networks, and deep learning architectures.

Embedded Systems (ECE301)

This elective introduces students to the design and development of embedded systems, focusing on microcontroller-based applications. Topics include hardware-software co-design, real-time operating systems, device drivers, and sensor integration. Students work on projects involving Arduino and Raspberry Pi platforms.

Heat Transfer (MAT301)

This course explores conduction, convection, and radiation heat transfer mechanisms. Students learn to apply governing equations to solve practical problems in engineering applications such as thermal insulation, cooling systems, and heat exchangers. The course includes laboratory experiments to validate theoretical concepts.

Software Engineering (CS302)

This course covers the software development lifecycle from requirements gathering to deployment and maintenance. Students learn about agile methodologies, version control tools, testing strategies, and project management techniques. Practical assignments involve developing full-stack web applications using modern frameworks.

Finite Element Analysis (ENG302)

This advanced course teaches students how to use finite element methods for solving engineering problems. It covers mesh generation, boundary conditions, material properties, and solution techniques. Applications include structural analysis, fluid dynamics, and electromagnetic field simulation.

Advanced Control Systems (ENG303)

This elective builds upon basic control theory to explore advanced topics such as state-space representation, optimal control, robust control, and nonlinear systems. Students gain hands-on experience with MATLAB/Simulink for system modeling and simulation.

Power Electronics (ECE302)

This course focuses on the design and analysis of power electronic converters and inverters. Students learn about switching devices, rectifiers, DC-DC converters, and AC-AC converters. Laboratory sessions involve building prototype circuits and analyzing performance characteristics.

Computational Fluid Dynamics (MAT302)

This course introduces students to numerical methods for solving fluid flow problems. It covers finite difference and finite volume methods, turbulence modeling, and grid independence studies. Students use CFD software packages like ANSYS Fluent and OpenFOAM for simulations.

Cloud Computing (CS302)

This course explores cloud computing models, architectures, and services offered by platforms like AWS, Azure, and Google Cloud. Students learn about virtualization, containerization, microservices, and DevOps practices. Hands-on labs involve deploying applications on cloud platforms.

Advanced Materials Science (ENG304)

This course examines advanced materials properties and their applications in engineering systems. Topics include nanomaterials, composite materials, smart materials, and biodegradable polymers. Students engage in research projects related to material characterization and processing techniques.

Project-Based Learning Philosophy

The program places a strong emphasis on project-based learning as an integral part of the curriculum. This approach enables students to apply theoretical knowledge to real-world scenarios, enhancing their problem-solving abilities and technical skills.

Mini-Projects

Mini-projects are conducted in the third and fourth semesters, allowing students to work on small-scale problems within specific domains. These projects typically last 3-4 weeks and involve group collaboration under faculty supervision.

Final-Year Thesis/Capstone Project

The capstone project spans both the seventh and eighth semesters, requiring students to complete an extensive research or design task under the guidance of a faculty mentor. The project must demonstrate mastery in engineering principles and innovation capabilities.

Project Selection Process

Students select their projects based on interests, available resources, and faculty availability. Faculty mentors are assigned according to the alignment of expertise with project requirements. Regular progress reviews ensure timely completion and quality outcomes.

Evaluation Criteria

Projects are evaluated based on technical execution, innovation, documentation quality, presentation skills, and teamwork effectiveness. Peer review and faculty feedback contribute to overall assessment scores.