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Fees
₹8,50,000
Placement
94.5%
Avg Package
₹5,50,000
Highest Package
₹8,50,000
Fees
₹8,50,000
Placement
94.5%
Avg Package
₹5,50,000
Highest Package
₹8,50,000
Seats
120
Students
1,200
Seats
120
Students
1,200
The engineering program at Monark University Ahmedabad is meticulously structured across eight semesters, with each semester comprising a balanced mix of core subjects, departmental electives, science electives, and laboratory courses. The curriculum is designed to ensure that students gain both depth and breadth in their technical knowledge while developing critical thinking and innovation skills.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MATH101 | Mathematics I | 3-1-0-4 | - |
| 1 | PHYS101 | Physics I | 3-1-0-4 | - |
| 1 | CHEM101 | Chemistry I | 3-1-0-4 | - |
| 1 | ENG101 | Engineering Graphics | 2-1-0-3 | - |
| 1 | CPRO101 | Computer Programming | 2-1-0-3 | - |
| 1 | ES101 | English for Engineering | 2-0-0-2 | - |
| 1 | L101 | Lab I: Mathematics and Physics | 0-0-3-2 | - |
| 2 | MATH201 | Mathematics II | 3-1-0-4 | MATH101 |
| 2 | PHYS201 | Physics II | 3-1-0-4 | PHYS101 |
| 2 | CHEM201 | Chemistry II | 3-1-0-4 | CHEM101 |
| 2 | ENG201 | Engineering Mechanics | 3-1-0-4 | - |
| 2 | CPRO201 | Data Structures and Algorithms | 3-1-0-4 | CPRO101 |
| 2 | L201 | Lab II: Computer Programming | 0-0-3-2 | - |
| 3 | MATH301 | Mathematics III | 3-1-0-4 | MATH201 |
| 3 | PHYS301 | Physics III | 3-1-0-4 | PHYS201 |
| 3 | CHEM301 | Chemistry III | 3-1-0-4 | CHEM201 |
| 3 | ENG301 | Thermodynamics | 3-1-0-4 | - |
| 3 | CPRO301 | Object-Oriented Programming | 3-1-0-4 | CPRO201 |
| 3 | L301 | Lab III: Engineering Mechanics | 0-0-3-2 | - |
| 4 | MATH401 | Mathematics IV | 3-1-0-4 | MATH301 |
| 4 | PHYS401 | Physics IV | 3-1-0-4 | PHYS301 |
| 4 | CHEM401 | Chemistry IV | 3-1-0-4 | CHEM301 |
| 4 | ENG401 | Fluid Mechanics | 3-1-0-4 | - |
| 4 | CPRO401 | Database Management Systems | 3-1-0-4 | CPRO301 |
| 4 | L401 | Lab IV: Thermodynamics and Fluid Mechanics | 0-0-3-2 | - |
| 5 | MATH501 | Mathematics V | 3-1-0-4 | MATH401 |
| 5 | PHYS501 | Physics V | 3-1-0-4 | PHYS401 |
| 5 | CHEM501 | Chemistry V | 3-1-0-4 | CHEM401 |
| 5 | ENG501 | Electrical Circuits | 3-1-0-4 | - |
| 5 | CPRO501 | Operating Systems | 3-1-0-4 | CPRO401 |
| 5 | L501 | Lab V: Electrical Circuits and Electronics | 0-0-3-2 | - |
| 6 | MATH601 | Mathematics VI | 3-1-0-4 | MATH501 |
| 6 | PHYS601 | Physics VI | 3-1-0-4 | PHYS501 |
| 6 | CHEM601 | Chemistry VI | 3-1-0-4 | CHEM501 |
| 6 | ENG601 | Mechanics of Materials | 3-1-0-4 | - |
| 6 | CPRO601 | Software Engineering | 3-1-0-4 | CPRO501 |
| 6 | L601 | Lab VI: Mechanics of Materials and Materials Testing | 0-0-3-2 | - |
| 7 | MATH701 | Mathematics VII | 3-1-0-4 | MATH601 |
| 7 | PHYS701 | Physics VII | 3-1-0-4 | PHYS601 |
| 7 | CHEM701 | Chemistry VII | 3-1-0-4 | CHEM601 |
| 7 | ENG701 | Control Systems | 3-1-0-4 | - |
| 7 | CPRO701 | Computer Networks | 3-1-0-4 | CPRO601 |
| 7 | L701 | Lab VII: Control Systems and Instrumentation | 0-0-3-2 | - |
| 8 | MATH801 | Mathematics VIII | 3-1-0-4 | MATH701 |
| 8 | PHYS801 | Physics VIII | 3-1-0-4 | PHYS701 |
| 8 | CHEM801 | Chemistry VIII | 3-1-0-4 | CHEM701 |
| 8 | ENG801 | Project Work | 2-0-0-6 | - |
| 8 | CPRO801 | Capstone Project | 2-0-0-6 | CPRO701 |
| 8 | L801 | Lab VIII: Final Project and Research | 0-0-3-2 | - |
Departmental electives form a crucial part of the engineering curriculum, allowing students to explore specialized areas based on their interests and career goals. These courses provide in-depth knowledge and practical skills necessary for advanced specialization.
Course 1: Machine Learning Fundamentals
This course introduces students to fundamental concepts in machine learning including supervised and unsupervised learning techniques, neural networks, and deep learning architectures. Students will implement algorithms using Python and TensorFlow, gaining hands-on experience with real-world datasets. The course emphasizes the theoretical foundations of machine learning while providing practical applications through projects involving computer vision, natural language processing, and robotics.
Course 2: Cybersecurity and Network Defense
This advanced course covers modern cybersecurity threats, network defense mechanisms, and secure system design principles. Students will learn about encryption techniques, penetration testing, incident response strategies, and compliance frameworks. The course includes hands-on labs where students simulate cyber attacks and defend against them using industry-standard tools such as Wireshark, Metasploit, and Nmap.
Course 3: Renewable Energy Technologies
This course explores the design, implementation, and optimization of renewable energy systems including solar panels, wind turbines, hydroelectric generators, and bioenergy conversion technologies. Students will analyze energy storage systems, smart grid integration, and environmental impact assessments. Projects involve designing and simulating renewable energy installations using software tools like MATLAB and PSpice.
Course 4: Advanced Materials Science
This course delves into the structure, properties, and applications of advanced materials including composites, ceramics, polymers, and nanomaterials. Students will study material characterization techniques, phase diagrams, and processing methods. The course includes laboratory experiments involving synthesis and testing of new materials, providing practical insights into material science research and development.
Course 5: Robotics and Automation
This course combines mechanical engineering principles with computer science and artificial intelligence to build autonomous robotic systems. Students will learn about sensor integration, motion control, path planning, and human-robot interaction. The course includes building and programming robots using Arduino, Raspberry Pi, and ROS (Robot Operating System).
Course 6: Data Analytics and Visualization
This course teaches students how to extract meaningful insights from large datasets using statistical methods and visualization tools. Topics include data mining, predictive modeling, regression analysis, and machine learning for data science. Students will use Python libraries like pandas, scikit-learn, and matplotlib to analyze real-world datasets and present findings effectively.
Course 7: Sustainable Engineering Design
This course focuses on designing sustainable solutions that balance environmental, economic, and social considerations. Students will learn about life cycle assessment, eco-design principles, circular economy concepts, and green technology innovations. Projects involve developing sustainable products or systems addressing real-world challenges such as waste reduction, energy efficiency, or urban sustainability.
Course 8: Advanced Power Systems
This course examines the operation and control of modern power systems including transmission lines, transformers, generators, and renewable energy integration. Students will study grid stability, load forecasting, power quality issues, and smart grid technologies. The course includes simulations using MATLAB/Simulink and practical case studies from real-world power system operations.
Course 9: Computational Fluid Dynamics
This course introduces students to numerical methods for solving fluid flow problems using computational tools. Students will learn about governing equations, mesh generation, boundary conditions, and turbulence modeling. The course includes practical sessions using ANSYS Fluent, OpenFOAM, and other CFD software to simulate complex flow scenarios.
Course 10: Human Factors in Engineering
This course explores the interaction between humans and engineered systems from ergonomic, cognitive, and safety perspectives. Students will study human perception, decision-making processes, user interface design, and human reliability analysis. The course includes workshops on usability testing, risk assessment, and human-centered design methodologies.
Monark University Ahmedabad places a strong emphasis on project-based learning as a core component of engineering education. Our philosophy is rooted in the belief that real-world experience enhances understanding and develops critical skills essential for professional success.
The structure of our project-based learning includes mandatory mini-projects during the second and third years, followed by an advanced final-year capstone project. Mini-projects are designed to be completed within 6-8 weeks and involve solving specific engineering problems using concepts learned in coursework. These projects encourage collaboration among students, foster creativity, and promote practical application of theoretical knowledge.
Final-year thesis/capstone projects span 12-16 weeks and are typically conducted in collaboration with industry partners or research institutions. Students select projects based on their interests, faculty guidance, and available resources. The selection process involves a proposal submission, review by faculty mentors, and final approval by the department head.
Evaluation criteria for project-based learning include technical depth, innovation, presentation quality, teamwork effectiveness, and adherence to deadlines. Students are assessed through peer reviews, faculty evaluations, and public presentations. Additionally, projects are often submitted as reports or prototypes, contributing to their professional portfolio and potentially leading to publications or patents.
The university provides dedicated project rooms, access to specialized software, and mentorship from experienced faculty members throughout the project duration. This support system ensures that students receive guidance on research methodologies, technical challenges, and professional presentation skills necessary for success in industry or academia.
