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Fees
₹12,00,000
Placement
92.0%
Avg Package
₹4,20,000
Highest Package
₹8,00,000
Fees
₹12,00,000
Placement
92.0%
Avg Package
₹4,20,000
Highest Package
₹8,00,000
Seats
120
Students
300
Seats
120
Students
300
The Engineering program at Mahindra University Telangana is structured over 8 semesters, with each semester carrying a credit load of 16-18 credits. The curriculum balances foundational sciences, core engineering principles, departmental electives, and practical training through lab sessions and projects.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | ENG102 | Engineering Physics | 3-1-0-4 | - |
| 1 | ENG103 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | ENG104 | Engineering Drawing & Graphics | 2-0-2-3 | - |
| 1 | ENG105 | Programming for Engineers | 2-0-2-3 | - |
| 1 | ENG106 | Introduction to Engineering | 2-0-0-2 | - |
| 1 | ENG107 | Physical Education & Sports | 1-0-0-1 | - |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Electrical Circuits & Networks | 3-1-0-4 | - |
| 2 | ENG203 | Mechanics of Materials | 3-1-0-4 | - |
| 2 | ENG204 | Fluid Mechanics & Hydraulic Machines | 3-1-0-4 | - |
| 2 | ENG205 | Engineering Materials | 3-1-0-4 | - |
| 2 | ENG206 | Computer Programming & Data Structures | 2-0-2-3 | ENG105 |
| 2 | ENG207 | Engineering Ethics & Professionalism | 1-0-0-1 | - |
| 3 | ENG301 | Thermodynamics | 3-1-0-4 | ENG201 |
| 3 | ENG302 | Digital Logic & Design | 3-1-0-4 | - |
| 3 | ENG303 | Signals & Systems | 3-1-0-4 | ENG201 |
| 3 | ENG304 | Control Systems | 3-1-0-4 | - |
| 3 | ENG305 | Electromagnetic Fields & Waves | 3-1-0-4 | ENG201 |
| 3 | ENG306 | Object-Oriented Programming with C++ | 2-0-2-3 | ENG105 |
| 3 | ENG307 | Engineering Economics & Management | 2-0-0-2 | - |
| 4 | ENG401 | Microprocessors & Microcontrollers | 3-1-0-4 | ENG302 |
| 4 | ENG402 | Probability & Statistics for Engineers | 3-1-0-4 | ENG201 |
| 4 | ENG403 | Embedded Systems Design | 3-1-0-4 | - |
| 4 | ENG404 | Communication Systems | 3-1-0-4 | ENG303 |
| 4 | ENG405 | Data Structures & Algorithms | 2-0-2-3 | ENG206 |
| 4 | ENG406 | Software Engineering | 2-0-2-3 | - |
| 4 | ENG407 | Environmental Science & Engineering | 2-0-0-2 | - |
| 5 | ENG501 | Advanced Mathematics for Engineers | 3-1-0-4 | ENG201 |
| 5 | ENG502 | Advanced Control Systems | 3-1-0-4 | ENG304 |
| 5 | ENG503 | Machine Learning & AI Fundamentals | 3-1-0-4 | - |
| 5 | ENG504 | Operations Research | 3-1-0-4 | ENG201 |
| 5 | ENG505 | Advanced Data Structures & Algorithms | 2-0-2-3 | ENG405 |
| 5 | ENG506 | Research Methodology & Ethics | 2-0-0-2 | - |
| 5 | ENG507 | Entrepreneurship & Innovation | 2-0-0-2 | - |
| 6 | ENG601 | Advanced Signal Processing | 3-1-0-4 | ENG303 |
| 6 | ENG602 | Power Electronics & Drives | 3-1-0-4 | - |
| 6 | ENG603 | Cybersecurity & Network Protocols | 3-1-0-4 | - |
| 6 | ENG604 | Advanced Control Systems | 3-1-0-4 | ENG502 |
| 6 | ENG605 | Cloud Computing & Big Data Analytics | 3-1-0-4 | - |
| 6 | ENG606 | Advanced Computer Architecture | 2-0-2-3 | - |
| 6 | ENG607 | Project Management & Quality Assurance | 2-0-0-2 | - |
| 7 | ENG701 | Special Topics in AI & Machine Learning | 3-1-0-4 | ENG503 |
| 7 | ENG702 | Advanced Embedded Systems Design | 3-1-0-4 | ENG403 |
| 7 | ENG703 | Renewable Energy Technologies | 3-1-0-4 | - |
| 7 | ENG704 | Industrial Automation & Robotics | 3-1-0-4 | - |
| 7 | ENG705 | Advanced Network Security | 3-1-0-4 | ENG603 |
| 7 | ENG706 | Advanced Software Engineering | 2-0-2-3 | ENG406 |
| 7 | ENG707 | Capstone Project I | 2-0-2-3 | - |
| 8 | ENG801 | Special Topics in Cybersecurity | 3-1-0-4 | ENG603 |
| 8 | ENG802 | Advanced Renewable Energy Systems | 3-1-0-4 | ENG703 |
| 8 | ENG803 | Intelligent Transportation Systems | 3-1-0-4 | - |
| 8 | ENG804 | Advanced Robotics & Automation | 3-1-0-4 | ENG704 |
| 8 | ENG805 | Capstone Project II | 2-0-2-3 | - |
| 8 | ENG806 | Professional Internship | 4-0-0-4 | - |
| 8 | ENG807 | Final Year Thesis | 6-0-0-6 | - |
Departmental electives are designed to allow students to explore specialized areas of interest and gain deeper insights into advanced engineering concepts. These courses are typically offered in the later semesters and require prerequisite knowledge.
This course introduces students to machine learning algorithms, neural networks, and deep learning frameworks. Students learn to implement models using Python libraries like TensorFlow and PyTorch. The course emphasizes practical applications in image recognition, natural language processing, and predictive analytics.
Building upon foundational knowledge of signals and systems, this course explores advanced topics such as filter design, spectral analysis, and wavelet transforms. Students apply these techniques to real-world problems in biomedical signal processing and audio engineering.
This course covers modern cybersecurity threats, encryption methods, network security protocols, and incident response strategies. Students gain hands-on experience with penetration testing tools and learn to design secure systems using industry-standard frameworks.
This course focuses on designing complex embedded systems for applications in IoT, robotics, and smart devices. Students work with microcontrollers like ARM Cortex-M series and develop real-time operating system (RTOS) solutions for resource-constrained environments.
Students explore automation technologies used in manufacturing industries. The course covers programmable logic controllers (PLCs), robot kinematics, sensor integration, and human-machine interfaces. Practical labs involve building and programming industrial robots for assembly line tasks.
This course delves into advanced network security concepts including zero-trust architecture, cloud security, and threat intelligence platforms. Students learn to conduct vulnerability assessments and deploy security solutions using tools like Nessus and Wireshark.
This course addresses challenges in modern transportation networks using AI and sensor technologies. Students study traffic flow modeling, autonomous vehicle systems, and smart city infrastructure design. The curriculum includes case studies of successful ITS implementations globally.
Focusing on cutting-edge renewable energy technologies, this course explores solar panel efficiency improvements, wind turbine optimization, and grid integration strategies. Students evaluate environmental impacts and economic feasibility of different energy sources.
This course introduces students to cloud platforms like AWS and Azure, along with big data processing frameworks such as Hadoop and Spark. Students learn to build scalable applications that process large datasets for business intelligence and decision-making.
The course covers modern processor design principles, memory hierarchies, and parallel computing architectures. Students examine performance optimization techniques and explore emerging technologies like quantum computing and neuromorphic chips.
Project-based learning is central to the engineering curriculum at Mahindra University Telangana. It ensures that students develop both technical competence and practical problem-solving skills through real-world challenges.
Mini-projects are introduced in the third semester and continue throughout the program. These projects allow students to apply theoretical knowledge to solve practical engineering problems. Each project has a defined scope, timeline, and deliverables, with regular progress reviews and feedback sessions.
The capstone project is the culmination of the undergraduate experience, where students work in teams on an industry-sponsored or faculty-led research initiative. The project spans two semesters and involves extensive documentation, presentation, and demonstration components. Students are paired with faculty mentors who guide them through the research process.
Students select their projects based on interests, faculty availability, and alignment with industry needs. The selection process includes a proposal submission, mentor assignment, and milestone planning. Projects may be chosen from available research areas or initiated by students themselves under faculty supervision.
