Comprehensive Course Listing Across All Semesters
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | ENG102 | Engineering Physics I | 3-1-0-4 | None |
| 1 | ENG103 | Engineering Chemistry I | 3-1-0-4 | None |
| 1 | ENG104 | Engineering Graphics & Design | 2-1-0-3 | None |
| 1 | ENG105 | Programming & Problem Solving | 2-1-0-3 | None |
| 1 | ENG106 | Engineering Mechanics | 3-1-0-4 | None |
| 1 | ENG107 | Workshop Practices | 0-0-2-2 | None |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Engineering Physics II | 3-1-0-4 | ENG102 |
| 2 | ENG203 | Engineering Chemistry II | 3-1-0-4 | ENG103 |
| 2 | ENG204 | Basic Electrical Engineering | 3-1-0-4 | None |
| 2 | ENG205 | Computer Programming & Data Structures | 3-1-0-4 | ENG105 |
| 2 | ENG206 | Engineering Materials | 3-1-0-4 | ENG103 |
| 2 | ENG207 | Lab: Basic Electrical & Electronics | 0-0-2-2 | None |
| 3 | ENG301 | Engineering Mathematics III | 3-1-0-4 | ENG201 |
| 3 | ENG302 | Engineering Physics III | 3-1-0-4 | ENG202 |
| 3 | ENG303 | Engineering Chemistry III | 3-1-0-4 | ENG203 |
| 3 | ENG304 | Engineering Thermodynamics | 3-1-0-4 | ENG206 |
| 3 | ENG305 | Digital Logic Design | 3-1-0-4 | ENG205 |
| 3 | ENG306 | Signals & Systems | 3-1-0-4 | ENG201 |
| 3 | ENG307 | Lab: Digital Logic & Microprocessor | 0-0-2-2 | ENG205 |
| 4 | ENG401 | Engineering Mathematics IV | 3-1-0-4 | ENG301 |
| 4 | ENG402 | Engineering Physics IV | 3-1-0-4 | ENG302 |
| 4 | ENG403 | Engineering Chemistry IV | 3-1-0-4 | ENG303 |
| 4 | ENG404 | Mechanics of Materials | 3-1-0-4 | ENG106 |
| 4 | ENG405 | Control Systems | 3-1-0-4 | ENG306 |
| 4 | ENG406 | Electromagnetic Fields & Waves | 3-1-0-4 | ENG202 |
| 4 | ENG407 | Lab: Control Systems & Electromagnetics | 0-0-2-2 | ENG305 |
| 5 | ENG501 | Advanced Engineering Mathematics | 3-1-0-4 | ENG401 |
| 5 | ENG502 | Advanced Physics | 3-1-0-4 | ENG402 |
| 5 | ENG503 | Advanced Chemistry | 3-1-0-4 | ENG403 |
| 5 | ENG504 | Fluid Mechanics & Hydraulic Machines | 3-1-0-4 | ENG304 |
| 5 | ENG505 | Machine Design | 3-1-0-4 | ENG404 |
| 5 | ENG506 | Power Plant Engineering | 3-1-0-4 | ENG304 |
| 5 | ENG507 | Lab: Fluid Mechanics & Machine Design | 0-0-2-2 | ENG404 |
| 6 | ENG601 | Mathematical Modeling & Optimization | 3-1-0-4 | ENG501 |
| 6 | ENG602 | Quantum Physics & Applications | 3-1-0-4 | ENG502 |
| 6 | ENG603 | Advanced Chemical Processes | 3-1-0-4 | ENG503 |
| 6 | ENG604 | Heat Transfer & Mass Transfer | 3-1-0-4 | ENG504 |
| 6 | ENG605 | Advanced Materials Science | 3-1-0-4 | ENG306 |
| 6 | ENG606 | Nuclear Engineering | 3-1-0-4 | ENG504 |
| 6 | ENG607 | Lab: Advanced Materials & Nuclear Engineering | 0-0-2-2 | ENG505 |
| 7 | ENG701 | Research Methodology & Ethics | 3-1-0-4 | None |
| 7 | ENG702 | Advanced Engineering Topics | 3-1-0-4 | ENG601 |
| 7 | ENG703 | Elective I: AI & Machine Learning | 3-1-0-4 | None |
| 7 | ENG704 | Elective II: Cybersecurity | 3-1-0-4 | None |
| 7 | ENG705 | Elective III: Renewable Energy | 3-1-0-4 | None |
| 7 | ENG706 | Elective IV: Biomedical Engineering | 3-1-0-4 | None |
| 7 | ENG707 | Lab: Advanced Elective Projects | 0-0-2-2 | None |
| 8 | ENG801 | Final Year Project & Thesis | 0-0-4-6 | ENG701 |
| 8 | ENG802 | Internship & Industry Exposure | 0-0-4-6 | None |
Detailed Course Descriptions for Departmental Electives
Elective I: Artificial Intelligence & Machine Learning
This course introduces students to the fundamental concepts of artificial intelligence, including search algorithms, knowledge representation, planning, and machine learning techniques. Students will learn how to implement AI systems using Python and TensorFlow frameworks. The course includes hands-on labs covering neural networks, deep learning architectures, and natural language processing applications.
Elective II: Cybersecurity
Cybersecurity is a critical discipline that protects information systems from unauthorized access, use, disclosure, disruption, modification, or destruction. This course covers network security protocols, cryptography, risk management, and ethical hacking techniques. Students will engage in simulated attacks and defensive strategies to understand real-world security challenges.
Elective III: Renewable Energy Systems
This elective explores sustainable energy technologies including solar panels, wind turbines, hydroelectric systems, and battery storage solutions. Students will study the design, installation, and optimization of renewable energy systems. The course integrates practical components with theoretical knowledge to prepare students for careers in clean energy industries.
Elective IV: Biomedical Engineering
Biomedical engineering combines principles from engineering and medicine to develop medical devices, diagnostic tools, and therapeutic methods. This course covers bioinstrumentation, biomechanics, biomaterials, and tissue engineering. Students will work on projects involving prosthetic design, medical imaging systems, and health monitoring technologies.
Elective V: Advanced Materials Science
This course delves into the structure-property relationships of materials, including metals, ceramics, polymers, and composites. Students will explore advanced characterization techniques, material processing methods, and applications in aerospace, automotive, and electronics industries. The curriculum includes laboratory sessions on material synthesis and testing.
Elective VI: Industrial Automation
Industrial automation focuses on improving manufacturing efficiency through robotics, programmable logic controllers (PLCs), and sensor integration. This course covers SCADA systems, process control, and automation design principles. Students will gain practical experience in programming industrial equipment and designing automated production lines.
Elective VII: Quantum Computing
Quantum computing represents a paradigm shift in computation using quantum mechanical phenomena such as superposition and entanglement. This course introduces students to quantum algorithms, quantum gates, and error correction methods. The curriculum includes simulations using quantum software tools and exploration of current research frontiers.
Elective VIII: Internet of Things (IoT)
The Internet of Things connects everyday objects to the internet, enabling data collection and remote control. This course covers IoT architecture, communication protocols, embedded systems, and cloud integration. Students will build IoT applications using microcontrollers and develop real-time monitoring systems.
Elective IX: Data Science & Big Data Analytics
Data science involves extracting insights from large datasets using statistical methods, machine learning, and visualization tools. This course teaches students to analyze complex data structures, build predictive models, and communicate findings effectively. Practical components include working with big data platforms like Hadoop and Spark.
Elective X: Sustainable Engineering Design
Sustainable engineering design emphasizes creating products and systems that minimize environmental impact while meeting performance requirements. Students will learn life cycle assessment methods, eco-design principles, and green manufacturing processes. Projects focus on developing sustainable solutions for urban planning, transportation, and energy sectors.
Project-Based Learning Philosophy
The department's approach to project-based learning is rooted in the belief that students acquire deeper understanding when they actively apply theoretical knowledge to solve real-world problems. From the first semester, students participate in mini-projects that reinforce classroom learning and build foundational skills.
Mini-projects are designed to be collaborative, multidisciplinary, and industry-relevant. They typically span 4-6 weeks and involve teams of 3-5 students working under faculty supervision. These projects help students develop critical thinking, teamwork, and communication abilities essential for professional success.
The final-year thesis/capstone project is a comprehensive endeavor that integrates all aspects of the student's education. Students select topics aligned with their interests or industry needs, often collaborating with research labs or corporate partners. The project involves literature review, experimental design, data analysis, and technical reporting.
Faculty mentors are assigned based on students' academic performance, interest areas, and research expertise. The selection process ensures that each student receives personalized guidance throughout their project journey. Regular progress reviews, milestone assessments, and peer feedback sessions maintain quality standards and foster continuous improvement.
Projects are evaluated using rubrics that assess technical competence, innovation, presentation skills, and team collaboration. Students must demonstrate proficiency in research methodology, problem-solving, and ethical considerations. The evaluation criteria emphasize not only the final outcome but also the learning process and personal growth achieved during the project.