Comprehensive Course Catalog
This detailed table outlines all the core, departmental elective, science elective, and laboratory courses offered over eight semesters for the Bachelor of Technology in Engineering program at Major S D Singh University Farrukhabad.
| Year | Semester | Course Code | Course Title | Credit (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|---|
| 1st Year | 1 | ENGS101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | ENGS102 | Engineering Physics I | 3-1-0-4 | - | |
| 1 | ENGS103 | Engineering Chemistry I | 3-1-0-4 | - | |
| 1 | ENGS104 | Basic Electrical Engineering | 3-1-0-4 | - | |
| 2nd Year | 3 | ENGS201 | Engineering Mathematics II | 3-1-0-4 | ENGS101 |
| 3 | ENGS202 | Engineering Physics II | 3-1-0-4 | ENGS102 | |
| 3 | ENGS203 | Engineering Chemistry II | 3-1-0-4 | ENGS103 | |
| 3 | ENGS204 | Electrical Circuits and Networks | 3-1-0-4 | ENGS104 | |
| 3rd Year | 5 | ENGS301 | Engineering Mathematics III | 3-1-0-4 | ENGS201 |
| 5 | ENGS302 | Thermodynamics | 3-1-0-4 | ENGS202 | |
| 5 | ENGS303 | Fluid Mechanics | 3-1-0-4 | ENGS202 | |
| 5 | ENGS304 | Machine Design | 3-1-0-4 | ENGS204 | |
| 4th Year | 7 | ENGS401 | Advanced Mathematics | 3-1-0-4 | ENGS301 |
| 7 | ENGS402 | Control Systems | 3-1-0-4 | ENGS302 | |
| 7 | ENGS403 | Signals and Systems | 3-1-0-4 | ENGS301 | |
| 7 | ENGS404 | Project Management | 3-1-0-4 | - | |
| Laboratory Courses | Lab 1 | ENGL101 | Basic Electrical Lab | 0-0-3-1 | ENGS104 |
| Lab 1 | ENGL102 | Physics Lab | 0-0-3-1 | ENGS102 | |
| Elective Courses | Elective 1 | ENGE101 | Introduction to AI | 3-1-0-4 | ENGS201 |
| Elective 1 | ENGE102 | Database Systems | 3-1-0-4 | ENGS201 |
Advanced Departmental Electives
These advanced elective courses provide students with specialized knowledge and practical skills in niche areas of engineering. Each course is designed to build upon foundational concepts while introducing cutting-edge developments and methodologies.
- Introduction to Artificial Intelligence: This course covers fundamental AI techniques including neural networks, machine learning algorithms, and natural language processing. Students learn to implement AI solutions using Python frameworks like TensorFlow and PyTorch. The course emphasizes real-world applications such as image recognition, sentiment analysis, and recommendation systems.
- Database Systems: Focused on relational databases, SQL queries, data modeling, and database design principles. Students gain hands-on experience with Oracle and MySQL databases, learning to optimize performance and ensure data integrity through normalization and indexing strategies.
- Advanced Control Systems: Delves into modern control theory, state-space representation, stability analysis, and digital control systems. The course includes practical simulations using MATLAB/Simulink to model and analyze complex dynamic systems.
- Quantum Computing Fundamentals: Introduces quantum mechanics principles relevant to computing, qubit states, superposition, entanglement, and quantum algorithms. Students explore IBM Quantum Experience and simulate quantum circuits using Qiskit.
- Renewable Energy Technologies: Examines solar, wind, hydroelectric, and geothermal energy systems. The course covers energy conversion processes, grid integration challenges, and environmental impact assessments for renewable sources.
- Biomedical Instrumentation: Focuses on designing medical devices and sensors for diagnostics and treatment monitoring. Topics include bioelectrical signals, imaging modalities, and regulatory compliance in medical device development.
- Robotics and Automation: Covers robot kinematics, dynamics, sensor integration, and control systems. Students build autonomous robots using Arduino and Raspberry Pi platforms, learning to program motion control and navigation algorithms.
- Materials Science and Engineering: Explores atomic structure, crystallography, phase diagrams, and material properties. The course includes laboratory experiments on metal alloys, ceramics, polymers, and composites, focusing on their applications in engineering design.
- Advanced Thermodynamics: Builds upon basic thermodynamic principles to study irreversible processes, entropy changes, and heat transfer mechanisms. Students analyze power cycles, refrigeration systems, and combustion engines using computational tools.
- Data Structures and Algorithms: Provides comprehensive coverage of data structures like trees, graphs, hash tables, and sorting algorithms. The course emphasizes algorithmic complexity analysis and problem-solving techniques for software development and optimization.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a means to bridge the gap between theoretical knowledge and practical application. Students engage in both mini-projects during their second year and final-year capstone projects that span multiple semesters.
Mini-projects are assigned at the beginning of each semester, focusing on specific engineering challenges related to course content. These projects encourage collaboration among students and provide opportunities for faculty mentorship. Projects typically involve designing, prototyping, testing, and presenting solutions within a specified timeframe.
The final-year thesis/capstone project represents the culmination of student learning experiences. Students select projects aligned with their interests and career goals, often collaborating with industry partners or faculty research groups. The project involves extensive literature review, hypothesis formulation, experimental design, data collection, analysis, and documentation.
Faculty mentors guide students throughout their project journey, providing technical expertise, feedback on progress, and professional development support. The evaluation criteria include project proposal quality, execution efficiency, innovation level, presentation skills, and final deliverables. This approach ensures that students develop critical thinking, communication, and teamwork abilities essential for success in the engineering profession.