Course Structure and Academic Progression
The Engineering program at Geeta University Panipat is structured over eight semesters, with each semester carrying a defined set of core courses, departmental electives, science electives, and laboratory sessions. The curriculum is meticulously designed to ensure that students progress systematically from foundational knowledge to advanced specialization.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | ENG102 | Physics for Engineers | 3-1-0-4 | - |
| 1 | ENG103 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | ENG104 | Computer Programming Essentials | 2-1-0-3 | - |
| 1 | ENG105 | Engineering Graphics and Design | 2-1-0-3 | - |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Electrical Circuits and Networks | 3-1-0-4 | ENG102 |
| 2 | ENG203 | Thermodynamics | 3-1-0-4 | ENG102 |
| 2 | ENG204 | Fluid Mechanics | 3-1-0-4 | ENG102 |
| 2 | ENG205 | Materials Science | 3-1-0-4 | ENG103 |
| 3 | ENG301 | Data Structures and Algorithms | 3-1-0-4 | ENG104 |
| 3 | ENG302 | Digital Logic Design | 3-1-0-4 | ENG102 |
| 3 | ENG303 | Signals and Systems | 3-1-0-4 | ENG201 |
| 3 | ENG304 | Control Systems | 3-1-0-4 | ENG201 |
| 3 | ENG305 | Structural Analysis | 3-1-0-4 | ENG203 |
| 4 | ENG401 | Database Management Systems | 3-1-0-4 | ENG301 |
| 4 | ENG402 | Software Engineering | 3-1-0-4 | ENG301 |
| 4 | ENG403 | Machine Learning | 3-1-0-4 | ENG301 |
| 4 | ENG404 | Power Systems | 3-1-0-4 | ENG202 |
| 4 | ENG405 | Heat Transfer | 3-1-0-4 | ENG203 |
| 5 | ENG501 | Advanced Data Structures | 3-1-0-4 | ENG301 |
| 5 | ENG502 | Embedded Systems | 3-1-0-4 | ENG302 |
| 5 | ENG503 | Computer Vision | 3-1-0-4 | ENG301 |
| 5 | ENG504 | Renewable Energy Systems | 3-1-0-4 | ENG203 |
| 5 | ENG505 | Geotechnical Engineering | 3-1-0-4 | ENG205 |
| 6 | ENG601 | Advanced Algorithms | 3-1-0-4 | ENG501 |
| 6 | ENG602 | Neural Networks | 3-1-0-4 | ENG403 |
| 6 | ENG603 | Distributed Systems | 3-1-0-4 | ENG402 |
| 6 | ENG604 | Industrial Automation | 3-1-0-4 | ENG404 |
| 6 | ENG605 | Structural Dynamics | 3-1-0-4 | ENG505 |
| 7 | ENG701 | Capstone Project I | 2-0-0-2 | - |
| 7 | ENG702 | Research Methodology | 3-1-0-4 | - |
| 7 | ENG703 | Advanced Signal Processing | 3-1-0-4 | ENG303 |
| 7 | ENG704 | Smart Materials | 3-1-0-4 | ENG205 |
| 7 | ENG705 | Environmental Impact Assessment | 3-1-0-4 | ENG505 |
| 8 | ENG801 | Capstone Project II | 2-0-0-2 | - |
| 8 | ENG802 | Final Thesis | 3-0-0-3 | - |
| 8 | ENG803 | Professional Practice | 1-0-0-1 | - |
| 8 | ENG804 | Entrepreneurship in Engineering | 2-0-0-2 | - |
| 8 | ENG805 | Industry Internship | 1-0-0-1 | - |
The curriculum includes a mix of core engineering subjects, departmental electives, science electives, and laboratory sessions. Core courses provide foundational knowledge essential for any engineer, while departmental electives allow students to specialize in areas of interest such as AI, cybersecurity, renewable energy, or manufacturing processes.
Advanced Departmental Elective Courses
Several advanced elective courses are offered to deepen student understanding and enhance their expertise. One such course is Machine Learning, which introduces students to supervised and unsupervised learning techniques using Python and TensorFlow. The course covers neural networks, decision trees, clustering algorithms, and reinforcement learning, with hands-on projects that simulate real-world applications.
The Computer Vision elective delves into image processing techniques, object detection, feature extraction, and deep learning models for visual recognition. Students work on datasets like CIFAR-10 and ImageNet to train convolutional neural networks and develop applications such as facial recognition and autonomous vehicle systems.
In Embedded Systems, students learn to design and program microcontrollers using C/C++ and ARM architectures. The course includes practical lab sessions where students build IoT devices, control robotics systems, and implement sensor integration for smart city solutions.
The Advanced Data Structures course explores complex data structures like graphs, trees, and hash tables, with a focus on algorithmic complexity analysis. Students apply these concepts to solve optimization problems in competitive programming competitions and real-world software development tasks.
Neural Networks provides an in-depth look at artificial neural networks, including feedforward, recurrent, and convolutional architectures. Students study backpropagation, gradient descent, and regularization techniques through practical assignments involving TensorFlow and PyTorch frameworks.
Distributed Systems teaches students how to design scalable applications that run across multiple nodes in a network. Topics include consensus algorithms, distributed databases, cloud computing platforms, and security protocols used in large-scale systems.
Industrial Automation combines theoretical knowledge with practical implementation in industrial settings. Students learn about programmable logic controllers (PLCs), SCADA systems, robotic arms, and process control methodologies, preparing them for careers in manufacturing automation.
The Smart Materials elective focuses on materials with adaptive properties such as shape memory alloys, piezoelectric ceramics, and self-healing polymers. Students explore applications in aerospace, biomedical devices, and smart infrastructure, conducting experiments to characterize material behavior under varying conditions.
In Environmental Impact Assessment, students assess the ecological implications of engineering projects. They study environmental regulations, mitigation strategies, and sustainability principles through case studies of real-world developments like dams, highways, and industrial plants.
The Advanced Signal Processing course covers digital signal processing techniques for audio, video, and biomedical signals. Students implement filtering, spectral analysis, and compression algorithms using MATLAB and Python libraries, applying them to audio enhancement and medical diagnostics.
Project-Based Learning Philosophy
Geeta University Panipat emphasizes project-based learning throughout the engineering program. Students engage in both mini-projects and a final-year thesis, which are integral components of their academic journey. Mini-projects are undertaken during the second and third years, where students work on small-scale problems that mirror real-world challenges.
These projects are supervised by faculty members who guide students through research methodologies, design thinking, and technical documentation. Evaluation criteria include project proposal quality, implementation progress, final presentation, and peer feedback. The projects help students develop practical skills such as teamwork, communication, and problem-solving under time constraints.
The final-year thesis is a comprehensive research endeavor that allows students to explore an area of personal interest or industry relevance. Students select their topics in consultation with faculty mentors who provide guidance on literature review, experimental design, data collection, and analysis. The thesis culminates in a formal presentation and defense before an expert panel.
Project selection is based on student preferences, faculty availability, and alignment with ongoing research initiatives. Students can propose their own ideas or choose from a list of suggested topics provided by the department. Regular milestones and progress reviews ensure that students stay on track toward completion.