Comprehensive Course Structure
The engineering curriculum at D A V University Jalandhar is meticulously structured to provide a balanced mix of theoretical foundations, practical skills, and real-world exposure. The program spans 8 semesters over four years, with each semester designed to build upon previous knowledge while introducing new concepts.
| Year | Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|---|
| First Year | I | ENG101 | Engineering Graphics | 3-0-0-3 | - |
| ENG102 | Basic Electrical Circuits | 3-0-0-3 | - | ||
| First Year | II | MAT101 | Mathematics I | 4-0-0-4 | - |
| PHY101 | Physics I | 3-0-0-3 | - | ||
| Second Year | III | MAT201 | Mathematics II | 4-0-0-4 | MAT101 |
| CHE101 | Chemistry I | 3-0-0-3 | - | ||
| Second Year | IV | MAT202 | Mathematics III | 4-0-0-4 | MAT201 |
| PHY102 | Physics II | 3-0-0-3 | PHY101 | ||
| Third Year | V | MAT301 | Mathematics IV | 4-0-0-4 | MAT202 |
| ECE201 | Signals & Systems | 3-0-0-3 | - | ||
| Third Year | VI | MAT302 | Mathematics V | 4-0-0-4 | MAT301 |
| MEC301 | Mechanics of Solids | 3-0-0-3 | - | ||
| Fourth Year | VII | ECE301 | Control Systems | 3-0-0-3 | ECE201 |
| MAT401 | Probability & Statistics | 3-0-0-3 | MAT302 | ||
| Fourth Year | VIII | ECE401 | Capstone Project | 0-0-6-6 | All previous semesters |
| ECE402 | Thesis Research | 0-0-6-6 | ECE401 |
Advanced Departmental Electives
Advanced departmental electives form a crucial part of the curriculum, allowing students to tailor their learning experience based on interests and career aspirations.
- Neural Networks: This course covers the fundamentals of artificial neural networks, including perceptrons, backpropagation, and deep learning architectures. Students learn to implement neural networks using Python and TensorFlow.
- Cryptography and Network Security: The course explores classical and modern cryptographic techniques, including RSA, AES, and elliptic curve cryptography. It includes hands-on labs on secure network protocols and penetration testing.
- Renewable Energy Systems: This subject delves into solar, wind, and hydroelectric power generation technologies. Students study energy conversion efficiency, grid integration, and policy frameworks for sustainable development.
- Robotics and Automation: Designed to introduce students to robotics fundamentals, this course covers kinematics, control systems, sensor integration, and autonomous navigation using ROS (Robot Operating System).
- Sustainable Design Principles: This elective focuses on green building design, life cycle assessment, and eco-friendly materials. Students engage in designing sustainable infrastructure projects.
- Advanced Control Systems: Building upon basic control theory, this course covers state-space methods, digital control systems, and advanced modeling techniques used in aerospace and industrial applications.
- Data Mining & Big Data Analytics: The course teaches students to extract patterns from large datasets using machine learning algorithms and statistical tools. Practical assignments involve real-world data sets from social media, healthcare, and finance.
- Embedded Systems Design: This course introduces microcontroller programming, embedded C, real-time operating systems, and hardware-software co-design. Students build working prototypes of smart devices.
- Quantum Computing Fundamentals: An introductory look at quantum algorithms and qubit manipulation, this elective prepares students for advanced research in quantum technologies and their applications in cryptography and optimization.
- Advanced Materials Science: This course examines nanomaterials, composites, smart materials, and their applications in engineering. Laboratory sessions involve synthesis and characterization of novel materials.
Project-Based Learning Philosophy
The department strongly believes in experiential learning through project-based education. From the second year onward, students are required to complete mini-projects under faculty guidance, culminating in a final-year thesis or capstone project.
Mini-projects are typically completed in groups of 3–5 students and span two semesters. Each group selects a topic relevant to their specialization or industry needs, working closely with a faculty mentor. Projects are evaluated based on technical execution, innovation, presentation quality, and peer feedback.
The capstone project, undertaken in the final year, allows students to work on an interdisciplinary challenge that requires integrating knowledge from multiple domains. Projects often involve collaboration with industry partners, resulting in real-world solutions with potential commercial viability.
Faculty mentors are assigned based on student interests and availability. Students can propose topics or suggest mentors from the department's roster of experts. The selection process ensures a balance between academic rigor and practical relevance.