Curriculum Overview
The Bachelor of Technology program at Patel College of Science and Technology is structured over eight semesters, with a carefully designed curriculum that balances foundational theory, practical application, and specialized knowledge. The program includes core engineering subjects, departmental electives, science electives, and laboratory sessions aimed at developing technical competence and innovation capabilities.
Course Breakdown by Semester
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | PH101 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CH101 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | MA101 | Mathematics I | 4-0-0-4 | - |
| 1 | CS101 | Introduction to Programming | 2-0-2-3 | - |
| 1 | EC101 | Basic Electrical Engineering | 3-1-0-4 | - |
| 1 | GE101 | English for Engineers | 2-0-0-2 | - |
| 2 | PH201 | Physics II | 3-1-0-4 | PH101 |
| 2 | CH201 | Organic Chemistry | 3-1-0-4 | CH101 |
| 2 | MA201 | Mathematics II | 4-0-0-4 | MA101 |
| 2 | CS201 | Data Structures and Algorithms | 3-1-2-6 | CS101 |
| 2 | EC201 | Digital Electronics | 3-1-0-4 | EC101 |
| 2 | GE201 | Humanities and Social Sciences | 2-0-0-2 | - |
| 3 | MA301 | Probability and Statistics | 3-0-0-3 | MA201 |
| 3 | ME301 | Mechanics of Materials | 3-1-0-4 | - |
| 3 | CE301 | Strength of Materials | 3-1-0-4 | - |
| 3 | ECE301 | Analog Electronics | 3-1-0-4 | EC201 |
| 3 | CS301 | Database Management Systems | 3-1-2-6 | CS201 |
| 3 | GE301 | Professional Communication | 2-0-0-2 | - |
| 4 | MA401 | Linear Algebra | 3-0-0-3 | MA301 |
| 4 | ME401 | Thermodynamics | 3-1-0-4 | ME301 |
| 4 | CE401 | Concrete Technology | 3-1-0-4 | CE301 |
| 4 | ECE401 | Signals and Systems | 3-1-0-4 | ECE301 |
| 4 | CS401 | Operating Systems | 3-1-2-6 | CS301 |
| 4 | GE401 | Environmental Studies | 2-0-0-2 | - |
| 5 | CS501 | Machine Learning | 3-1-2-6 | CS401 |
| 5 | ME501 | Fluid Mechanics | 3-1-0-4 | ME401 |
| 5 | CE501 | Geotechnical Engineering | 3-1-0-4 | CE401 |
| 5 | ECE501 | Microprocessors and Microcontrollers | 3-1-2-6 | ECE401 |
| 5 | CS502 | Computer Networks | 3-1-2-6 | CS401 |
| 5 | GE501 | Ethics and Values in Engineering | 2-0-0-2 | - |
| 6 | CS601 | Advanced Algorithms | 3-1-2-6 | CS502 |
| 6 | ME601 | Heat Transfer | 3-1-0-4 | ME501 |
| 6 | CE601 | Transportation Engineering | 3-1-0-4 | CE501 |
| 6 | ECE601 | Digital Signal Processing | 3-1-2-6 | ECE501 |
| 6 | CS602 | Software Engineering | 3-1-2-6 | CS502 |
| 6 | GE601 | Project Management | 2-0-0-2 | - |
| 7 | CS701 | Deep Learning | 3-1-2-6 | CS501 |
| 7 | ME701 | Manufacturing Processes | 3-1-0-4 | ME601 |
| 7 | CE701 | Structural Analysis | 3-1-0-4 | CE601 |
| 7 | ECE701 | VLSI Design | 3-1-2-6 | ECE601 |
| 7 | CS702 | Cybersecurity | 3-1-2-6 | CS602 |
| 7 | GE701 | Entrepreneurship Development | 2-0-0-2 | - |
| 8 | CS801 | Capstone Project | 0-0-6-9 | CS702 |
| 8 | ME801 | Capstone Project | 0-0-6-9 | ME701 |
| 8 | CE801 | Capstone Project | 0-0-6-9 | CE701 |
| 8 | ECE801 | Capstone Project | 0-0-6-9 | ECE701 |
| 8 | GE801 | Internship | 0-0-0-3 | - |
Advanced Departmental Electives
The department offers a rich selection of advanced departmental electives designed to deepen student understanding in specialized areas. These courses are regularly updated to reflect current industry trends and research developments.
Machine Learning (CS501)
This course explores the fundamentals of machine learning, including supervised and unsupervised learning algorithms, neural networks, deep learning architectures, and reinforcement learning techniques. Students engage in hands-on projects using libraries like TensorFlow, PyTorch, and Scikit-learn.
Computer Networks (CS502)
Students learn about network protocols, TCP/IP stack, routing algorithms, wireless communication, and network security. The course includes practical lab sessions involving packet capture tools, router configuration, and network simulation software like NS3.
Software Engineering (CS602)
This course covers the entire software development lifecycle, from requirements analysis to deployment and maintenance. Students work on group projects using agile methodologies and industry-standard tools like JIRA, Git, Jenkins, and Docker.
Cybersecurity (CS702)
Designed to prepare students for careers in cybersecurity, this course covers network security, cryptography, ethical hacking, incident response, and compliance frameworks. Practical labs involve penetration testing using Kali Linux and vulnerability assessment tools.
Embedded Systems (ECE601)
This elective introduces students to embedded system design, microcontroller programming, real-time operating systems, and hardware-software co-design. Students build functional prototypes using Arduino, Raspberry Pi, and ARM-based platforms.
Digital Signal Processing (ECE601)
Students study signal representation, sampling theory, Fourier transforms, filter design, and applications in audio/video processing and communications. Labs involve MATLAB simulations and FPGA implementations.
VLSI Design (ECE701)
This course focuses on very large-scale integration, logic synthesis, circuit optimization, and layout design. Students gain experience with CAD tools like Cadence and Synopsys through practical assignments and a final project involving chip design.
Deep Learning (CS701)
Building upon earlier machine learning concepts, this course delves into convolutional neural networks, recurrent neural networks, transformers, and generative models. Students implement complex architectures using TensorFlow and PyTorch.
Manufacturing Processes (ME701)
This course provides insights into modern manufacturing techniques, including additive manufacturing, CNC machining, and automation technologies. Students participate in factory visits and hands-on experiments with industrial equipment.
Structural Analysis (CE701)
Students learn to analyze complex structures under various loads using matrix methods, finite element analysis, and structural dynamics. The course includes lab sessions involving structural testing and modeling software like SAP2000 and ETABS.
Project-Based Learning Philosophy
Our philosophy of project-based learning emphasizes experiential education that bridges the gap between theory and practice. Projects are assigned at different stages of the curriculum to reinforce learning outcomes and encourage innovation.
Mini-Projects (Year 2-3)
Mini-projects are introduced in the second year, allowing students to explore specific topics under faculty guidance. These projects typically last 3-4 months and involve literature review, experimentation, and documentation. Evaluation criteria include technical depth, presentation quality, teamwork, and innovation.
Final-Year Thesis/Capstone Project (Year 4)
The final-year project is a comprehensive endeavor that integrates knowledge from all previous semesters. Students select projects based on their interests and industry relevance, working closely with faculty mentors. Projects may be sponsored by companies or initiated by students themselves.
Project Selection Process
Students begin selecting projects in the third year, choosing from a list of faculty-recommended topics or proposing their own ideas. The selection process involves interviews with potential mentors, feasibility assessments, and alignment with program learning outcomes.
Evaluation Criteria
Projects are evaluated using rubrics that assess technical competence, creativity, documentation quality, oral presentations, peer reviews, and final deliverables. Faculty members from relevant departments form evaluation committees to ensure objective assessment.