Curriculum Overview
The Computer Engineering program at Government Polytechnic Jakhanidhar is meticulously designed to provide a comprehensive foundation in both theoretical and practical aspects of computing. The curriculum spans eight semesters, integrating core subjects with specialized electives that reflect current industry trends and academic excellence.
Semester-wise Course Structure
Each semester builds upon previous knowledge while introducing new concepts relevant to modern engineering practices. The following table outlines the complete course structure across all semesters:
| SEMESTER | COURSE CODE | COURSE TITLE | CREDIT STRUCTURE (L-T-P-C) | PREREQUISITES |
|---|---|---|---|---|
| I | CS101 | Introduction to Programming | 3-0-0-3 | - |
| I | CS102 | Mathematics I | 4-0-0-4 | - |
| I | CS103 | Physics for Computer Engineering | 3-0-0-3 | - |
| I | CS104 | Chemistry for Computer Engineering | 3-0-0-3 | - |
| I | CS105 | Engineering Drawing & Graphics | 2-0-0-2 | - |
| I | CS106 | Communication Skills | 2-0-0-2 | - |
| I | CS107 | Introduction to Digital Logic Design | 3-0-0-3 | - |
| I | CS108 | Python Programming Lab | 0-0-2-1 | - |
| I | CS109 | Digital Logic Design Lab | 0-0-2-1 | - |
| II | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| II | CS202 | Mathematics II | 4-0-0-4 | CS102 |
| II | CS203 | Electrical Circuits and Networks | 3-0-0-3 | CS103 |
| II | CS204 | Digital Electronics | 3-0-0-3 | CS107 |
| II | CS205 | Computer Organization and Architecture | 3-0-0-3 | CS107 |
| II | CS206 | Object Oriented Programming with C++ | 3-0-0-3 | CS101 |
| II | CS207 | Programming Lab (C++) | 0-0-2-1 | CS101 |
| II | CS208 | Digital Electronics Lab | 0-0-2-1 | CS107 |
| III | CS301 | Database Management Systems | 3-0-0-3 | CS201 |
| III | CS302 | Operating Systems | 3-0-0-3 | CS201 |
| III | CS303 | Computer Networks | 3-0-0-3 | CS204 |
| III | CS304 | Signals and Systems | 3-0-0-3 | CS202 |
| III | CS305 | Microprocessors and Microcontrollers | 3-0-0-3 | CS204 |
| III | CS306 | Software Engineering | 3-0-0-3 | CS201 |
| III | CS307 | Microprocessors Lab | 0-0-2-1 | CS204 |
| III | CS308 | Operating Systems Lab | 0-0-2-1 | CS202 |
| IV | CS401 | Artificial Intelligence and Machine Learning | 3-0-0-3 | CS301 |
| IV | CS402 | Cybersecurity Fundamentals | 3-0-0-3 | CS303 |
| IV | CS403 | Embedded Systems | 3-0-0-3 | CS305 |
| IV | CS404 | Data Structures and Algorithms Lab | 0-0-2-1 | CS201 |
| IV | CS405 | Computer Architecture Lab | 0-0-2-1 | CS205 |
| IV | CS406 | Networks Lab | 0-0-2-1 | CS303 |
| V | CS501 | Internet of Things (IoT) | 3-0-0-3 | CS403 |
| V | CS502 | Big Data Analytics | 3-0-0-3 | CS401 |
| V | CS503 | Software Testing and Quality Assurance | 3-0-0-3 | CS306 |
| V | CS504 | Advanced Computer Architecture | 3-0-0-3 | CS205 |
| V | CS505 | Cloud Computing | 3-0-0-3 | CS301 |
| V | CS506 | Robotics and Automation | 3-0-0-3 | CS403 |
| V | CS507 | Cloud Computing Lab | 0-0-2-1 | CS505 |
| V | CS508 | IoT Lab | 0-0-2-1 | CS501 |
| VI | CS601 | Computer Vision and Image Processing | 3-0-0-3 | CS401 |
| VI | CS602 | DevOps and CI/CD | 3-0-0-3 | CS505 |
| VI | CS603 | Research Methodology | 2-0-0-2 | - |
| VI | CS604 | Project Management | 2-0-0-2 | - |
| VI | CS605 | Internship | 0-0-0-4 | - |
| VI | CS606 | Capstone Project Preparation | 0-0-0-2 | - |
| VII | CS701 | Advanced Machine Learning | 3-0-0-3 | CS401 |
| VII | CS702 | Quantitative Finance | 3-0-0-3 | CS304 |
| VII | CS703 | Research Project | 0-0-0-6 | - |
| VIII | CS801 | Final Year Capstone Project | 0-0-0-8 | CS703 |
Advanced Departmental Electives
Departmental electives are offered to provide students with specialized knowledge in advanced areas of Computer Engineering. These courses aim to deepen understanding and prepare students for advanced research or industry roles.
- Advanced Machine Learning: This course delves into deep learning architectures, reinforcement learning algorithms, natural language processing techniques, and applications in healthcare and autonomous systems. Students learn to implement complex models using TensorFlow and PyTorch frameworks, preparing them for roles in AI research and development.
- Cybersecurity and Ethical Hacking: Students explore encryption methods, vulnerability assessment, penetration testing, and defensive strategies against modern cyber threats. This course includes hands-on labs where students practice identifying and mitigating security flaws in real-world systems.
- Internet of Things (IoT) Design and Implementation: The course covers sensor networks, communication protocols, edge computing, and smart city applications using IoT technologies. Projects involve designing and deploying IoT solutions for various domains including agriculture, healthcare, and urban infrastructure.
- Cloud Computing and DevOps: This module explores cloud infrastructure management, containerization tools like Docker and Kubernetes, CI/CD pipelines, and scalable application deployment strategies. Students gain experience in managing cloud environments and automating software delivery processes.
- Embedded Systems Design: Focuses on microcontroller programming, real-time operating systems, hardware-software co-design, and integration of sensors and actuators in embedded devices. Labs involve building functional embedded systems for specific applications such as robotics or home automation.
- Data Science and Big Data Analytics: Students gain proficiency in Python libraries for data analysis, statistical modeling, predictive analytics, and big data processing using Hadoop and Spark frameworks. Real-world datasets are used to train students in extracting insights and building data-driven solutions.
- Computer Vision and Image Processing: Covers image enhancement, object detection algorithms, facial recognition systems, and video analytics using OpenCV and TensorFlow. Practical projects include developing surveillance systems, medical imaging tools, and autonomous vehicle navigation components.
- Robotics and Automation: Involves robot kinematics, control systems, sensor integration, and AI-driven decision-making in autonomous machines. Students design and program robots for various tasks including exploration, manufacturing, and assistive technologies.
- Software Engineering Practices: Emphasizes agile methodologies, software architecture design, testing frameworks, and project lifecycle management using DevOps tools. Case studies from industry help students understand best practices in large-scale software development.
- Quantitative Finance: Explores mathematical modeling of financial markets, derivatives pricing, risk management, and algorithmic trading strategies. Students apply computational methods to solve problems in finance, preparing them for careers in quantitative research or fintech companies.
Project-Based Learning Philosophy
The department emphasizes project-based learning as a cornerstone of the educational experience. This approach encourages students to apply theoretical knowledge to real-world challenges, fostering innovation and critical thinking skills.
Mini-projects are introduced in the second year, focusing on individual or small group tasks that reinforce core concepts learned in class. These projects span across different domains such as web development, mobile applications, hardware design, and algorithm implementation. The goal is to build confidence and practical expertise early in the academic journey.
The final-year capstone project is a comprehensive endeavor where students work on an interdisciplinary topic related to their area of interest. Students select projects based on faculty research areas, industry needs, or personal passion. Each project must be supervised by a faculty mentor who guides the student through conceptualization, design, implementation, and documentation phases.
Evaluation criteria for these projects include innovation, technical execution, presentation quality, and documentation standards. The final project defense involves both internal and external panel evaluations, ensuring that students demonstrate mastery of their chosen domain while showcasing their ability to work collaboratively and independently.