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₹3,50,000
Placement
93.5%
Avg Package
₹5,80,000
Highest Package
₹8,50,000
Fees
₹3,50,000
Placement
93.5%
Avg Package
₹5,80,000
Highest Package
₹8,50,000
Seats
120
Students
1,200
Seats
120
Students
1,200
The Computer Engineering curriculum at Government Polytechnic Shaktifarm is meticulously designed to provide a comprehensive understanding of both hardware and software aspects of computing systems. The program spans eight semesters, with each semester consisting of core courses, departmental electives, science electives, and laboratory sessions.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| I | CE101 | Mathematics I | 3-1-0-4 | - |
| I | CE102 | Physics I | 3-1-0-4 | - |
| I | CE103 | Chemistry I | 3-1-0-4 | - |
| I | CE104 | English Communication Skills | 2-0-0-2 | - |
| I | CE105 | Introduction to Programming | 3-0-2-4 | - |
| I | CE106 | Basic Electrical Engineering | 3-1-0-4 | - |
| I | CE107 | Engineering Graphics | 2-1-0-3 | - |
| II | CE201 | Mathematics II | 3-1-0-4 | CE101 |
| II | CE202 | Physics II | 3-1-0-4 | CE102 |
| II | CE203 | Engineering Mechanics | 3-1-0-4 | - |
| II | CE204 | Data Structures & Algorithms | 3-1-0-4 | CE105 |
| II | CE205 | Digital Logic Design | 3-1-0-4 | - |
| II | CE206 | Computer Organization & Architecture | 3-1-0-4 | - |
| III | CE301 | Mathematics III | 3-1-0-4 | CE201 |
| III | CE302 | Signals & Systems | 3-1-0-4 | CE202 |
| III | CE303 | Analog Electronics | 3-1-0-4 | CE106 |
| III | CE304 | Operating Systems | 3-1-0-4 | CE204 |
| III | CE305 | Database Management Systems | 3-1-0-4 | CE204 |
| III | CE306 | Computer Networks | 3-1-0-4 | CE205 |
| IV | CE401 | Mathematics IV | 3-1-0-4 | CE301 |
| IV | CE402 | Control Systems | 3-1-0-4 | CE302 |
| IV | CE403 | Digital Signal Processing | 3-1-0-4 | CE302 |
| IV | CE404 | Software Engineering | 3-1-0-4 | CE304 |
| IV | CE405 | Microprocessor & Microcontroller | 3-1-0-4 | CE206 |
| IV | CE406 | Embedded Systems | 3-1-0-4 | CE405 |
| V | CE501 | Artificial Intelligence | 3-1-0-4 | CE404 |
| V | CE502 | Cybersecurity | 3-1-0-4 | CE306 |
| V | CE503 | Image Processing | 3-1-0-4 | CE403 |
| V | CE504 | Machine Learning | 3-1-0-4 | CE501 |
| V | CE505 | Advanced Computer Architecture | 3-1-0-4 | CE206 |
| V | CE506 | Internet of Things (IoT) | 3-1-0-4 | CE406 |
| VI | CE601 | Data Science & Analytics | 3-1-0-4 | CE501 |
| VI | CE602 | Cloud Computing | 3-1-0-4 | CE306 |
| VI | CE603 | Network Security | 3-1-0-4 | CE502 |
| VI | CE604 | Computer Vision | 3-1-0-4 | CE503 |
| VI | CE605 | Mobile Computing | 3-1-0-4 | CE306 |
| VI | CE606 | VLSI Design | 3-1-0-4 | CE303 |
| VII | CE701 | Research Methodology | 2-0-0-2 | - |
| VII | CE702 | Advanced Topics in AI | 3-1-0-4 | CE504 |
| VII | CE703 | Big Data Analytics | 3-1-0-4 | CE601 |
| VII | CE704 | Blockchain Technology | 3-1-0-4 | CE502 |
| VII | CE705 | Quantum Computing | 3-1-0-4 | CE501 |
| VIII | CE801 | Capstone Project | 3-0-0-6 | All previous semesters |
| VIII | CE802 | Industry Internship | 0-0-0-6 | - |
The department offers several advanced elective courses that delve deep into specialized areas of Computer Engineering. These courses are designed to provide students with cutting-edge knowledge and practical skills required in today's competitive job market.
This course explores the fundamental concepts of artificial intelligence, including search algorithms, knowledge representation, reasoning systems, and machine learning techniques. Students learn to build intelligent agents capable of perception, decision-making, and interaction with complex environments. The course emphasizes practical implementation using Python and TensorFlow frameworks.
Students study the principles and practices of cybersecurity, covering topics such as network security protocols, cryptographic algorithms, intrusion detection systems, and secure software development. The course includes hands-on labs using industry-standard tools like Wireshark, Metasploit, and Kali Linux.
This elective introduces students to the techniques used in processing digital images and extracting meaningful information from them. Topics include image enhancement, filtering, segmentation, feature extraction, and object recognition. Students gain proficiency in MATLAB and OpenCV libraries.
Focused on building predictive models using statistical methods and algorithms, this course covers supervised learning, unsupervised learning, neural networks, and reinforcement learning. Real-world applications are emphasized through projects involving data analysis and model deployment.
This course examines modern processor design principles, including pipelining, caching, memory hierarchy, and parallel computing architectures. Students study the impact of architectural decisions on performance and learn to simulate and analyze system behavior using tools like Gem5 and Simics.
Students explore the design and implementation of IoT systems, covering sensor networks, communication protocols, edge computing, and cloud integration. The course includes practical projects involving microcontrollers, wireless modules, and real-time data processing platforms.
This course teaches students how to extract insights from large datasets using statistical analysis, data mining, and visualization techniques. Emphasis is placed on Python-based tools like Pandas, NumPy, Scikit-learn, and Tableau for practical implementation.
Students learn about cloud infrastructure, virtualization technologies, distributed computing models, and service delivery mechanisms. The course covers platform-specific services from AWS, Azure, and Google Cloud, with hands-on labs involving deployment and management of scalable applications.
This elective focuses on protecting computer networks from unauthorized access, misuse, and data breaches. Topics include firewall configurations, secure network design, penetration testing, and compliance standards such as ISO 27001 and NIST.
Students study the theory and practice of image and video analysis, including object detection, recognition, tracking, and scene understanding. The course includes practical implementation using deep learning frameworks like TensorFlow and PyTorch.
Project-based learning is central to our Computer Engineering program. Students engage in both mini-projects during their second year and a comprehensive final-year capstone project that integrates all aspects of their education.
Mini-projects are assigned in the second year, typically lasting one semester. These projects focus on applying theoretical concepts to practical problems, encouraging innovation and teamwork. Projects often involve designing small-scale systems or solving real-world challenges related to embedded systems, network design, or data analysis.
The final-year capstone project is a multi-semester endeavor that allows students to demonstrate their mastery of Computer Engineering principles. Projects are selected based on student interests and industry needs, with faculty mentors guiding the research and development process. The project culminates in a public presentation and documentation of results.
Students select projects through a structured process involving proposal submissions, faculty evaluations, and mentor assignments. Projects are categorized into three types: research-oriented, application-focused, and entrepreneurial ventures. Students receive support from the Innovation Hub to ensure successful completion and potential commercialization.
