Course Structure Overview
The Computer Engineering program at Govt Polytechnic Gaja spans 8 semesters, with a carefully balanced mix of core engineering subjects, departmental electives, science electives, and hands-on laboratory experiences. Each semester is structured to progressively build upon previous knowledge while introducing new concepts relevant to modern industry demands.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | CE-101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | CE-102 | Physics for Engineers | 3-1-0-4 | None |
| 1 | CE-103 | Basic Electrical & Electronics Engineering | 3-1-0-4 | None |
| 1 | CE-104 | Introduction to Programming | 2-1-0-3 | None |
| 1 | CE-105 | Communication Skills | 2-0-0-2 | None |
| 2 | CE-201 | Engineering Mathematics II | 3-1-0-4 | CE-101 |
| 2 | CE-202 | Chemistry for Engineers | 3-1-0-4 | None |
| 2 | CE-203 | Digital Logic Design | 3-1-0-4 | CE-103 |
| 2 | CE-204 | Data Structures and Algorithms | 3-1-0-4 | CE-104 |
| 2 | CE-205 | Computer Organization and Architecture | 3-1-0-4 | CE-203 |
| 3 | CE-301 | Probability and Statistics | 3-1-0-4 | CE-201 |
| 3 | CE-302 | Signals and Systems | 3-1-0-4 | CE-201 |
| 3 | CE-303 | Operating Systems | 3-1-0-4 | CE-205 |
| 3 | CE-304 | Database Management Systems | 3-1-0-4 | CE-204 |
| 3 | CE-305 | Microprocessors and Microcontrollers | 3-1-0-4 | CE-203 |
| 4 | CE-401 | Computer Networks | 3-1-0-4 | CE-305 |
| 4 | CE-402 | Software Engineering | 3-1-0-4 | CE-304 |
| 4 | CE-403 | Object-Oriented Programming with C++ | 2-1-0-3 | CE-204 |
| 4 | CE-404 | Embedded Systems Design | 3-1-0-4 | CE-305 |
| 4 | CE-405 | Human Computer Interaction | 2-1-0-3 | CE-304 |
| 5 | CE-501 | Machine Learning | 3-1-0-4 | CE-301 |
| 5 | CE-502 | Cybersecurity Fundamentals | 3-1-0-4 | CE-401 |
| 5 | CE-503 | Big Data Analytics | 3-1-0-4 | CE-304 |
| 5 | CE-504 | Advanced Computer Architecture | 3-1-0-4 | CE-205 |
| 5 | CE-505 | Internet of Things (IoT) | 3-1-0-4 | CE-305 |
| 6 | CE-601 | Cloud Computing | 3-1-0-4 | CE-402 |
| 6 | CE-602 | DevOps Practices | 3-1-0-4 | CE-402 |
| 6 | CE-603 | Mobile Application Development | 3-1-0-4 | CE-403 |
| 6 | CE-604 | Robotics and Automation | 3-1-0-4 | CE-505 |
| 6 | CE-605 | Research Methodology | 2-1-0-3 | CE-301 |
| 7 | CE-701 | Capstone Project - Part I | 2-0-0-2 | CE-605 |
| 7 | CE-702 | Advanced Topics in Computer Engineering | 3-1-0-4 | CE-501 |
| 7 | CE-703 | Internship Preparation | 2-0-0-2 | CE-401 |
| 8 | CE-801 | Capstone Project - Part II | 4-0-0-4 | CE-701 |
| 8 | CE-802 | Industry Project | 4-0-0-4 | CE-701 |
Advanced Departmental Electives
Departmental electives are designed to deepen students' understanding of specialized domains and prepare them for advanced roles in their chosen fields. Here are descriptions of key courses:
Machine Learning
This course covers supervised and unsupervised learning techniques, including decision trees, clustering algorithms, neural networks, and reinforcement learning. Students learn to apply these concepts using Python libraries like TensorFlow, Keras, and Scikit-Learn. The course emphasizes practical implementation through hands-on labs and real-world datasets.
Cybersecurity Fundamentals
This course introduces fundamental concepts of cybersecurity including network security protocols, cryptography, threat modeling, and vulnerability assessment. Students explore tools like Wireshark, Metasploit, and Nmap, gaining skills in ethical hacking and penetration testing. The curriculum includes case studies from recent security incidents.
Big Data Analytics
Students are introduced to big data technologies such as Hadoop, Spark, and NoSQL databases. The course covers data processing pipelines, visualization tools, and analytics frameworks. Practical sessions involve working with real-time datasets and building scalable data solutions using cloud platforms.
Advanced Computer Architecture
This advanced topic explores modern processor design principles including pipelining, cache memory, memory hierarchies, and parallel computing architectures. Students examine industry-standard processors like ARM Cortex-A series and Intel Xeon processors, comparing performance characteristics and design trade-offs.
Internet of Things (IoT)
This course delves into IoT architecture, sensor technologies, wireless communication protocols, and edge computing platforms. Students work on designing and implementing IoT applications using Raspberry Pi, Arduino, and microcontrollers. The curriculum includes discussions on privacy, security, and scalability issues in IoT deployments.
Cloud Computing
This course provides a comprehensive overview of cloud service models (IaaS, PaaS, SaaS) and deployment models (public, private, hybrid). Students learn to design and deploy applications using AWS, Azure, and Google Cloud platforms. Hands-on labs include containerization with Docker and orchestration with Kubernetes.
DevOps Practices
This course covers continuous integration/continuous delivery (CI/CD) pipelines, infrastructure as code (IaC), and automation tools like Jenkins, Ansible, GitLab CI, and GitHub Actions. Students learn to build automated workflows for software development and deployment.
Mobile Application Development
Students learn to develop native and cross-platform mobile applications using frameworks like React Native and Flutter. The course covers UI/UX design principles, app store publishing, and performance optimization techniques. Projects include building real-time communication apps and health tracking systems.
Robotics and Automation
This advanced course explores robotics kinematics, control systems, sensor integration, and autonomous navigation. Students work with robotic arms, drones, and mobile robots, developing algorithms for path planning, object detection, and human-robot interaction.
Project-Based Learning Framework
The department's philosophy on project-based learning is centered around real-world relevance and industry alignment. Projects are structured to encourage innovation, critical thinking, and collaboration among students. The program emphasizes:
- Mini-Projects (Semester 4): Students undertake small-scale projects under faculty supervision, focusing on applying concepts learned in core subjects.
- Final-Year Thesis/Capstone Project (Semesters 7-8): These projects are typically collaborative efforts involving multiple students and are often sponsored by industry partners or research institutions.
Project selection involves a proposal submission process where students propose ideas aligned with current industry trends. Faculty mentors guide students through each phase of the project lifecycle, ensuring technical feasibility and academic rigor.
Evaluation criteria include:
- Technical Implementation (40%)
- Research/Design Quality (25%)
- Documentation & Presentation (20%)
- Team Collaboration & Time Management (15%)
Students are encouraged to publish their work in conferences or journals, and many projects have led to patents or startup ventures.