Course Structure Overview
The Diploma in Engineering program at GOVT POLYTECHNIC COLLEGE DAMOH is structured into 6 semesters, spanning 3 academic years. Each semester consists of core subjects, departmental electives, science electives, and practical laboratory sessions designed to provide a holistic understanding of engineering principles and applications.
| Year | Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|---|
| I | I | ENG101 | English for Engineers | 3-0-2-4 | - |
| I | MAT101 | Applied Mathematics I | 4-0-2-6 | - | |
| I | PHY101 | Physics for Engineers | 3-0-2-5 | - | |
| II | II | MAT201 | Applied Mathematics II | 4-0-2-6 | MAT101 |
| II | CHE101 | Chemistry for Engineers | 3-0-2-5 | - | |
| II | BEE101 | Basic Electrical Engineering | 3-0-2-5 | - | |
| II | INT101 | Introduction to Programming | 3-0-2-5 | - | |
| III | III | MAT301 | Applied Mathematics III | 4-0-2-6 | MAT201 |
| III | ECE101 | Electrical Circuits and Networks | 3-0-2-5 | BEE101 | |
| III | MEC101 | Mechanics of Solids | 3-0-2-5 | - | |
| III | CSE101 | Data Structures and Algorithms | 3-0-2-5 | INT101 | |
| IV | IV | MAT401 | Applied Mathematics IV | 4-0-2-6 | MAT301 |
| IV | ECE201 | Analog Electronics | 3-0-2-5 | ECE101 | |
| IV | CSE201 | Database Management Systems | 3-0-2-5 | CSE101 | |
| IV | MEC201 | Thermodynamics | 3-0-2-5 | MEC101 | |
| V | V | ECE301 | Digital Electronics | 3-0-2-5 | ECE201 |
| V | CSE301 | Computer Networks | 3-0-2-5 | CSE201 | |
| V | MEC301 | Strength of Materials | 3-0-2-5 | MEC201 | |
| V | CIV101 | Structural Analysis | 3-0-2-5 | - | |
| VI | VI | ECE401 | Control Systems | 3-0-2-5 | ECE301 |
| VI | CSE401 | Software Engineering | 3-0-2-5 | CSE301 | |
| VI | MEC401 | Mechanical Design | 3-0-2-5 | MEC301 | |
| VI | CIV201 | Foundation Engineering | 3-0-2-5 | CIV101 |
Advanced Departmental Elective Courses
The program offers advanced elective courses in each specialization track to provide students with in-depth knowledge and practical skills. These courses are designed to align with current industry trends and research developments.
Machine Learning Algorithms
This course introduces students to fundamental machine learning techniques including supervised and unsupervised learning, neural networks, decision trees, and clustering algorithms. Students will gain hands-on experience through assignments using Python libraries such as Scikit-learn and TensorFlow.
Natural Language Processing
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. This course covers text preprocessing, sentiment analysis, named entity recognition, and language modeling techniques using tools like NLTK and spaCy.
Deep Learning
This advanced course explores deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students will implement models for image classification, sequence prediction, and generative tasks using frameworks like PyTorch and Keras.
Data Mining
Data mining involves discovering patterns in large datasets through algorithms and machine learning techniques. This course covers association rule mining, clustering, classification, and anomaly detection methods. Students will apply these techniques to real-world data sets using tools like WEKA and RapidMiner.
Network Security
This course provides an overview of cybersecurity principles and practices. Topics include network protocols, encryption, firewalls, intrusion detection systems, and secure programming practices. Students will engage in practical exercises using tools like Wireshark and Metasploit to understand security vulnerabilities and mitigation strategies.
Cryptography
Cryptography is the practice of securing communication through mathematical techniques. This course covers symmetric and asymmetric encryption algorithms, hash functions, digital signatures, and key management. Students will implement cryptographic protocols using OpenSSL and other libraries.
Control Systems
This course focuses on modeling, analysis, and design of control systems. It covers classical control theory, state-space representation, transfer functions, and stability analysis. Students will use MATLAB/Simulink to simulate and analyze dynamic systems.
Signal and Systems
This course introduces students to the mathematical tools used in signal processing. Topics include time-domain and frequency-domain analysis, convolution, Fourier transforms, and Z-transforms. Practical applications in audio and image processing will be demonstrated using MATLAB.
Power Electronics
Power electronics deals with the conversion and control of electric power. This course covers rectifiers, inverters, DC-DC converters, and power factor correction techniques. Students will design circuits using simulation software like LTspice and build physical prototypes.
Computer Networks
This course provides a comprehensive understanding of computer networking concepts including OSI model, TCP/IP protocols, routing, switching, and network security. Students will perform hands-on experiments with routers, switches, and network simulation tools like Packet Tracer.
Project-Based Learning Philosophy
The program emphasizes project-based learning as a core component of education. Projects are designed to simulate real-world engineering challenges and encourage students to apply theoretical knowledge in practical contexts.
Mini-projects begin in the second semester and continue throughout the program. These projects typically last for 4-6 weeks and require students to work in teams on specific problems or technologies. Each project is supervised by a faculty member who guides students through planning, execution, and documentation phases.
The final-year capstone project is a significant undertaking that spans 8-10 weeks. Students select projects from industry partners or research areas aligned with their interests. The project involves literature review, design, implementation, testing, and presentation of results. A formal evaluation is conducted by a panel of faculty members and external experts.
Faculty mentors are selected based on expertise and availability. Students may propose their own project ideas or choose from suggested topics provided by the department. The selection process ensures that projects align with academic goals and industry relevance.