Curriculum
The engineering curriculum at G H Raisoni International Skill Tech University Pune is meticulously designed to ensure comprehensive coverage of theoretical concepts, practical skills, and industry relevance. The program spans eight semesters, with each semester comprising a mix of core courses, departmental electives, science electives, and laboratory sessions.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| I | PHY101 | Physics for Engineers | 3-1-0-4 | - |
| I | CHE101 | Chemistry for Engineers | 3-1-0-4 | - |
| I | CS101 | Introduction to Programming | 2-0-2-3 | - |
| I | ENG102 | Engineering Drawing & Graphics | 1-0-2-2 | - |
| I | PHY102 | Basic Electrical Engineering | 3-1-0-4 | - |
| I | ENG103 | Technical Communication Skills | 2-0-0-2 | - |
| I | ME101 | Engineering Mechanics | 3-1-0-4 | - |
| II | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| II | ME201 | Mechanics of Materials | 3-1-0-4 | ENG101 |
| II | EE201 | Electrical Circuits & Networks | 3-1-0-4 | PHY102 |
| II | CS201 | Data Structures and Algorithms | 3-1-0-4 | CS101 |
| II | ME202 | Thermodynamics | 3-1-0-4 | ENG101 |
| II | CHE201 | Material Science | 3-1-0-4 | CHE101 |
| II | ENG202 | Engineering Ethics and Values | 2-0-0-2 | - |
| III | ME301 | Fluid Mechanics | 3-1-0-4 | ME202 |
| III | CS301 | Database Management Systems | 3-1-0-4 | CS201 |
| III | EE301 | Electromagnetic Fields and Waves | 3-1-0-4 | EE201 |
| III | ME302 | Mechanical Vibrations | 3-1-0-4 | ME201 |
| III | CHE301 | Chemical Engineering Principles | 3-1-0-4 | CHE201 |
| III | ENG301 | Project Management | 2-0-0-2 | - |
| IV | CS401 | Operating Systems | 3-1-0-4 | CS301 |
| IV | EE401 | Power Systems Engineering | 3-1-0-4 | EE301 |
| IV | ME401 | Heat Transfer | 3-1-0-4 | ME301 |
| IV | CS402 | Computer Networks | 3-1-0-4 | CS401 |
| IV | CHE401 | Process Control | 3-1-0-4 | CHE301 |
| IV | ENG401 | Leadership and Teamwork | 2-0-0-2 | - |
| V | CS501 | Machine Learning Fundamentals | 3-1-0-4 | CS402 |
| V | EE501 | Renewable Energy Technologies | 3-1-0-4 | EE401 |
| V | ME501 | Design of Machine Elements | 3-1-0-4 | ME401 |
| V | CS502 | Web Development and Cloud Computing | 3-1-0-4 | CS402 |
| V | CHE501 | Environmental Engineering | 3-1-0-4 | CHE401 |
| V | ENG501 | Innovation and Entrepreneurship | 2-0-0-2 | - |
| VI | CS601 | Advanced Machine Learning | 3-1-0-4 | CS501 |
| VI | EE601 | Power Electronics and Drives | 3-1-0-4 | EE501 |
| VI | ME601 | Finite Element Methods | 3-1-0-4 | ME501 |
| VI | CS602 | Big Data Analytics | 3-1-0-4 | CS502 |
| VI | CHE601 | Biochemical Engineering | 3-1-0-4 | CHE501 |
| VI | ENG601 | Project Planning and Execution | 2-0-0-2 | - |
| VII | CS701 | Artificial Intelligence and Neural Networks | 3-1-0-4 | CS601 |
| VII | EE701 | Smart Grid Technologies | 3-1-0-4 | EE601 |
| VII | ME701 | Advanced Manufacturing Processes | 3-1-0-4 | ME601 |
| VII | CS702 | Cybersecurity and Cryptography | 3-1-0-4 | CS602 |
| VII | CHE701 | Pharmaceutical Engineering | 3-1-0-4 | CHE601 |
| VII | ENG701 | Industrial Internship | 2-0-0-2 | - |
| VIII | CS801 | Capstone Project in AI/ML | 3-1-0-4 | CS701 |
| VIII | EE801 | Final Year Project in Renewable Energy | 3-1-0-4 | EE701 |
| VIII | ME801 | Capstone Project in Mechanical Engineering | 3-1-0-4 | ME701 |
| VIII | CS802 | Final Year Project in Cybersecurity | 3-1-0-4 | CS702 |
| VIII | CHE801 | Capstone Project in Biotechnology | 3-1-0-4 | CHE701 |
| VIII | ENG801 | Final Year Thesis Presentation | 2-0-0-2 | - |
The department's philosophy on project-based learning is centered around experiential education that encourages students to apply theoretical knowledge in solving real-world problems. From the first semester, students are introduced to mini-projects that allow them to work in teams and present their findings.
Mini-projects are assigned at the end of each semester and typically last for 4-6 weeks. These projects are designed to help students understand how engineering principles can be applied to address practical challenges. Students must submit a project report, give a presentation, and demonstrate a working prototype or solution.
The final-year thesis/capstone project is a more extensive endeavor that spans two semesters. Students select their topic in consultation with faculty members based on current industry trends or research opportunities. The project involves designing, implementing, testing, and documenting a significant engineering solution.
Students are encouraged to choose projects related to emerging technologies or societal issues. For instance, recent capstone projects have included developing an AI-powered chatbot for mental health support, designing a solar-powered irrigation system for rural farmers, creating a smart traffic management system using IoT sensors, and building an autonomous drone for agricultural monitoring.
Faculty mentors play a crucial role in guiding students throughout their project journey. They provide feedback on research methodologies, technical aspects, and presentation skills. Additionally, industry professionals are invited to review student projects and offer insights into market viability and commercial potential.
Advanced Departmental Electives
- Deep Learning Architectures: This course explores advanced neural network models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformers. Students learn how to design and train complex deep learning architectures for image recognition, natural language processing, and time-series forecasting.
- Reinforcement Learning: Focused on decision-making processes in uncertain environments, this course introduces students to Markov decision processes, Q-learning, policy gradients, and actor-critic methods. Practical applications include robotics control, game playing, and autonomous navigation systems.
- Computer Vision Techniques: This elective covers fundamental concepts in computer vision such as image segmentation, object detection, facial recognition, and 3D reconstruction. Students gain hands-on experience with libraries like OpenCV, TensorFlow, and PyTorch while working on real-world computer vision tasks.
- NLP Applications: Students explore natural language processing techniques including sentiment analysis, named entity recognition, machine translation, and text summarization. The course includes practical sessions using NLP frameworks like spaCy, NLTK, and Hugging Face Transformers.
- Network Security Protocols: This course delves into the principles of network security, including firewalls, intrusion detection systems (IDS), secure socket layer (SSL) protocols, and virtual private networks (VPNs). Students learn how to design secure communication channels and defend against cyber threats.
- Cryptography and Network Defense: Covering both classical and modern cryptographic techniques, this course includes symmetric and asymmetric encryption algorithms, hash functions, digital signatures, and key management strategies. The focus is on securing data transmission in networked environments.
- Embedded Systems Design: This elective teaches students how to design and implement embedded systems using microcontrollers, real-time operating systems (RTOS), and hardware-software co-design techniques. Practical components include programming ARM-based platforms and interfacing sensors with microprocessors.
- IoT Prototyping: Students learn the fundamentals of Internet of Things (IoT) architecture, wireless communication protocols, sensor integration, and cloud connectivity. The course emphasizes rapid prototyping using platforms like Arduino, Raspberry Pi, and AWS IoT Core.
- Big Data Technologies: This course introduces students to distributed computing frameworks such as Apache Hadoop, Spark, and Kafka. Students learn how to process large datasets, perform analytics, and build scalable data pipelines for enterprise applications.
- Business Intelligence Tools: Focused on transforming raw data into actionable insights, this course covers tools like Tableau, Power BI, and Qlik Sense. Students gain experience in data visualization, dashboard creation, and predictive modeling for business intelligence purposes.
- Advanced Structural Analysis: This elective explores advanced methods for analyzing structures under various loads and conditions. Topics include finite element analysis, dynamic response of structures, and structural optimization techniques using MATLAB and ANSYS.
- Seismic Design Principles: Students learn how to design structures that can withstand earthquake forces. The course covers seismic hazard assessment, base isolation systems, damping devices, and retrofitting strategies for existing buildings.
- Concrete Technology: This course provides in-depth knowledge of concrete composition, mixing, curing, testing methods, and performance characteristics. Students gain hands-on experience with concrete testing equipment and learn about sustainable concrete alternatives.
- Bridge Engineering: Focused on the design and construction of bridge structures, this elective covers structural behavior under load, materials selection, and construction techniques. Practical sessions include bridge modeling using software tools like STAAD.Pro and AutoCAD.
- Renewable Energy Conversion Systems: Students study the principles of converting renewable energy sources into usable electricity. The course includes solar photovoltaic systems, wind turbines, hydroelectric generators, and geothermal systems with practical components involving system design and simulation.