Comprehensive Course Structure
The Computer Applications program at G H Raisoni International Skill Tech University Pune is meticulously structured to ensure a balanced progression from foundational concepts to advanced specializations. The curriculum spans eight semesters, with each semester comprising core courses, departmental electives, science electives, and laboratory components designed to foster both theoretical understanding and practical application.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | CS101 | Mathematics for Computing | 3-1-0-4 | - |
| 1 | CS102 | Physics of Electronics | 3-1-0-4 | - |
| 1 | CS103 | Introduction to Programming | 3-1-0-4 | - |
| 1 | CS104 | English for Technical Communication | 3-1-0-4 | - |
| 1 | CS105 | Introduction to Computer Science | 3-1-0-4 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-1-0-4 | CS103 |
| 2 | CS202 | Digital Logic Design | 3-1-0-4 | - |
| 2 | CS203 | Database Systems | 3-1-0-4 | CS103 |
| 2 | CS204 | Computer Organization and Architecture | 3-1-0-4 | CS102 |
| 2 | CS205 | Object-Oriented Programming with Java | 3-1-0-4 | CS103 |
| 3 | CS301 | Operating Systems | 3-1-0-4 | CS201, CS202 |
| 3 | CS302 | Computer Networks | 3-1-0-4 | CS201 |
| 3 | CS303 | Software Engineering | 3-1-0-4 | CS201, CS205 |
| 3 | CS304 | Web Technologies | 3-1-0-4 | CS205 |
| 3 | CS305 | Computer Graphics and Multimedia | 3-1-0-4 | CS201 |
| 4 | CS401 | Design and Analysis of Algorithms | 3-1-0-4 | CS201, CS301 |
| 4 | CS402 | Artificial Intelligence | 3-1-0-4 | CS201, CS303 |
| 4 | CS403 | Cybersecurity Fundamentals | 3-1-0-4 | CS201, CS302 |
| 4 | CS404 | Data Mining and Analytics | 3-1-0-4 | CS203 |
| 4 | CS405 | Mobile Computing | 3-1-0-4 | CS205, CS304 |
| 5 | CS501 | Advanced Machine Learning | 3-1-0-4 | CS402 |
| 5 | CS502 | Cloud Computing | 3-1-0-4 | CS302 |
| 5 | CS503 | Big Data Technologies | 3-1-0-4 | CS404 |
| 5 | CS504 | Distributed Systems | 3-1-0-4 | CS302, CS401 |
| 5 | CS505 | Human-Computer Interaction | 3-1-0-4 | CS304 |
| 6 | CS601 | Advanced Cybersecurity | 3-1-0-4 | CS403 |
| 6 | CS602 | Internet of Things (IoT) | 3-1-0-4 | CS302, CS504 |
| 6 | CS603 | DevOps and CI/CD | 3-1-0-4 | CS303, CS502 |
| 6 | CS604 | Game Development | 3-1-0-4 | CS305 |
| 6 | CS605 | Financial Engineering | 3-1-0-4 | CS404 |
| 7 | CS701 | Capstone Project I | 2-0-6-8 | CS501, CS503 |
| 7 | CS702 | Research Methodology | 2-0-4-6 | - |
| 7 | CS703 | Elective I (AI) | 3-1-0-4 | CS501 |
| 7 | CS704 | Elective II (Cybersecurity) | 3-1-0-4 | CS601 |
| 7 | CS705 | Elective III (Data Science) | 3-1-0-4 | CS503 |
| 8 | CS801 | Capstone Project II | 2-0-6-8 | CS701 |
| 8 | CS802 | Internship | 0-0-12-12 | - |
| 8 | CS803 | Elective IV (Cloud) | 3-1-0-4 | CS502 |
| 8 | CS804 | Elective V (IoT) | 3-1-0-4 | CS602 |
| 8 | CS805 | Elective VI (UX Design) | 3-1-0-4 | CS505 |
Advanced Departmental Electives
The department offers a wide range of advanced elective courses that allow students to delve deeper into specialized areas of interest and gain expertise in emerging technologies. These electives are designed to align with industry trends and prepare students for leadership roles in their chosen fields.
Advanced Machine Learning
This course builds upon foundational knowledge in machine learning by exploring advanced algorithms, neural networks, reinforcement learning, and deep learning frameworks such as TensorFlow and PyTorch. Students engage in hands-on projects involving image recognition, natural language processing, and predictive analytics.
Cloud Computing
The Cloud Computing elective introduces students to cloud architecture, deployment models (IaaS, PaaS, SaaS), virtualization technologies, and major cloud platforms like AWS, Azure, and Google Cloud. Practical labs involve designing scalable applications and implementing containerized solutions using Docker and Kubernetes.
Big Data Technologies
This course focuses on processing and analyzing large datasets using Hadoop, Spark, NoSQL databases, and streaming frameworks like Kafka. Students learn to build end-to-end big data pipelines and apply advanced analytics techniques for business intelligence and decision-making.
Distributed Systems
Students study the principles of distributed computing including consensus algorithms, fault tolerance, synchronization mechanisms, and network protocols. The course includes lab work on building fault-tolerant systems and understanding microservices architectures using tools like Apache Zookeeper and gRPC.
Human-Computer Interaction
This elective emphasizes user-centered design principles, usability evaluation methods, prototyping techniques, and accessibility standards. Students learn to conduct user research, create wireframes, perform usability testing, and develop inclusive digital products through iterative design processes.
Advanced Cybersecurity
The Advanced Cybersecurity course covers advanced topics such as penetration testing, cryptography, network security, incident response, and compliance frameworks. Students engage in ethical hacking labs and learn to implement robust security measures in enterprise environments.
Internet of Things (IoT)
This course explores the architecture, protocols, sensors, actuators, and applications of IoT systems. Students work on real-world projects involving smart cities, industrial automation, and healthcare monitoring using platforms like Arduino, Raspberry Pi, and MQTT.
DevOps and CI/CD
The DevOps elective focuses on continuous integration, delivery, and deployment practices using tools like Jenkins, GitLab CI, Ansible, and Terraform. Students learn to automate infrastructure provisioning, implement monitoring solutions, and optimize development workflows for faster release cycles.
Game Development
This course covers game design principles, 3D modeling, animation techniques, sound synthesis, and engine development using Unity and Unreal Engine. Students build interactive games from concept to completion, learning best practices in asset management, performance optimization, and user experience design.
Financial Engineering
The Financial Engineering elective introduces students to quantitative methods used in finance including derivatives pricing, risk management, portfolio optimization, and algorithmic trading strategies. Practical applications involve using Python libraries like NumPy, Pandas, and QuantLib for financial modeling and backtesting.
Project-Based Learning Philosophy
The department strongly advocates for project-based learning as a cornerstone of the educational experience. Projects are integrated throughout the curriculum to provide students with opportunities to apply theoretical concepts in practical scenarios. The structure includes both individual assignments and group collaborations that simulate real-world team dynamics.
Mini-projects are assigned at regular intervals during semesters, focusing on specific learning objectives related to core subjects. These projects typically span 2-4 weeks and require students to demonstrate their understanding through documentation, presentations, and peer evaluations. The final-year thesis or capstone project is a comprehensive endeavor that spans the entire academic year.
Students select their projects based on interests, faculty expertise, and industry relevance. Faculty mentors guide students throughout the process, offering feedback on methodology, feasibility, and outcomes. Projects are evaluated using rubrics that assess technical competency, innovation, teamwork, communication skills, and impact.