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+91 88943 57155
Pune, Maharashtra, India

Duration

4 Years

Computer Applications

G H Raisoni International Skill Tech University Pune
Duration
4 Years
Computer Applications UG OFFLINE

Duration

4 Years

Computer Applications

G H Raisoni International Skill Tech University Pune
Duration
Apply

Fees

₹5,00,000

Placement

92.0%

Avg Package

₹4,50,000

Highest Package

₹7,50,000

OverviewAdmissionsCurriculumFeesPlacements
4 Years
Computer Applications
UG
OFFLINE

Fees

₹5,00,000

Placement

92.0%

Avg Package

₹4,50,000

Highest Package

₹7,50,000

Seats

120

Students

1,200

ApplyCollege

Seats

120

Students

1,200

Curriculum

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.

SemesterCourse CodeCourse TitleCredit Structure (L-T-P-C)Pre-requisites
1CS101Mathematics for Computing3-1-0-4-
1CS102Physics of Electronics3-1-0-4-
1CS103Introduction to Programming3-1-0-4-
1CS104English for Technical Communication3-1-0-4-
1CS105Introduction to Computer Science3-1-0-4-
2CS201Data Structures and Algorithms3-1-0-4CS103
2CS202Digital Logic Design3-1-0-4-
2CS203Database Systems3-1-0-4CS103
2CS204Computer Organization and Architecture3-1-0-4CS102
2CS205Object-Oriented Programming with Java3-1-0-4CS103
3CS301Operating Systems3-1-0-4CS201, CS202
3CS302Computer Networks3-1-0-4CS201
3CS303Software Engineering3-1-0-4CS201, CS205
3CS304Web Technologies3-1-0-4CS205
3CS305Computer Graphics and Multimedia3-1-0-4CS201
4CS401Design and Analysis of Algorithms3-1-0-4CS201, CS301
4CS402Artificial Intelligence3-1-0-4CS201, CS303
4CS403Cybersecurity Fundamentals3-1-0-4CS201, CS302
4CS404Data Mining and Analytics3-1-0-4CS203
4CS405Mobile Computing3-1-0-4CS205, CS304
5CS501Advanced Machine Learning3-1-0-4CS402
5CS502Cloud Computing3-1-0-4CS302
5CS503Big Data Technologies3-1-0-4CS404
5CS504Distributed Systems3-1-0-4CS302, CS401
5CS505Human-Computer Interaction3-1-0-4CS304
6CS601Advanced Cybersecurity3-1-0-4CS403
6CS602Internet of Things (IoT)3-1-0-4CS302, CS504
6CS603DevOps and CI/CD3-1-0-4CS303, CS502
6CS604Game Development3-1-0-4CS305
6CS605Financial Engineering3-1-0-4CS404
7CS701Capstone Project I2-0-6-8CS501, CS503
7CS702Research Methodology2-0-4-6-
7CS703Elective I (AI)3-1-0-4CS501
7CS704Elective II (Cybersecurity)3-1-0-4CS601
7CS705Elective III (Data Science)3-1-0-4CS503
8CS801Capstone Project II2-0-6-8CS701
8CS802Internship0-0-12-12-
8CS803Elective IV (Cloud)3-1-0-4CS502
8CS804Elective V (IoT)3-1-0-4CS602
8CS805Elective VI (UX Design)3-1-0-4CS505

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.