Curriculum Overview
The Computer Applications program at Rai Technology University Bangalore is structured over eight semesters, with a carefully designed curriculum that balances theoretical knowledge with practical application. The program is divided into core courses, departmental electives, science electives, and laboratory sessions to provide a holistic educational experience.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | CS102 | Physics for Computer Science | 3-1-0-4 | None |
| 1 | CS103 | Introduction to Programming | 3-1-0-4 | None |
| 1 | CS104 | Computer Organization | 3-1-0-4 | None |
| 1 | CS105 | English for Engineers | 3-0-0-3 | None |
| 1 | CS106 | Lab: Introduction to Programming | 0-0-3-1 | None |
| 2 | CS201 | Engineering Mathematics II | 3-1-0-4 | CS101 |
| 2 | CS202 | Object-Oriented Programming | 3-1-0-4 | CS103 |
| 2 | CS203 | Data Structures and Algorithms | 3-1-0-4 | CS103 |
| 2 | CS204 | Database Management Systems | 3-1-0-4 | CS103 |
| 2 | CS205 | Discrete Mathematics | 3-1-0-4 | CS101 |
| 2 | CS206 | Lab: Object-Oriented Programming | 0-0-3-1 | CS103 |
| 3 | CS301 | Operating Systems | 3-1-0-4 | CS202 |
| 3 | CS302 | Computer Networks | 3-1-0-4 | CS202 |
| 3 | CS303 | Software Engineering | 3-1-0-4 | CS202 |
| 3 | CS304 | Compiler Design | 3-1-0-4 | CS202 |
| 3 | CS305 | Probability and Statistics | 3-1-0-4 | CS201 |
| 3 | CS306 | Lab: Operating Systems | 0-0-3-1 | CS202 |
| 4 | CS401 | Machine Learning | 3-1-0-4 | CS305 |
| 4 | CS402 | Cybersecurity | 3-1-0-4 | CS302 |
| 4 | CS403 | Data Mining | 3-1-0-4 | CS305 |
| 4 | CS404 | Cloud Computing | 3-1-0-4 | CS302 |
| 4 | CS405 | Human-Computer Interaction | 3-1-0-4 | CS202 |
| 4 | CS406 | Lab: Machine Learning | 0-0-3-1 | CS305 |
| 5 | CS501 | Advanced Algorithms | 3-1-0-4 | CS203 |
| 5 | CS502 | Web Technologies | 3-1-0-4 | CS202 |
| 5 | CS503 | Mobile Computing | 3-1-0-4 | CS202 |
| 5 | CS504 | Database Systems | 3-1-0-4 | CS204 |
| 5 | CS505 | Internet of Things | 3-1-0-4 | CS202 |
| 5 | CS506 | Lab: Web Technologies | 0-0-3-1 | CS202 |
| 6 | CS601 | Big Data Analytics | 3-1-0-4 | CS403 |
| 6 | CS602 | Artificial Intelligence | 3-1-0-4 | CS401 |
| 6 | CS603 | Network Security | 3-1-0-4 | CS402 |
| 6 | CS604 | Software Architecture | 3-1-0-4 | CS303 |
| 6 | CS605 | Quantitative Finance | 3-1-0-4 | CS305 |
| 6 | CS606 | Lab: Artificial Intelligence | 0-0-3-1 | CS401 |
| 7 | CS701 | Capstone Project | 0-0-6-6 | CS601 |
| 7 | CS702 | Research Methodology | 3-1-0-4 | CS403 |
| 7 | CS703 | Special Topics in Computer Applications | 3-1-0-4 | CS601 |
| 7 | CS704 | Professional Ethics | 3-1-0-4 | CS303 |
| 7 | CS705 | Internship | 0-0-0-12 | CS601 |
| 7 | CS706 | Lab: Capstone Project | 0-0-6-6 | CS601 |
| 8 | CS801 | Advanced Capstone Project | 0-0-6-6 | CS701 |
| 8 | CS802 | Thesis Writing | 3-1-0-4 | CS702 |
| 8 | CS803 | Specialized Electives | 3-1-0-4 | CS701 |
| 8 | CS804 | Final Project Presentation | 0-0-3-3 | CS801 |
| 8 | CS805 | Industry Exposure | 0-0-0-6 | CS705 |
| 8 | CS806 | Lab: Advanced Capstone Project | 0-0-6-6 | CS801 |
Advanced Departmental Electives
Departmental electives offer students the opportunity to explore specialized areas within computer applications. These courses are designed to provide in-depth knowledge and practical skills in emerging fields.
Machine Learning
This course covers advanced topics in machine learning, including deep learning, reinforcement learning, and neural networks. Students learn to implement algorithms using frameworks like TensorFlow and PyTorch. The course emphasizes practical applications in image recognition, natural language processing, and predictive modeling.
Cybersecurity
This course explores the principles and practices of cybersecurity, including network security, cryptography, and ethical hacking. Students gain hands-on experience through simulations and real-world case studies. The course also covers emerging threats and mitigation strategies in cloud computing and IoT environments.
Data Mining
This course introduces students to data mining techniques and algorithms for extracting knowledge from large datasets. Topics include clustering, classification, association rules, and anomaly detection. Students work with real-world datasets using tools like Python, R, and Weka.
Cloud Computing
This course covers the fundamentals of cloud computing, including virtualization, distributed systems, and cloud service models. Students learn to design and deploy applications on cloud platforms like AWS, Azure, and Google Cloud. The course also explores security and compliance in cloud environments.
Human-Computer Interaction
This course focuses on designing user-friendly interfaces and systems. Students learn about user research, usability testing, and interface design principles. The course includes hands-on projects involving prototyping and evaluating interactive systems.
Web Technologies
This course explores modern web development technologies, including HTML, CSS, JavaScript, and frameworks like React and Angular. Students learn to build responsive and interactive web applications. The course also covers backend development using Node.js and database integration.
Mobile Computing
This course covers the development of mobile applications for iOS and Android platforms. Students learn about mobile app design, user interface development, and backend integration. The course includes practical sessions on mobile development tools and platforms.
Database Systems
This course provides an in-depth understanding of database systems, including relational models, SQL, and database design. Students learn to implement database systems using tools like MySQL and PostgreSQL. The course also covers advanced topics like indexing, query optimization, and transaction management.
Internet of Things
This course introduces students to IoT concepts and technologies. Students learn about sensor networks, embedded systems, and IoT protocols. The course includes hands-on projects involving hardware and software integration.
Big Data Analytics
This course covers big data processing and analytics using tools like Hadoop, Spark, and Kafka. Students learn to analyze large datasets and extract meaningful insights. The course also explores data visualization and machine learning in big data environments.
Artificial Intelligence
This course covers the fundamentals of artificial intelligence, including search algorithms, knowledge representation, and reasoning. Students learn to implement AI systems using Python and frameworks like TensorFlow and PyTorch. The course emphasizes practical applications in robotics, natural language processing, and computer vision.
Network Security
This course explores network security protocols and practices. Students learn about firewalls, intrusion detection systems, and secure network design. The course includes hands-on labs involving network security tools and techniques.
Software Architecture
This course focuses on software design principles and architecture patterns. Students learn to design scalable and maintainable software systems. The course covers topics like microservices, cloud architecture, and system design principles.
Quantitative Finance
This course combines computer science with financial modeling and analysis. Students study financial markets, risk management, and algorithmic trading. The course includes exposure to financial data analysis and quantitative tools.
Project-Based Learning Philosophy
The department emphasizes project-based learning as a core component of the curriculum. This approach encourages students to apply theoretical knowledge to real-world problems, fostering critical thinking and innovation.
Mini-projects are assigned in the second and third years, allowing students to work on small-scale applications or research problems. These projects are designed to reinforce concepts learned in class and provide practical experience.
The final-year capstone project is a comprehensive endeavor that integrates all the knowledge and skills acquired throughout the program. Students work in teams to develop a complete solution to a real-world problem, often in collaboration with industry partners.
Project selection is based on student interests, faculty expertise, and industry relevance. Students are paired with faculty mentors who guide them through the research and development process. The evaluation criteria include technical proficiency, innovation, presentation skills, and project impact.