Course Structure Overview
The Computer Applications program at Shri Rawatpura Sarkar University Raipur is structured over 8 semesters, with a balanced mix of core courses, departmental electives, science electives, and laboratory sessions. The program aims to provide students with a comprehensive understanding of computer science principles while allowing them to specialize in areas of interest.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | None |
| 1 | CS102 | Mathematics for Computer Science | 3-0-0-3 | None |
| 1 | CS103 | Physics for Computer Science | 3-0-0-3 | None |
| 1 | CS104 | Chemistry for Computer Science | 3-0-0-3 | None |
| 1 | CS105 | English for Communication | 3-0-0-3 | None |
| 1 | CS106 | Introduction to Computer Science | 3-0-0-3 | None |
| 1 | CS107 | Computer Laboratory | 0-0-3-1 | None |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | CS202 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| 2 | CS203 | Database Management Systems | 3-0-0-3 | CS101 |
| 2 | CS204 | Computer Networks | 3-0-0-3 | CS101 |
| 2 | CS205 | Discrete Mathematics | 3-0-0-3 | CS102 |
| 2 | CS206 | Computer Architecture | 3-0-0-3 | CS103 |
| 2 | CS207 | Lab Sessions | 0-0-3-1 | CS101 |
| 3 | CS301 | Operating Systems | 3-0-0-3 | CS201 |
| 3 | CS302 | Software Engineering | 3-0-0-3 | CS202 |
| 3 | CS303 | Computer Graphics | 3-0-0-3 | CS201 |
| 3 | CS304 | Artificial Intelligence | 3-0-0-3 | CS201 |
| 3 | CS305 | Cryptography and Network Security | 3-0-0-3 | CS204 |
| 3 | CS306 | Web Technologies | 3-0-0-3 | CS202 |
| 3 | CS307 | Lab Sessions | 0-0-3-1 | CS201 |
| 4 | CS401 | Machine Learning | 3-0-0-3 | CS301 |
| 4 | CS402 | Deep Learning | 3-0-0-3 | CS301 |
| 4 | CS403 | Data Mining | 3-0-0-3 | CS301 |
| 4 | CS404 | Big Data Analytics | 3-0-0-3 | CS301 |
| 4 | CS405 | Mobile Application Development | 3-0-0-3 | CS306 |
| 4 | CS406 | Cloud Computing | 3-0-0-3 | CS301 |
| 4 | CS407 | Lab Sessions | 0-0-3-1 | CS301 |
| 5 | CS501 | Advanced Algorithms | 3-0-0-3 | CS301 |
| 5 | CS502 | Compiler Design | 3-0-0-3 | CS301 |
| 5 | CS503 | Computer Vision | 3-0-0-3 | CS301 |
| 5 | CS504 | Neural Networks | 3-0-0-3 | CS301 |
| 5 | CS505 | Internet of Things | 3-0-0-3 | CS301 |
| 5 | CS506 | Embedded Systems | 3-0-0-3 | CS301 |
| 5 | CS507 | Lab Sessions | 0-0-3-1 | CS301 |
| 6 | CS601 | Research Methodology | 3-0-0-3 | CS301 |
| 6 | CS602 | Capstone Project | 3-0-0-3 | CS301 |
| 6 | CS603 | Project Management | 3-0-0-3 | CS301 |
| 6 | CS604 | Advanced Data Structures | 3-0-0-3 | CS301 |
| 6 | CS605 | Quantitative Analysis | 3-0-0-3 | CS301 |
| 6 | CS606 | Business Intelligence | 3-0-0-3 | CS301 |
| 6 | CS607 | Lab Sessions | 0-0-3-1 | CS301 |
| 7 | CS701 | Internship | 0-0-0-6 | CS301 |
| 7 | CS702 | Mini Project | 0-0-0-3 | CS301 |
| 7 | CS703 | Research Paper Writing | 3-0-0-3 | CS301 |
| 7 | CS704 | Professional Development | 3-0-0-3 | CS301 |
| 7 | CS705 | Final Year Thesis | 3-0-0-3 | CS301 |
| 7 | CS706 | Lab Sessions | 0-0-3-1 | CS301 |
| 8 | CS801 | Final Year Project | 0-0-0-6 | CS301 |
| 8 | CS802 | Research Paper | 0-0-0-3 | CS301 |
| 8 | CS803 | Capstone Presentation | 0-0-0-3 | CS301 |
| 8 | CS804 | Professional Ethics | 3-0-0-3 | CS301 |
| 8 | CS805 | Industry Collaboration | 3-0-0-3 | CS301 |
| 8 | CS806 | Lab Sessions | 0-0-3-1 | CS301 |
Advanced Departmental Elective Courses
Advanced departmental elective courses are designed to provide students with in-depth knowledge and practical skills in specialized areas. These courses are offered in the later semesters and are typically chosen based on student interests and career goals.
Machine Learning: This course covers advanced topics in machine learning, including supervised and unsupervised learning, neural networks, deep learning, reinforcement learning, and natural language processing. Students will work on real-world datasets and develop models for various applications.
Deep Learning: This course focuses on the principles and applications of deep learning, including convolutional neural networks, recurrent neural networks, transformers, and generative adversarial networks. Students will implement and train deep learning models using frameworks like TensorFlow and PyTorch.
Data Mining: This course explores techniques for extracting knowledge from large datasets, including data preprocessing, clustering, classification, association rules, and anomaly detection. Students will use tools like Python, R, and SQL to analyze data and generate insights.
Big Data Analytics: This course covers the fundamentals of big data technologies and analytics, including Hadoop, Spark, NoSQL databases, and stream processing. Students will learn to process and analyze large datasets using distributed computing frameworks.
Mobile Application Development: This course focuses on developing mobile applications for iOS and Android platforms. Students will learn about mobile UI/UX design, app architecture, and integration with backend services using modern frameworks and tools.
Cloud Computing: This course covers cloud computing concepts, services, and architectures, including IaaS, PaaS, and SaaS. Students will learn to deploy and manage applications on cloud platforms like AWS, Azure, and Google Cloud.
Advanced Algorithms: This course delves into advanced algorithmic techniques, including graph algorithms, dynamic programming, greedy algorithms, and approximation algorithms. Students will analyze and design algorithms for complex computational problems.
Compiler Design: This course covers the principles and techniques of compiler design, including lexical analysis, parsing, semantic analysis, code generation, and optimization. Students will build a simple compiler for a programming language.
Computer Vision: This course explores the principles and applications of computer vision, including image processing, feature detection, object recognition, and scene understanding. Students will implement computer vision algorithms using libraries like OpenCV and TensorFlow.
Neural Networks: This course covers the fundamentals of neural networks, including perceptrons, multi-layer networks, backpropagation, and activation functions. Students will design and train neural networks for various tasks.
Internet of Things: This course explores the architecture and applications of IoT systems, including sensors, actuators, wireless communication, and data processing. Students will develop IoT applications for smart cities and smart homes.
Embedded Systems: This course focuses on designing and developing embedded systems for real-time applications. Students will work with microcontrollers, real-time operating systems, and hardware-software integration.
Research Methodology: This course introduces students to research methodologies and scientific writing. Students will learn to design experiments, analyze data, and write research papers for publication.
Capstone Project: This course provides students with an opportunity to work on a comprehensive project that integrates knowledge from various areas of computer science. Students will collaborate with faculty members and industry partners to develop innovative solutions.
Project Management: This course covers project management principles and practices, including planning, scheduling, risk management, and team leadership. Students will learn to manage software development projects effectively.
Advanced Data Structures: This course explores advanced data structures and their applications, including heaps, hash tables, trees, and graphs. Students will implement and analyze algorithms using these data structures.
Quantitative Analysis: This course covers mathematical and statistical methods for analyzing data, including regression analysis, hypothesis testing, and time series analysis. Students will use tools like Python and R for quantitative analysis.
Business Intelligence: This course focuses on the principles and tools of business intelligence, including data warehousing, ETL processes, and reporting. Students will learn to design and implement business intelligence solutions.
Project-Based Learning Approach
The department's philosophy on project-based learning is rooted in the belief that hands-on experience is essential for developing practical skills and deep understanding. The approach emphasizes real-world problem-solving, collaboration, and innovation.
The structure of project-based learning includes mini-projects in the early semesters, followed by a final-year thesis or capstone project. Mini-projects are typically completed in teams of 3-5 students and are designed to reinforce concepts learned in coursework. These projects are supervised by faculty members and are evaluated based on technical quality, presentation, and teamwork.
The final-year thesis or capstone project is a significant undertaking that allows students to apply their knowledge to a complex problem or research question. Students work closely with faculty mentors to select a topic, conduct research, and develop a solution. The project is typically completed over two semesters and involves extensive documentation, experimentation, and presentation.
Evaluation criteria for projects include technical depth, innovation, feasibility, and presentation quality. Students are encouraged to publish their work in conferences or journals and to present at university events. The department also hosts an annual project exhibition where students showcase their work to faculty, industry partners, and the public.
The project selection process involves faculty mentors who guide students in choosing topics that align with their interests and career goals. Students are encouraged to propose their own ideas, but they are also supported in exploring topics suggested by faculty or industry partners. The department provides resources such as research databases, software licenses, and access to specialized equipment to support project development.