Comprehensive Course Structure
The Computer Science program at Anjaneya University Raipur is structured across eight semesters, with each semester comprising a mix of core courses, departmental electives, science electives, and laboratory sessions. The curriculum is designed to build foundational knowledge in the first two years before transitioning into specialized areas in the later semesters.
| Semester | Course Code | Course Title | L-T-P-C | Prerequisites |
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | CS102 | Mathematics for Computer Science | 3-0-0-3 | - |
| 1 | CS103 | Physics for Computing | 3-0-0-3 | - |
| 1 | CS104 | Digital Logic Design | 3-0-0-3 | - |
| 1 | CS105 | Computer Fundamentals | 2-0-0-2 | - |
| 1 | CS106 | Lab: Programming & Debugging | 0-0-3-1 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | CS202 | Object-Oriented Programming (Java) | 3-0-0-3 | CS101 |
| 2 | CS203 | Operating Systems | 3-0-0-3 | CS101 |
| 2 | CS204 | Computer Networks | 3-0-0-3 | CS103 |
| 2 | CS205 | Database Management Systems | 3-0-0-3 | CS101 |
| 2 | CS206 | Lab: Data Structures & Algorithms | 0-0-3-1 | CS201 |
| 3 | CS301 | Software Engineering | 3-0-0-3 | CS202 |
| 3 | CS302 | Computer Architecture | 3-0-0-3 | CS104 |
| 3 | CS303 | Design & Analysis of Algorithms | 3-0-0-3 | CS201 |
| 3 | CS304 | Discrete Mathematics | 3-0-0-3 | CS102 |
| 3 | CS305 | Digital Image Processing | 3-0-0-3 | CS201 |
| 3 | CS306 | Lab: Operating Systems & Network | 0-0-3-1 | CS203, CS204 |
| 4 | CS401 | Machine Learning | 3-0-0-3 | CS301 |
| 4 | CS402 | Cryptography & Network Security | 3-0-0-3 | CS204 |
| 4 | CS403 | Data Mining | 3-0-0-3 | CS301 |
| 4 | CS404 | Web Technologies | 3-0-0-3 | CS202 |
| 4 | CS405 | Human Computer Interaction | 3-0-0-3 | CS301 |
| 4 | CS406 | Lab: Machine Learning & Web Dev | 0-0-3-1 | CS401, CS404 |
| 5 | CS501 | Advanced Database Systems | 3-0-0-3 | CS205 |
| 5 | CS502 | Cloud Computing | 3-0-0-3 | CS204 |
| 5 | CS503 | Artificial Intelligence | 3-0-0-3 | CS401 |
| 5 | CS504 | Computer Vision | 3-0-0-3 | CS305 |
| 5 | CS505 | Embedded Systems | 3-0-0-3 | CS203 |
| 5 | CS506 | Lab: Cloud & AI Projects | 0-0-3-1 | CS502, CS503 |
| 6 | CS601 | Research Methodology | 3-0-0-3 | - |
| 6 | CS602 | Capstone Project I | 2-0-0-2 | CS501, CS502 |
| 6 | CS603 | Mobile Application Development | 3-0-0-3 | CS202 |
| 6 | CS604 | Big Data Analytics | 3-0-0-3 | CS403 |
| 6 | CS605 | Internet of Things (IoT) | 3-0-0-3 | CS203 |
| 6 | CS606 | Lab: IoT & Mobile Dev | 0-0-3-1 | CS603, CS605 |
| 7 | CS701 | Capstone Project II | 2-0-0-2 | CS602 |
| 7 | CS702 | Special Topics in AI | 3-0-0-3 | CS503 |
| 7 | CS703 | Advanced Machine Learning | 3-0-0-3 | CS401 |
| 7 | CS704 | Reinforcement Learning | 3-0-0-3 | CS401 |
| 7 | CS705 | Human Factors in Design | 3-0-0-3 | CS505 |
| 7 | CS706 | Lab: Capstone & Advanced AI | 0-0-3-1 | CS701, CS703 |
| 8 | CS801 | Internship | 2-0-0-2 | - |
| 8 | CS802 | Project Presentation | 2-0-0-2 | CS701 |
| 8 | CS803 | Elective Course A | 3-0-0-3 | - |
| 8 | CS804 | Elective Course B | 3-0-0-3 | - |
| 8 | CS805 | Elective Course C | 3-0-0-3 | - |
| 8 | CS806 | Lab: Final Project | 0-0-3-1 | CS802 |
Detailed Course Descriptions
The department offers several advanced departmental elective courses that allow students to delve deeper into specialized areas of interest. These courses are designed to provide both theoretical knowledge and practical skills necessary for cutting-edge research and industry applications.
Machine Learning (CS401): This course explores the fundamentals of machine learning algorithms, including supervised and unsupervised learning, neural networks, deep learning architectures, reinforcement learning, and their applications in real-world scenarios. Students gain hands-on experience with frameworks like TensorFlow, PyTorch, and scikit-learn.
Cryptography & Network Security (CS402): The course covers modern cryptographic techniques, secure communication protocols, firewall design, intrusion detection systems, and network vulnerability assessment. It also addresses ethical hacking and digital forensics in cybersecurity contexts.
Data Mining (CS403): This course introduces students to data mining techniques for extracting meaningful patterns from large datasets. Topics include clustering, classification, association rules, anomaly detection, and data visualization tools such as Weka and KNIME.
Web Technologies (CS404): Students learn full-stack web development using modern frameworks such as React, Node.js, and MongoDB. The course emphasizes responsive design, RESTful APIs, authentication mechanisms, and deployment strategies for scalable web applications.
Human Computer Interaction (CS505): This elective focuses on designing user interfaces that are intuitive, accessible, and effective. It covers usability testing, cognitive psychology principles, prototyping techniques, accessibility standards, and the impact of design choices on user experience.
Advanced Database Systems (CS501): This course delves into advanced concepts in database management, including transaction processing, recovery mechanisms, query optimization, parallel databases, and distributed systems. Students explore NoSQL databases and cloud-based solutions.
Cloud Computing (CS502): Students are introduced to cloud computing models, virtualization technologies, infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). The course includes hands-on experience with AWS, Azure, and Google Cloud Platform.
Artificial Intelligence (CS503): This comprehensive course covers AI fundamentals, problem-solving strategies, search algorithms, knowledge representation, planning, reasoning under uncertainty, and natural language processing. It prepares students for advanced research in AI.
Computer Vision (CS504): The course explores image processing techniques, feature extraction, object recognition, and deep learning applications in computer vision. Students work with libraries like OpenCV and TensorFlow to build visual recognition systems.
Embedded Systems (CS505): This course focuses on designing embedded software for microcontrollers and real-time systems. It covers hardware-software integration, resource constraints, timing requirements, and programming languages such as C/C++ and assembly.
Mobile Application Development (CS603): Students develop cross-platform mobile applications using frameworks like Flutter and React Native. The course includes UI/UX design principles, app store publishing, backend integration, and performance optimization.
Big Data Analytics (CS604): This elective teaches students how to process and analyze large datasets using tools such as Hadoop, Spark, and Kafka. It covers data streaming, batch processing, predictive modeling, and visualization techniques for big data analytics.
Internet of Things (IoT) (CS605): Students explore IoT architecture, sensor networks, wireless communication protocols, edge computing, and smart city applications. The course includes hands-on labs using Arduino, Raspberry Pi, and cloud platforms like AWS IoT Core.
Project-Based Learning Philosophy
The department strongly emphasizes project-based learning as a cornerstone of its educational approach. Projects are integrated throughout the curriculum to provide students with opportunities to apply theoretical knowledge in practical contexts.
Mini-projects are assigned during the first three years, focusing on specific topics within each semester's coursework. These projects help reinforce concepts learned in lectures and encourage collaborative problem-solving among peers. Each project is evaluated based on technical accuracy, innovation, presentation quality, and team collaboration.
The final-year thesis/capstone project serves as a culmination of the student’s learning journey. Students select projects aligned with their chosen specialization track or personal interest areas. They work closely with faculty mentors who guide them through research methodologies, experimental design, data analysis, and report writing.
Project selection involves a formal proposal submission process where students must justify their choice, outline objectives, propose methodology, and present expected outcomes. Faculty members review proposals and assign suitable mentors based on expertise alignment and availability.
Evaluation criteria for capstone projects include innovation, feasibility, impact, documentation quality, presentation skills, and adherence to ethical standards. Students are required to submit final reports and deliver oral presentations to a panel of experts from academia and industry.