Curriculum Overview
The Computer Science program at Homoeopathy University Jaipur is meticulously structured to provide a comprehensive understanding of computing principles and their practical applications. The curriculum spans eight semesters, with each semester offering a blend of core courses, departmental electives, science electives, and laboratory sessions designed to foster both theoretical knowledge and hands-on experience.
Semester-wise Course Structure
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | CS102 | Mathematics for Computing I | 3-0-0-3 | - |
| 1 | CS103 | Computer Organization | 3-0-0-3 | - |
| 1 | CS104 | Basic Electronics | 3-0-0-3 | - |
| 1 | CS105 | Introduction to Data Structures and Algorithms | 3-0-0-3 | - |
| 1 | CS106 | English for Communication | 2-0-0-2 | - |
| 1 | CS107 | Lab: Introduction to Programming | 0-0-3-1 | - |
| 2 | CS201 | Data Structures and Algorithms II | 3-0-0-3 | CS105 |
| 2 | CS202 | Database Systems | 3-0-0-3 | CS105 |
| 2 | CS203 | Operating Systems | 3-0-0-3 | CS103 |
| 2 | CS204 | Software Engineering | 3-0-0-3 | CS105 |
| 2 | CS205 | Mathematics for Computing II | 3-0-0-3 | CS102 |
| 2 | CS206 | Physics Laboratory | 0-0-3-1 | - |
| 2 | CS207 | Lab: Data Structures and Algorithms | 0-0-3-1 | CS105 |
| 3 | CS301 | Computer Networks | 3-0-0-3 | CS203 |
| 3 | CS302 | Object-Oriented Programming with Java | 3-0-0-3 | CS101 |
| 3 | CS303 | Microprocessor Architecture | 3-0-0-3 | CS104 |
| 3 | CS304 | Discrete Mathematics | 3-0-0-3 | CS102 |
| 3 | CS305 | Probability and Statistics for Computing | 3-0-0-3 | CS102 |
| 3 | CS306 | Lab: Object-Oriented Programming with Java | 0-0-3-1 | CS101 |
| 4 | CS401 | Artificial Intelligence and Machine Learning | 3-0-0-3 | CS201, CS305 |
| 4 | CS402 | Cybersecurity Fundamentals | 3-0-0-3 | CS301 |
| 4 | CS403 | Data Mining and Big Data Analytics | 3-0-0-3 | CS202, CS305 |
| 4 | CS404 | Cloud Computing | 3-0-0-3 | CS301 |
| 4 | CS405 | Human-Computer Interaction | 3-0-0-3 | CS204 |
| 4 | CS406 | Lab: Cloud Computing and Big Data | 0-0-3-1 | CS403 |
| 5 | CS501 | Advanced Algorithms | 3-0-0-3 | CS201 |
| 5 | CS502 | Distributed Systems | 3-0-0-3 | CS301 |
| 5 | CS503 | Software Testing and Quality Assurance | 3-0-0-3 | CS204 |
| 5 | CS504 | Internet of Things (IoT) | 3-0-0-3 | CS303 |
| 5 | CS505 | Embedded Systems Design | 3-0-0-3 | CS303 |
| 5 | CS506 | Lab: Embedded Systems and IoT | 0-0-3-1 | CS504 |
| 6 | CS601 | Research Methodology | 2-0-0-2 | - |
| 6 | CS602 | Project Management | 2-0-0-2 | - |
| 6 | CS603 | Elective Course 1 | 3-0-0-3 | - |
| 6 | CS604 | Elective Course 2 | 3-0-0-3 | - |
| 6 | CS605 | Mini Project I | 0-0-3-1 | - |
| 6 | CS606 | Mini Project II | 0-0-3-1 | - |
| 7 | CS701 | Final Year Thesis/Project | 4-0-0-4 | CS605, CS606 |
| 7 | CS702 | Internship | 0-0-3-1 | - |
| 7 | CS703 | Special Topics in Computer Science | 3-0-0-3 | - |
| 7 | CS704 | Elective Course 3 | 3-0-0-3 | - |
| 7 | CS705 | Elective Course 4 | 3-0-0-3 | |
| 8 | CS801 | Capstone Project | 6-0-0-6 | CS701, CS702 |
| 8 | CS802 | Research Internship | 0-0-3-1 | - |
Advanced Departmental Elective Courses
The department offers a wide range of advanced elective courses designed to deepen students' understanding and provide specialized knowledge in various domains. These courses are taught by experienced faculty members who are actively involved in research and industry projects.
- Deep Learning with TensorFlow: This course introduces students to neural networks, convolutional networks, recurrent networks, and transformer models using TensorFlow and Keras frameworks. Students learn how to build and train deep learning models for image recognition, natural language processing, and other applications.
- Cryptography and Network Security: Covering symmetric and asymmetric encryption, hash functions, digital signatures, and secure communication protocols, this course prepares students for careers in cybersecurity. It includes hands-on labs on implementing cryptographic algorithms and analyzing vulnerabilities in network systems.
- Big Data Technologies and Analytics: Students explore Hadoop, Spark, Hive, Pig, and other big data tools to process and analyze large datasets. The course emphasizes real-world applications such as recommendation systems, fraud detection, and predictive analytics.
- DevOps and CI/CD Pipelines: This course covers continuous integration, continuous delivery, containerization with Docker, orchestration with Kubernetes, and automation tools like Jenkins and GitLab CI. Students gain practical experience in setting up DevOps pipelines for software deployment and maintenance.
- Game Development Using Unity: Designed for students interested in interactive media, this course teaches game design principles, scripting with C#, and asset creation using Unity engine. Projects include building 2D and 3D games from scratch, integrating audio and visual elements, and deploying across multiple platforms.
- Quantum Computing Fundamentals: Introducing quantum bits (qubits), quantum gates, entanglement, and quantum algorithms, this course explores the theoretical foundations of quantum computing. Students learn how to simulate quantum circuits using Qiskit and IBM Quantum Experience platform.
- Blockchain Technologies and Smart Contracts: This course covers blockchain architecture, consensus mechanisms, cryptocurrency frameworks, smart contracts, and decentralized applications (dApps). Students develop practical skills in Ethereum development using Solidity and Truffle framework.
- User Experience Design and Prototyping: Focused on human-centered design principles, this course teaches students how to conduct user research, create personas, build wireframes and prototypes, and evaluate usability of digital products. Tools like Figma, Sketch, and Adobe XD are used extensively.
- Internet of Things (IoT) and Sensor Networks: Exploring IoT architecture, wireless communication protocols, sensor technologies, and edge computing, this course enables students to design and deploy IoT systems for smart agriculture, environmental monitoring, healthcare tracking, and industrial automation.
- Computational Biology and Bioinformatics: Combining computational methods with biological data analysis, this course introduces students to genomics, proteomics, phylogenetic trees, and molecular modeling. Students work on projects involving gene expression analysis and protein structure prediction using Python and R libraries.
Project-Based Learning Philosophy
The department places significant emphasis on project-based learning as a core component of the educational experience. The philosophy behind this approach is rooted in the belief that students learn best when they engage actively with real-world problems and develop solutions collaboratively.
Mini projects begin in the second year, where students are assigned small-scale tasks related to their coursework or interests. These projects encourage experimentation, critical thinking, and teamwork. The mini-projects are evaluated based on design documentation, presentation skills, and final deliverables.
The final-year thesis/capstone project is a comprehensive endeavor that spans the entire seventh semester. Students select a topic aligned with their specialization track or personal interest under the guidance of a faculty mentor. The project involves extensive literature review, experimental design, implementation, testing, and documentation.
Students are encouraged to participate in research initiatives and collaborate with industry partners on live projects. This exposure helps them understand the practical implications of their studies and prepares them for professional environments.
The evaluation criteria for mini-projects include:
- Problem definition and scope clarity
- Design and methodology
- Implementation quality
- Documentation standards
- Presentation and communication skills
For the final-year thesis, additional factors such as originality, contribution to the field, technical depth, and scholarly rigor are assessed. Students must submit a formal report and defend their work in front of an evaluation panel comprising faculty members and external experts.