Comprehensive Course List
| Semester | Course Code | Full Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | CSE101 | Engineering Mathematics I | 3-0-0-3 | - |
| 1 | CSE102 | Physics for Engineers | 3-0-0-3 | - |
| 1 | CSE103 | Introduction to Programming using Python | 3-0-0-3 | - |
| 1 | CSE104 | English Communication Skills | 2-0-0-2 | - |
| 1 | CSE105 | Introduction to Computer Science | 2-0-0-2 | - |
| 1 | CSE106 | Workshop in Basic Engineering Tools | 1-0-3-2 | - |
| 2 | CSE201 | Engineering Mathematics II | 3-0-0-3 | CSE101 |
| 2 | CSE202 | Chemistry for Engineers | 3-0-0-3 | - |
| 2 | CSE203 | Data Structures and Algorithms | 3-0-0-3 | CSE103 |
| 2 | CSE204 | Object-Oriented Programming using Java | 3-0-0-3 | CSE103 |
| 2 | CSE205 | Computer Organization and Architecture | 3-0-0-3 | - |
| 2 | CSE206 | Engineering Graphics & Design | 1-0-3-2 | - |
| 3 | CSE301 | Database Management Systems | 3-0-0-3 | CSE203 |
| 3 | CSE302 | Software Engineering Principles | 3-0-0-3 | CSE204 |
| 3 | CSE303 | Web Technologies | 3-0-0-3 | CSE204 |
| 3 | CSE304 | Computer Networks | 3-0-0-3 | CSE205 |
| 3 | CSE305 | Operating Systems | 3-0-0-3 | CSE205 |
| 3 | CSE306 | Mathematical Foundation of Computer Science | 3-0-0-3 | CSE101 |
| 4 | CSE401 | Machine Learning | 3-0-0-3 | CSE301 |
| 4 | CSE402 | Cybersecurity Fundamentals | 3-0-0-3 | CSE304 |
| 4 | CSE403 | Data Science and Analytics | 3-0-0-3 | CSE301 |
| 4 | CSE404 | Mobile Application Development | 3-0-0-3 | CSE204 |
| 4 | CSE405 | Human-Computer Interaction | 3-0-0-3 | CSE203 |
| 4 | CSE406 | Cloud Computing and Distributed Systems | 3-0-0-3 | CSE304 |
| 5 | CSE501 | Advanced Algorithms | 3-0-0-3 | CSE203 |
| 5 | CSE502 | Research Methodology and Project Planning | 2-0-0-2 | - |
| 5 | CSE503 | Project Implementation & Testing | 4-0-0-4 | - |
| 5 | CSE504 | Capstone Project (Mini Project) | 2-0-0-2 | - |
| 5 | CSE505 | Internship Training | 2-0-0-2 | - |
| 5 | CSE506 | Entrepreneurship and Innovation | 2-0-0-2 | - |
| 6 | CSE601 | Advanced Topics in AI/ML | 3-0-0-3 | CSE401 |
| 6 | CSE602 | Penetration Testing and Ethical Hacking | 3-0-0-3 | CSE402 |
| 6 | CSE603 | Big Data Technologies | 3-0-0-3 | CSE301 |
| 6 | CSE604 | Advanced Web Development | 3-0-0-3 | CSE303 |
| 6 | CSE605 | Advanced Human Interface Design | 3-0-0-3 | CSE504 |
| 6 | CSE606 | Quantum Computing Concepts | 3-0-0-3 | CSE301 |
| 7 | CSE701 | Final Year Project (Research) | 6-0-0-6 | - |
| 7 | CSE702 | Capstone Project (Advanced) | 4-0-0-4 | - |
| 7 | CSE703 | Industry Internship | 2-0-0-2 | - |
| 7 | CSE704 | Technical Writing and Presentation Skills | 2-0-0-2 | - |
| 7 | CSE705 | Professional Ethics and Social Responsibility | 2-0-0-2 | - |
| 7 | CSE706 | Leadership and Team Management | 2-0-0-2 | - |
| 8 | CSE801 | Research Internship (Optional) | 4-0-0-4 | - |
| 8 | CSE802 | Final Project Defense | 2-0-0-2 | - |
| 8 | CSE803 | Industry Certification Preparation | 2-0-0-2 | - |
| 8 | CSE804 | Graduation Thesis Writing | 2-0-0-2 | - |
| 8 | CSE805 | Alumni Network & Career Guidance | 2-0-0-2 | - |
| 8 | CSE806 | Placement Preparation Workshop | 2-0-0-2 | - |
Advanced Departmental Electives
These advanced courses are designed to deepen students' understanding of specialized domains within computer applications and prepare them for leadership roles in industry or research.
1. Advanced Algorithms
This course delves into complex algorithmic techniques including approximation algorithms, online algorithms, and parameterized complexity. Students learn to analyze the efficiency of algorithms and design new ones for specific problem classes. The course includes practical sessions on algorithm implementation and optimization using modern programming languages.
2. Artificial Intelligence and Machine Learning
Building upon foundational knowledge, this course covers advanced topics in neural networks, deep learning architectures, reinforcement learning, and natural language processing. Students engage in projects involving real-world datasets and apply cutting-edge frameworks like TensorFlow, PyTorch, and Scikit-Learn.
3. Cybersecurity and Network Defense
This course explores the latest trends in cybersecurity, including zero-trust architecture, threat modeling, and secure coding practices. Students gain hands-on experience with security tools and conduct penetration testing exercises to identify vulnerabilities in network infrastructures.
4. Big Data Technologies
Focused on handling massive datasets efficiently, this course covers distributed computing frameworks like Apache Hadoop and Spark, data streaming platforms like Kafka, and NoSQL databases such as MongoDB and Cassandra. Students learn to implement scalable data processing pipelines using these technologies.
5. Cloud Computing and DevOps
This course introduces students to cloud platforms like AWS, Google Cloud Platform, and Microsoft Azure. It covers containerization with Docker, orchestration with Kubernetes, CI/CD pipelines, microservices architecture, and serverless computing models. Practical labs include setting up production-grade cloud environments.
6. Human-Computer Interaction (HCI)
This course emphasizes the design and evaluation of interactive systems for diverse users. Students learn user-centered design principles, usability testing methodologies, accessibility standards, and prototyping techniques. Projects involve designing interfaces for mobile apps, web applications, and assistive technologies.
7. Mobile Application Development
Students develop cross-platform mobile applications using frameworks like Flutter, React Native, and Xamarin. The course covers app lifecycle management, performance optimization, and integration with backend services. Emphasis is placed on user experience and app store deployment strategies.
8. Quantum Computing Concepts
Introducing quantum algorithms and computing models, this course explores qubit manipulation, quantum gates, and error correction techniques. Students experiment with quantum simulators like Qiskit and IBM Quantum Experience to solve computational problems beyond classical capabilities.
9. Software Architecture and Design Patterns
This advanced topic focuses on scalable software architecture principles, design patterns, and architectural frameworks. Students learn to model complex systems using UML diagrams, evaluate system scalability, and apply domain-driven design concepts in enterprise applications.
10. Computer Vision and Image Processing
Students explore image recognition algorithms, object detection models, and computer vision applications in robotics, medical imaging, and autonomous vehicles. Practical sessions involve using OpenCV, TensorFlow, and PyTorch for building real-time computer vision systems.
11. Data Mining and Predictive Analytics
This course teaches students how to extract meaningful patterns from large datasets using statistical methods and machine learning algorithms. Topics include clustering, classification, regression analysis, association rules, and anomaly detection in business intelligence contexts.
12. Internet of Things (IoT) and Embedded Systems
Students learn to design and deploy IoT solutions using sensors, actuators, and microcontrollers. The course covers wireless communication protocols, edge computing, and real-time data processing for smart cities, agriculture, and healthcare applications.
13. Natural Language Processing and Computational Linguistics
This course focuses on building systems that understand and generate human language using NLP techniques. Students work with language models like BERT, GPT, and transformer architectures to develop chatbots, sentiment analysis tools, and automated translation systems.
14. Network Security and Ethical Hacking
Students learn about network security vulnerabilities and defensive strategies through hands-on labs involving penetration testing, vulnerability assessment, and secure network design. The course includes real-world case studies from recent cyber incidents and defensive approaches.
15. Digital Forensics and Incident Response
This course prepares students to investigate digital crimes and respond to security breaches effectively. Students learn forensic techniques for recovering deleted files, analyzing malware behavior, and documenting evidence for legal proceedings in cybersecurity contexts.
Project-Based Learning Philosophy
At Nims University Jaipur, project-based learning forms the cornerstone of our Computer Applications program. This pedagogical approach ensures that students gain practical experience while developing critical thinking and problem-solving skills essential for professional success.
The curriculum includes both mini-projects and a final-year capstone project that spans multiple semesters. Mini-projects are introduced in the third year, allowing students to apply theoretical knowledge to real-world challenges. These projects are typically group-based and involve collaboration with faculty mentors from various domains.
Students begin their final-year thesis/capstone project during the sixth semester. This comprehensive endeavor requires them to identify a relevant problem, propose a solution, implement it using appropriate technologies, and document their findings in a research paper or technical report.
The selection of projects is guided by faculty expertise and industry trends. Students are encouraged to propose innovative ideas that align with current technological developments and societal needs. The faculty committee evaluates project proposals based on feasibility, relevance, and potential impact.
Evaluation criteria for projects include technical depth, innovation, presentation quality, teamwork, and documentation standards. Regular progress reviews ensure that students stay on track and receive feedback from mentors throughout the development process.
Our labs provide dedicated spaces for project work, equipped with high-performance hardware, cloud access, and software licenses. Students also have access to industry-standard tools and platforms for prototyping, testing, and deploying their solutions.