MITS Department of Computer Science, Pilani
1. Mathematics and Foundations
- Calculus
- Linear Algebra
- Discrete Mathematics
- Probability and Statistics
- Numerical Methods
- Mathematical Logic
- Graph Theory
- Theory of Computation
2. Programming Foundations
- Introduction to Computer Science
- Programming Fundamentals
- Object-Oriented Programming
- Programming in C/C++
- Programming in Java
- Python Programming
- Functional Programming
- Programming Languages and Paradigms
3. Algorithms and Data Structures
- Data Structures
- Design and Analysis of Algorithms
- Advanced Algorithms
- Computational Complexity
- Optimization Algorithms
4. Computer Systems
- Digital Logic and Computer Organization
- Computer Architecture
- Microprocessors and Microcontrollers
- Operating Systems
- Embedded Systems
- Parallel Computing
- Distributed Systems
- High-Performance Computing
- GPU Computing
5. Software Engineering
- Software Engineering
- Software Architecture
- Software Design and Design Patterns
- Requirements Engineering
- Software Testing and Quality Assurance
- DevOps
- Version Control and Collaborative Development
- Cloud-Native Application Development
Students should learn how a program becomes a reliable, maintainable and deployable software system.
6. Databases and Data
- Database Management Systems
- SQL and Relational Databases
- NoSQL Databases
- Data Modeling
- Data Warehousing
- Data Mining
- Big Data Systems
- Data Engineering
7. Networks, Internet and Cloud Computing
- Computer Networks
- Internet Technologies
- Web Application Development
- Distributed Computing
- Cloud Computing
- Data Center Technologies
- Internet of Things
- Edge Computing
8. Cybersecurity
- Foundations of Cybersecurity
- Network Security
- Computer and Operating-System Security
- Cryptography
- Secure Software Development
- Web and Cloud Security
- Digital Forensics
- Privacy and Data Protection
9. Artificial Intelligence and Data Science
Even with a separate Department of Artificial Intelligence, every CS graduate should understand modern AI.
- Introduction to Artificial Intelligence
- Machine Learning
- Deep Learning
- Data Science
- Natural Language Processing
- Computer Vision
- Generative AI and Large Language Models
- AI Agents and Agentic Systems
- Responsible AI
The AI department would study these subjects in considerably greater depth, while the CS department would ensure that every computer scientist has sufficient knowledge to build software systems incorporating AI.
10. Important Advanced Computer Science Subjects
- Compiler Construction
- Formal Languages and Automata
- Computer Graphics
- Human–Computer Interaction
- Mobile Computing
- Blockchain and Distributed Ledger Technologies
- Quantum Computing
- Robotics
- Bioinformatics
- Scientific Computing
11. Laboratories
Programming Laboratory, Data Structures and Algorithms Laboratory, Digital Systems Laboratory, Operating Systems Laboratory, Database Laboratory, Networking Laboratory, Software Engineering Laboratory, Cybersecurity Laboratory, Cloud/Distributed Systems Laboratory, and AI/ML Laboratory.
12. Projects, Research and Professional Education
Courses or structured activities in Technical Communication, Research Methodology, Technology Entrepreneurship, Intellectual Property, Computing Ethics, Open-Source Software Development, Industry Internship, Minor Project, and Final-Year Capstone Project.
Computer Science: How computation, algorithms, software and computer systems work.
Artificial Intelligence: How machines learn, reason, perceive, generate, decide and act intelligently.
Computer Science and Artificial Intelligence departments will share foundational subjects such as programming, algorithms, mathematics, databases and computer systems, but they will diverge substantially in the advanced years.
