- Faculty
Faculty of Engineering and Technology
- Department
Department of Computer Science and Engineering
- Campus
Technology Campus (Peenya Campus)
- Engagement Mode
Full Time
Overview
The B.Tech in Artificial Intelligence and Data Science is a four-year undergraduate programme designed to meet the surging global demand for skilled professionals in AI and Data Science.
With the AI market projected to reach nearly two trillion dollars by 2030 and over 2.3 million AI-related job openings expected globally, this programme equips students with the knowledge and skills to excel in this rapidly evolving field. The curriculum at M. S. Ramaiah University of Applied Sciences (RUAS) offers a strong foundation in mathematics, computing, and data engineering, combined with in-depth theoretical and practical exposure to core AI/DS domains.
The programme is designed to produce innovative, industry-ready engineers capable of developing data-driven solutions for real-world challenges. Students gain hands-on experience through internships, capstone projects, and industry-oriented electives, preparing them for high-impact careers in healthcare, finance, manufacturing, and beyond. With strong industry partnerships, world-class facilities, and an expert faculty, RUAS ensures a transformative learning experience.
Program Objectives
- To build a strong foundation in mathematics, computing, and sciences to solve complex problems in AI and Data Science.
- To develop analytic and innovative skills to create reliable, industry-ready solutions grounded in AI/DS principles.
- To cultivate managerial, entrepreneurial, and leadership skills with strong ethical values for global professional success.
Curriculum Details
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | MTF101B | Engineering Mathematics-1 | 3 | 1 | 0 | 4 | 100 |
| 2 | CYF102A | Engineering Chemistry | 2 | 1 | 2 | 4 | 100 |
| 3 | CSD103B | Introduction to Computer Science and Engineering | 3 | 0 | 2 | 4 | 100 |
| 4 | MEF103B | Engineering Graphics | 2 | 0 | 2 | 3 | 100 |
| 5 | TSM104A | Kannada Kali | 1 | 0 | 0 | 1 | 50 |
| 6 | DIL101A | Design Thinking and Idea Lab | 0 | 0 | 4 | 2 | 50 |
| Total | 11 | 2 | 10 | 18 | 500 | ||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | MTF101B | Engineering Mathematics-1 | 3 | 1 | 0 | 4 | 100 |
| 2 | PYF102B | Engineering Physics | 2 | 1 | 2 | 4 | 100 |
| 3 | CSD103B | Introduction to Computer Science and Engineering | 3 | 0 | 2 | 4 | 100 |
| 4 | CSL101A | AI Workshop Practice | 0 | 0 | 2 | 1 | 50 |
| 5 | TSM103A | Communicative English | 1 | 0 | 2 | 2 | 50 |
| 6 | BTN101A | Environmental Studies | 2 | 0 | 0 | 2 | 50 |
| Total | 11 | 2 | 8 | 17 | 450 | ||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | MTF102B | Engineering Mathematics-2 | 3 | 1 | 0 | 4 | 100 |
| 2 | CYF102A | Engineering Chemistry | 2 | 1 | 2 | 4 | 100 |
| 3 | ECD104A | Introduction to Electrical and Electronics Engineering | 3 | 1 | 0 | 4 | 100 |
| 4 | CSD105A | Programming in C | 2 | 0 | 2 | 3 | 100 |
| 5 | MEF103B | Engineering Graphics | 2 | 0 | 2 | 3 | 100 |
| 6 | TSM104A | Kannada Kali | 1 | 0 | 0 | 1 | 50 |
| 7 | DIL101A | Design Thinking and Idea Lab | 0 | 0 | 4 | 2 | 50 |
| Total | 13 | 3 | 10 | 21 | 600 | ||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | MTF102B | Engineering Mathematics-2 | 3 | 1 | 0 | 4 | 100 |
| 2 | PYF102B | Engineering Physics | 2 | 1 | 2 | 4 | 100 |
| 3 | ECD104A | Introduction to Electrical and Electronics Engineering | 3 | 1 | 0 | 4 | 100 |
| 4 | CSD105A | Programming in C | 2 | 0 | 2 | 3 | 100 |
| 5 | CSL101A | AI Workshop Practice | 0 | 0 | 2 | 1 | 50 |
| 6 | TSM103A | English Communication | 1 | 0 | 2 | 2 | 50 |
| 7 | BTN101A | Environmental Studies | 2 | 0 | 0 | 2 | 50 |
| Total | 13 | 3 | 8 | 20 | 550 | ||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | MTE301B | Probability and Statistics | 2 | 1 | 0 | 3 | 100 |
| 2 | CSD208B | Discrete Mathematics | 2 | 1 | 0 | 3 | 100 |
| 3 | CSC203B | Software Development Fundamentals | 2 | 0 | 2 | 3 | 100 |
| 4 | CSD201B | Data Structures and Algorithms | 3 | 0 | 2 | 4 | 100 |
| 5 | AID201A | Artificial Intelligence | 3 | 0 | 2 | 4 | 100 |
| 6 | AIC201A | Mathematics for Machine Learning | 2 | 1 | 0 | 3 | 100 |
| 7 | LAN201B | Indian Constitution, Human Rights and Professional Ethics | 2 | 0 | 0 | 2 | 50 |
| Total | 16 | 3 | 6 | 22 | 650 | ||
| Total Number of Contact Hours per Week | |||||||
| 8 | MTF210A | Foundation Mathematics-1 | 1 | 1 | 0 | 0 | Audit |
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | CSC212A | Differential Equation and Fourier Analysis | 2 | 1 | 0 | 3 | 100 |
| 2 | CSD206B | Design and Analysis of Algorithms | 2 | 1 | 0 | 3 | 100 |
| 3 | AIC203B | Machine Learning-1 | 3 | 0 | 2 | 4 | 100 |
| 4 | CSD209A | Database Systems | 3 | 0 | 2 | 4 | 100 |
| 5 | BAU201A | Entrepreneurial Mindset and Action | 3 | 0 | 0 | 3 | 100 |
| 6 | TSN201A | Essence of Indian Knowledge System | 1 | 0 | 0 | 1 | 50 |
| 7 | AII201A | Internship-1 | 0 | 0 | 4 | 2 | 50 |
| 8 | TSU201A | Universal Human Values-II | 2 | 0 | 0 | 2 | 50 |
| Total | 16 | 2 | 8 | 22 | 650 | ||
| Total Number of Contact Hours per Week | |||||||
| 9 | MTF220A | Foundation Mathematics-2 | 1 | 1 | 0 | 0 | Audit |
| Sl. No. | Code | Course Title | Theory (H/W/S) | Tutorials (H/W/S) | Practical (H/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | CSC307A | Generative AI Engineering | 2 | 0 | 2 | 3 | 100 |
| 2 | AIC306A | Data Engineering and Exploratory Data Analytics | 3 | 0 | 2 | 4 | 100 |
| 3 | AIC301A | Basics of Operating Systems | 2 | 0 | 2 | 3 | 100 |
| 4 | CSD301A | Computer Networks | 3 | 0 | 2 | 4 | 100 |
| 5 | AIC205A | Machine Learning-2 | 3 | 1 | 0 | 4 | 100 |
| 6 | BNE101A | Yoga for Health and Wellness | 0 | 0 | 4 | 2 | 50 |
| PTU101A | Sports | 1 | 0 | 2 | - | - | |
| 7 | PTU301A | Aptitude & Analytical Reasoning | 0 | 0 | 2 | 1 | 50 |
| Total | 14 | 1 | 16 | 21 | 600 | ||
| Sl. No. | Code | Course Title | Theory (H/W/S) | Tutorials (H/W/S) | Practical (H/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | CSD303A | Modern Application Development | 2 | 0 | 2 | 3 | 100 |
| 2 | AIC307A | Deep Learning for Computer Vision | 3 | 0 | 0 | 3 | 100 |
| 3 | CSC310A | Natural Language Processing | 3 | 0 | 2 | 4 | 100 |
| 4 | AIC308A | NoSQL Databases for AI Applications | 3 | 0 | 2 | 4 | 100 |
| 5 | DDDXXXX | Professional Core Elective-1 | 3 | 0 | 0 | 3 | 100 |
| 6 | CSC312A | Project Management & Finance | 2 | 0 | 0 | 2 | 50 |
| 7 | PTU302A | Logic and Coding | 0 | 0 | 2 | 1 | 50 |
| 8 | AII301A | Internship-2 | 0 | 0 | 4 | 2 | 50 |
| Total | 16 | 0 | 12 | 22 | 650 | ||
| Sl. No. | Code | Course Title | Theory (H/W/S) | Tutorials (H/W/S) | Practical (H/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | DDDXXXX | Professional Core Elective-2 | 3 | 0 | 0 | 3 | 100 |
| 2 | DDDXXXX | Professional Core Elective-3 | 3 | 0 | 0 | 3 | 100 |
| 3 | DDDXXXX | Professional Core Elective-4 | 3 | 0 | 0 | 3 | 100 |
| 4 | OETXXX | Open Elective-1 | 3 | 0 | 0 | 3 | 100 |
| 5 | AIP401A | Integrated Design Project | 0 | 0 | 8 | 4 | 100 |
| Total | 12 | 0 | 8 | 16 | 500 | ||
| Sl. No. | Code | Course Title | Theory (H/W/S) | Tutorials (H/W/S) | Practical (H/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | OETXXX | Open Elective-2 | 3 | 0 | 0 | 3 | 100 |
| 2 | AIS411A | Technical Seminar | 0 | 0 | 4 | 2 | 50 |
| 3 | AIP402A | Capstone Project | 0 | 0 | 24 | 12 | 300 |
| 4 | AII401A | Internship-3 | 0 | 0 | 4 | 2 | 50 |
| Total | 3 | 0 | 32 | 19 | 500 | ||
| Stream | PCE-1 | PCE-2 | PCE-3 | PCE-4 |
|---|---|---|---|---|
| Software Development | CSE301A Software Architecture |
CSC301A Compilers |
CSE405A Principles and Practices of Software Testing |
CSE407A Service Oriented Architecture |
| Artificial Intelligence in Multiple Disciplines | AIE404A Biomedical Signal Modalities |
AIE403A Artificial Intelligence in Healthcare |
AIE407A Data Privacy and Ethics in AI |
AIE408A Multimodal Data Analysis and Visualization |
| Models of Computation | MCE401A Information Theory and Coding |
DEE501A Multimedia Processing |
MCC309A Quantum Computing |
MCE405A Theory of Computation |
| Networking & Cyber Security | CSE302A Principles and Practices of Cryptography |
CSC306A Information Security and Protection |
ISE404A Internet of Things |
AIE409A Blockchain and Its Application |
| Advanced Machine Learning | CSE408A Computational Intelligence |
AIE405A Reinforcement Learning |
CSE432A Agentic AI |
AIE406A Generative AI and Applications |
| Data Science and Analytics | ISE302A Data Processing |
AIE410A Time Series Analysis and Forecasting |
AIE411A Graph Analytics and Network Science |
AIE412A Cloud Data Engineering |
Note:
Students are required to select:
One Professional Core Elective in Semester 6 from PCE-1 Group
Three Professional Core Electives in Semester 7, one each from PCE-2, PCE-3, and PCE-4 Groups
Eligibility Criteria
- Pass in 2nd PUC / 12th Std / Equivalent Examination with English as one of the languages.
- Obtained a minimum of 45% of marks in aggregate in Physics and Mathematics along with Chemistry / Biotechnology / Biology / Electronics / Computer Science.
- Admission to University quota will be based on COMEDK / JEE / KCET scores.
Fee Structure
Fee Structure 2026–27
| Course | Total Fees Per Year |
|---|---|
| B.Tech AI & DS | ₹ 5,00,000 |
Intake
60 Seats
Career Path
Graduates of the B.Tech in Artificial Intelligence and Data Science programme can pursue careers as:
- Data Scientist
- Machine Learning Engineer
- AI Research Scientist
- Software Developer
- Deep Learning Engineer
- Natural Language Processing (NLP) Engineer
- Computer Vision Engineer
- Business Intelligence Developer
- Robotics Scientist
- Database Developer
- Cloud Data Engineer
FAQs
It is a four-year full-time undergraduate programme (8 semesters).
Admission to the university quota will be based on COMEDK / JEE / KCET scores.
Yes, the programme includes three internships (Internship-1, Internship-2, and Internship-3), capstone projects, and industry-oriented electives as part of the curriculum.
Yes, the programme offers Professional Core Electives across six streams, allowing students to tailor their learning to their career interests.
Graduates can work as Data Scientists, Machine Learning Engineers, AI Research Scientists, and more across sectors like healthcare, finance, manufacturing, and education, with placement packages reaching up to ₹45 LPA.
Contact
Start your journey with MSRUAS
MS Ramaiah University of Applied Sciences
Heritage Building, Gnana Gangothri Campus
New BEL Road, MSR Nagar,
Bengaluru – 560054
ADDRESS
