The BS (Honours) in Data Science and Artificial Intelligence aims to prepare graduates who can responsibly design, analyse, and deploy data-driven and AI-based solutions to real-world problems across diverse domains, combining strong theoretical foundations with applied, interdisciplinary practice. The major in Computer Science offers both breadth and depth of knowledge in computing. Students undergo rigorous coursework coupled with exposure to a rich set of applications and tools through project-based courses, course projects, and the Undergraduate Capstone project and Summer Internships. The core courses under this Major provide a solid foundation to students in the field of Computer Science. In addition to core courses, students may pursue electives in one or more specialisation areas to gain a deeper understanding of these areas. The specialisation areas that we plan to offer are: artificial intelligence and machine learning, bio-computing, data science and its applications, quantum computing, systems and theoretical computer science.
Data Science and Artificial Intelligence have emerged at the intersection of Mathematics, Statistics and Computer Science, and domain-specific knowledge to address the challenges of extracting meaningful insights and creating intelligent systems from vast, complex, and diverse data. Over the years, Data Science and Artificial Intelligence have rapidly evolved to become foundational not just in technology sectors but across disciplines such as healthcare, education, social sciences, economics, finance, and environmental studies. The problems that data scientists and AI practitioners engage with range from the theoretical - understanding data patterns, predictive modelling, machine learning theory, and ethical AI - to the practical - designing scalable data solutions, building responsible AI applications, and deploying intelligent systems that are robust, interpretable, and socially accountable.
The Major in Data Science and Artificial Intelligence offers both breadth and depth in data-driven and AI-driven technologies. Students engage in rigorous coursework well integrated with hands-on learning through projects, project-based courses, internships, and an Undergraduate Thesis/Capstone Project. The curriculum provides a solid foundation in core areas of data science, machine learning, artificial intelligence, and essential computational, statistical, and mathematical skills.
A distinct feature of this Major is the opportunity for students to pursue focused specialisations. After completing the foundational coursework in the first five semesters, students may specialise in either Data Science or Artificial Intelligence to gain deeper expertise aligned with their interests and career aspirations. Students who prefer a more integrative perspective can continue in the default Data Science and Artificial Intelligence track, which offers a balanced blend of data science and AI competencies.
This structure ensures that graduates of the Data Science and Artificial Intelligence Major are equipped with versatile knowledge and skills to contribute to a wide range of industries and research domains, prepared to advance technological frontiers and address societal challenges responsibly and ethically.
This major seeks to provide students:
Students completing the Major in Data Science and Artificial Intelligence will be able to:
| Offered by | School of Arts and Sciences |
| Programme | Bachelor of Science (Honours) |
| Degree | Bachelor of Science (Honours) |
| Minimum Programme Credits | 167 |
| Minimum Major Credits | 80 |
| Credits | |
|---|---|
| Foundation Programme (Three Studios) | 9 |
| First Year Interdisciplinary Seminar on Critical Thinking and Writing | 3 |
Note: If a student’s performance in the First Year Interdisciplinary Seminar Course is unsatisfactory, they will have to retake the course during a specified time window.
| Credits | |
|---|---|
| Humanities and Languages ETH201 Ethics |
3 |
| Social Sciences ECO100 Microeconomics |
3 |
| Biological and Life Sciences | 3 |
| Mathematical and Physical Sciences CSD105 Introduction to Data Science |
3 |
| Performing and Visual Arts | 3 |
| ENR106 Introduction to Programming | 3 |
| GER Elective 1 (Course outside the Major) |
3 |
| GER Elective 2 (Course outside the Major) |
3 |
| COM101 Effective Reading and Comprehension Skills | 3 |
| Sports and Wellness* | 3 |
Notes:
|
Major Requirements |
Credits |
|---|---|
| Major Core | 64 |
| MAT101 Discrete Mathematics | 3 |
| MAT248 Applied Linear Algebra | 4 |
| MAT281 Multivariate Calculus | 4 |
| STA102 Probability and Random Variables | 4 |
| STA202 Mathematical Statistics | 4 |
| STA306 Statistical Computing | 3 |
| STA3xx Statistical Modelling and Inference | 4 |
| CSC210 Introduction to Data Structures and Algorithms | 4 |
| CSC231 Introduction to Artificial Intelligence and Machine Learning | 4 |
| CSE250 Database Management Systems | 3 |
| CSC3xx Implementing AI and ML | 4 |
| CSC3xx Machine Learning | 3 |
| CSC3xx Machine Learning Optimisation | 3 |
| CSC3xx Data Warehouse and Data Mining | 4 |
| CSC3xx Neural Networks | 3 |
| CSC3xx Natural Language Processing | 3 |
| CSC3xx Feature Engineering and Representation Learning | 4 |
| CSC4xx Deep Learning | 3 |
| Major Electives | 16 |
| MAT305 Mathematical Modelling | |
| MAT312 Abstract Algebra | |
| MAT316 Introduction to Analytic Number Theory | |
| MAT325 Foundations of Algebraic Graph Theory | |
| MAT334 Introductory Real Analysis | |
| MAT374 Introduction to Topology | |
| MAT386 Differential Geometry of Manifolds | |
| STA310 Bayesian Data Analysis | |
| STA300 Population Genetics | |
| STA355 Stochastic Processes | |
| STA340 Computational Epidemiology | |
| ECO521 Time Series Econometrics | |
| CSE301 Design and Analysis of Algorithms | |
| CSC4xx Data Visualisation | |
| CSE521 Big Data Analytics | |
| CSE540 Cloud Computing | |
| CSC4xx AI for Cybersecurity and Privacy | |
| CSC4xx Generative AI and Large Language Models | |
| CSC4xx Reinforcement Learning | |
| CSC5xx Graph Machine Learning | |
| CSC5xx Edge AI and Embedded Systems | |
| CSC530 Introduction to Prompt Engineering | |
| CSC531 Multi-agentic AI Systems | |
| CSE641 Computer Vision: Modern methods and applications | |
| Xxxnnn Data Science and AI in Public Health | |
| Xxxnnn Data Science and AI in Biology | |
| Xxxnnn Data Science and AI in Social Systems & Policy | |
| Xxxnnn Data Science and AI in Performing and Visual Arts | |
| Xxxnnn Data Science and AI in Education | |
| Xxxnnn Data Science and AI in Finance |
Note: The four credit courses include one credit of tutorial.
University recognises the evolving expectations of employers, who seek adaptable, tech-savvy, and well-rounded individuals with strong communication, problem-solving, and teamwork abilities. Accordingly, the university integrates structured skill courses delivered through both internal and external platforms to prepare students as “T-shaped” professionals capable of applying knowledge effectively in diverse professional contexts. Skill courses are designed and offered with an aim to develop applied, demonstrable capabilities aligned with career aspirations and job roles.
Note: Courses to be taken from the existing list or approved On-campus courses and MOOCs: See document Mapping Skills to Jobs.
Free electives let students pursue any particular area in more depth, develop alternate areas of expertise, or learn more about many other interests. They provide flexibility for customising education at the University. It can take the following form:
Notes
All graduating students must have completed an Undergraduate Thesis or a Capstone Project. Students can approach a faculty member within the University to serve as a mentor for their undergraduate thesis. Students with a CGPA below the cut-off must complete a capstone project. These projects involve interdisciplinary groups (four to six students) collaborating to solve real-world problems that require expertise from multiple fields. They identify a problem and seek guidance from a faculty mentor to formulate a capstone project, which can be done across two semesters (six credits each semester or three + nine credits) or in a single semester.
A summer internship offers invaluable professional experience and the chance to develop specific, employable skills. It provides an excellent opportunity to explore potential career paths post graduation or discover new graduate programme interests. This experience connects with industry, often serving as a direct stepping stone to a job offer. Most of our programmes include a mandatory internship requirement.
It's advised that you start thinking about internships early in the monsoon semester. You may seek advice from the Career Development Centre.
All students will complete 45 hours of community engagement to develop a sense of social responsibility, enhance problem-solving skills, and understand their role as engaged members of society. They must undergo a mandatory course, VOL101 Engagement with Society, which is a graduation requirement. Students are advised to complete the course during the first two years at the University. Volunteer work could include teaching underprivileged children, participating in adult literacy programmes, distributing blankets or other items, working with old age homes and orphanages, providing support and encouragement to patients at hospitals, cleaning heritage sites, engaging with workers on campus, operating a food bank, engaging with the police, municipal government, zoo, sanitation services, etc.