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  • Major in Data Science & Analytics

Major in Data Science & Applied AI

Bachelor of Science with Honours in Data Science and Applied AI 
(applicable only for Cohorts 2026/27 and after)


The Data Science and Applied AI (DSAAI) programme at NUS is an interdisciplinary four-year direct Honours undergraduate programme designed to address the demand for data science professionals both locally and globally. Rooted in a strong interdisciplinary foundation of Artificial Intelligence, Computer Science, Mathematics, and Statistics, the programme aims to produce graduates who can extract, analyse, and interpret data to generate valuable insights across a wide range of domains.

The learning outcomes of the DSAAI programme are: 

  • To comprehend the conceptual and methodological foundations of analytical methods and techniques for data science, drawn from the broad disciplines of computing, mathematics and statistics.

     

  • To appreciate and understand current data-scientific problems in engineering and sciences, government and public service, and industry at large, and be able to identify, formulate and resolve practically relevant scientific questions and issues in these sectors and domains using appropriately curated data.

  • To apply, develop and implement appropriate analytic tools and techniques to resolve complex data-scientific problems in various sectors and domains, and be able to communicate findings and insights gained clearly using appropriate visualisation tools.

  • To understand the conceptual foundations, capabilities, and limitations of artificial intelligence and to integrate AI methods appropriately into data science workflows, selecting and evaluating tools with rigour and critical judgement.

  • To exercise responsible and discerning use of AI models: selecting appropriate models for the task, critically evaluating their outputs, recognising the limits of automated reasoning, maintaining accountability for analytical decisions, and upholding ethical standards of fairness, transparency, and societal responsibility.

  • To cultivate in students the practice of independent and peer learning so as to prepare them for various data science roles, and for the continual need to re-skill in the fast-changing data science landscape.
 

The DSAAI programme supersedes the Major in Data Science (DSA) and Analytics which is applicable only for Cohorts AY2019/20 to AY2025/26.

 

The learning outcomes of the DSA programme are:

  • To comprehend the conceptual and methodological foundations of analytical methods and techniques for data science, drawn from the broad disciplines of computing, mathematics, and statistics

     

  • To appreciate and understand current data-scientific problems in engineering and sciences, government and public service, and industry at large, and be able to identify, formulate and resolve practically relevant scientific questions and issues in these sectors and domains using appropriately curated data

     

  • To apply, or develop and implement, appropriate analytic tools and techniques to resolve complex data-scientific problems in various sectors and domains, and be able to communicate findings and insights gained clearly using appropriate visualisation tools

     

  • To cultivate in the students the practice of independent and peer learning so as to prepare them to function effectively in diverse careers as data science professionals

     

For both the DSAAI and DSA programmes, professors work with industry partners to develop, incorporate and infuse applications into the industry-linked capstone and elective courses and the Honours project in the programme. This ensures graduates receive a well-rounded education and gain a competitive edge in the data science sector. They are equipped with in-depth practical experience through real-world business problem-solving case studies across diverse domains, including healthcare, transportation, finance, and public services.

Last updated: 3 July 2026

 
Students have the option to pursue specialisations in statistical methodology or operations research. Statistical methodology focuses on the development and application of advanced statistical techniques for data analysis and inference, while operations research emphasises optimisation techniques to support decision-making in areas such as logistics, resource allocation and systems management.
 
You will fulfill the specialisation requirements by passing at least five elective courses from a prescribed list. Up to 8 units (40% of the unit requirements for a Specialisation) may be double counted and used to meet the Specialisation requirement and another requirement (Primary Major, 2nd Major, Minor, Common Curriculum, etc.). Strictly no triple-counting of courses is allowed.
 

 

To be admitted to read a Major in Data Science and Applied AI (DSAAI), you will need to apply for admission to the Faculty of Science. After you are successfully admitted, you will be able to declare DSAAI as your major.

 

To be able to read some of the first-year core courses, you will need a very good H2 pass or equivalent in Mathematics/Further Mathematics. If you do not have this background, you are required to read the bridging course, MA1301 or MA1301X, first.

 

 

The requirements and sample study plan of the programme can be found here.

 

 

The Data Analytics and Consulting Centre is a consulting unit closely linked with the DSA programme. Interested students in the programme have the opportunities to assist in the Centre’s consulting services to the industry, thereby allowing them to gain practical experience in formulating data-driven solutions for real-world business problems in a wide spectrum of our corporate partners, from small and medium enterprises to multinational corporations.

 

The Centre’s Director source for industry projects and work with a team of professors to supervise students involved in the projects. The students prepare the data and perform data analytics on it. This way the students gain exposure to different business problems across different industries, better understand the challenges that the industry has and learn how industry overcomes these challenges using data analytics.

 

The Centre’s Director also ensure the security and confidentiality of data and monitor progress towards meeting the goals of the project. The team of professors attached to the project aids in the supervision of students, and in the interactions with clients to understand the problems they want to solve and the type of solutions are feasible for them to implement. The students are involved in the brainstorming of solutions, in the initial data massaging, in the writing of program codes and in the presentation of solutions to clients.

 

The benefits to students involved in the project are first that they learn how to treat data properly to ensure data security and confidentiality. They get to apply their statistical skills learned in classroom teaching and acquire new ones. They practice their programming knowledge in Python and R and learn how to access and make use of coding platforms. They develop their creative thinking by solving problems not encountered in structured classroom teaching. They boost their presentation skills during their presentations to clients. And finally, they have access to additional faculty mentoring.

 

 

 

The Co-operative (Co-op) Education Programme at NUS formally integrates academic studies with relevant work experience, where students complete multiple internship stints alternating with regular academic semesters over their four-year candidature at NUS. Co-operative education is optional.

 

Starting from AY2021/2022, DSA and DSAAI students who choose to undertake the Co-op pathway will spend four semesters/terms (15–16 months) at the workplace with reputable employers. This will equip them with the skills, knowledge and expertise that enhance their employability after graduation.

 

We have entered into partnerships with several companies and organisations to offer internships for the DSA Co-op programme. These companies and organisations include government agencies, telecommunications companies, management consulting, defence science agencies, banking/financial institutions, port operators, multi-sector corporations, etc.

 

The study/internship sequence for DSA and DSAAI students opting for the Co-op pathway is currently under revision and will be made available here after it has been approved.

 

  • Major in Data Science & Applied AI