Frequently Asked Questions

For Mathematics, you would study topics such as algebra, analysis, geometry and topology which focus more on foundations, theory, and proving techniques. The latter is particularly helpful in the training of a rigourous mind.

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For Applied Mathematics, you would focus more on mathematics that deals with applications, including modeling and algorithms. We offer a variety of subjects including financial mathematics, coding theory, numerical analysis and optimization.

Mathematics deals with numbers, discrete and continuous quantities, geometric figures and images, etc in all their generalities. It ranges from the most abstract and fundamental theories in pure mathematics to the most concrete methods and practical algorithms in applied mathematics. An undergraduate math major paves the way for a variety of quantitative disciplines at the postgraduate level, including statistics, economics and management science.

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On the other hand, Statistics deals with the collection and analysis of data and information in surveys, experiments, databases, etc in order to reach conclusions or decide on a suitable course of action.

With the introduction of CHS, the Major in Applied Mathematics has been merged with the Major in Mathematics. The new Major in Mathematics now has a more flexible graduation requirement. Students will have to option to focus on pure mathematics or applied mathematics topics. A student who wants to focus on applied mathematics topics can do so and fulfil the graduation requirements at the same time.

While both the majors in Mathematics (MA) and Quantitative Finance (QF) target students who are mathematically inclined, the QF major is specifically designed for students who want to pursue a career in the finance industry. The MA curriculum contains core courses in various areas of mathematics while the QF curriculum contains core courses in foundation mathematics and various areas of quantitative finance.

The topics would be mostly new and are naturally at a higher level than what is required in Junior Colleges. You should also expect some changes in the emphasis of and the way you deal with materials. You would need to develop analytical skills and learn more on fundamental ideas, proving techniques as well as application of mathematical theories.

Students who are broad-minded, good in mathematics and are interested in a career in banking and finance.

We expect students to have good opportunities in the finance and banking sector. Career prospects would be better as Singapore becomes the financial and IT centres in Asia and the demand for graduates in quantitative finance background increases.

Your job prospects and salary structure would be determined by the market. If you are considering reading other degree programmes, you should consult the relevant academic advisors and decide for yourself, a path that best suits your interest and needs.

You’ll need to have a good H2 pass (or equivalent) in Mathematics/Further Mathematics to start reading the Year 1 mathematics courses. Candidates without these prerequisites are required to read the bridging course in Mathematics (MA1301 or MA1301X).

DSE is an XDP (Cross Disciplinary Program) where the data science methods and techniques are customised for economic problems and taught in an integrated way. Compared to DSA, the program has clear objectives and domain areas of expertise, e.g. data science applications in FinTech and other areas of economics, including the digital economy. In contrast, DSA focus on data science methods that have no specified implementation environments. The integration between quantitative techniques and domain knowledge is important for tackling economic questions appropriately.

As mentioned above, courses in DSE are customised for economic problems and taught in an integrated way. In other words, the DSE program is more than 1 (Econ) +1 (Data Science). Several new courses have been carefully designed for the program by combining economic, mathematical, statistical and computing methods to solve specific modern digital economy problems.

In the DSE program, new courses have been carefully designed for the program by combining economic, mathematical, statistical and computing methods to solve specific modern digital economy problems.  Compared to business analytics, the training in mathematics, statistics and economics is more rigorous, and students are taught not only the “how”, but also the “why”. Our students are trained not only to implement the current methods/tools for solving existing problems, but also to have enough understanding for solving future problems they may face during their careers.

There is some overlap between DSE and QF in terms of data science applications in finance, since finance is an important part of the digital economy, especially in Singapore. Meanwhile, DSE focuses on a broader set of problems, including non-financial applications such as e-commerce, policy making, while QF is solely focused on financial problems, such as portfolio optimization, trading strategies, etc.

There are very broad and exciting career prospects. With the knowledge and skill of economics and appropriate data science methods, we expect our graduates will be particularly successful in e.g. government, financial sector, FinTech, E-commerce, among others. The business world is shifting towards data-driven decision making, and economics is a science about making such decisions. From past experience, economics graduates with strong technical skills went into data related positions in ministries, tech companies such as Shopee and Grab, major Singapore banks, hedge funds, consultancy companies etc. It is also a good platform for students who are considering graduate studies in economics later on (see the next question).

A DSE degree would give students a very good platform to go into Master or PhD degrees in Economics, Finance, Machine Learning or Analytics, both locally and overseas. There are some successful postgraduate programmes in NUS, such as Machine Learning and Data Science, and Quantitative Finance offered in Math, Digital Finance offered in Computer Science and AIDF, and Financial Engineering in Risk Management Institute. Current NUS graduates in economics with technical skills have a strong graduate placement record at the best programs worldwide.

About 100.

Yes. For details, refer to https://www.nus.edu.sg/oam/admissions/first-choice-bonus-points

DSE is a direct admission programme. The IGP for DSE is different from that for Economics or DSA. Detailed information for IGP can be found at: https://nus.edu.sg/oam/admissions/indicative-grade-profile

Please refer to the Data Science and Economics major requirements at:

https://www.stat.nus.edu.sg/wp-content/uploads/sites/8/2024/06/120624-Major-Data-Science-and-Economics-Programme-Requirements-AY21-22-and-after.pdf

For course descriptions, go to https://nusmods.com/courses

There is a specific list of courses that is required for DSE. Please refer to the Data Science and Economics major requirements at:

https://www.stat.nus.edu.sg/wp-content/uploads/sites/8/2024/06/120624-Major-Data-Science-and-Economics-Programme-Requirements-AY21-22-and-after.pdf

DSE is an integrated education program blending relevant skills in data science, economics, mathematics and computing. The course tree of the DSE program is carefully designed, including dedicated new courses that equip students with the necessary and relevant programming skills for tackling real-life problems in economics and finance.

The entry requirements for the program require a good math background, so students should be well equipped to tackle the technical courses. We refer to those courses as challenging (instead of difficult), and they will feel rewarding once students see the application of fundamental methods to the solution of real-life economic problems. 

Yes, DSE students can participate in credit-bearing internships under the

For details, refer to https://www.science.nus.edu.sg/undergraduates/internships/

Yes, students can do that as long as the intended double major or major-minor combination is not prohibited.

Second majors typically consist of 40 units. Up to 16 units of double-counting may be allowed between a second major and the primary major.

Students will be introduced to programming and relevant libraries in Python, R, Stata and some others languages depending on the course.