Bachelor of Science (Honours) with Major in Data Science and Economics
The Data Science and Economics (DSE) cross-disciplinary programme (XDP) aims to produce students who have strong foundation knowledge in data science and economics as well as hands-on experience with empirical analysis of economic data, to analyse and interpret the local and global impact of data on individuals, organisation, society and the global economic ecosystem.
The student learning outcomes are:
The DSE curriculum incorporates inter-disciplinary learning from data science and economics, with foundations in computer science, mathematics and statistics. In addition to higher-level courses that integrate knowledge and concepts from lower-level core foundational courses, students also read courses related to the application of data science and analytics to the financial market, labour market, and other applied economic issues in education, health, housing and industrial organisation.
The programme also provides opportunities for experiential and self-directed learning. In the industry-linked integrated courses (on digital currencies, FinTech and the digital economy) and the capstone project (which students complete in their final year of study), students learn from data science professionals and economists both within and beyond the formal classroom setting. Whereas the DSE integrated courses are generally taught in a formal classroom setting with industry participation, students may work on their capstone projects in certain partner institutions or companies. Interaction with data science professionals allows the students to hone their ability to ask the right questions and formulate problems, be resourceful and enterprising in their approach to data collection and analysis to problem-solve and yield insights, and sharpen their communication skills. Opportunities to work in a data science team inculcate in the students the value of being constructive and responsible members of the community.
Students in this DSE programme can choose to participate in student exchange programmes with overseas partner universities as part of their global education. Such participation immerses students in new learning environments to develop their sense of global citizenship and outlook, as well as their own unique Singapore and Asian identities in the international arena. Further experiential learning can be achieved through participation in internships with local or overseas institutions or companies.
The Data Science and Economics Cross Disciplinary Programme is jointly offered by the Department of Statistics and Data Science, Department of Mathematics and Department of Economics. This programme is hosted by the Department of Mathematics.
NUS will adopt three new academic terminologies from 1 August 2023 – “Module” will be renamed “Course”, “Modular Credit (MC)” will be renamed “Unit”, and ‘Cumulative Average Point (CAP)” will be renamed “Grade Point Average (GPA)”. For more information, please click here (undergraduate) or here (graduate).
Updated on 18 October 2024
Admission Requirements and Procedures
You will need a very good H2 pass (or equivalent) in Mathematics. The admission requirements for the respective applicant groups are as follows:
Admission to DSE is via direct admissions. Please refer to the NUS Office of Admissions website at this URL for more information on the application procedures: https://www.nus.edu.sg/oam/apply-to-nus.
Programme Requirements and Sample Study Plan
The requirements of the DSE programme can be downloaded here.