Sidebar    Data Science and Analytics

Data science is an emerging field of study that involves statistical and computational principles, methods and systems for extracting and structuring knowledge from data. On a daily basis, large data sets are routinely generated by activities in the sciences, administration, leisure and commerce. Data scientists are constantly seeking patterns and predicting outcomes from these vast collections of data.

The four-year direct Honours programme in Data Science and Analytics (DSA) is designed to prepare graduates who are ready to acquire, manage and explore data that will inspire changes around the world. Singapore is a financial hub, with key industries focusing on biomedical sciences, health care, manufacturing, e-commerce and sustainable energy, among others. The DSA programme will equip its graduates with the skills to contribute to the activities of these industries. They will be able to handle problems like uncovering hidden stock market indicators, extracting information from medical images, predicting consumer behaviour and a host of similar interesting questions.

Programme Structure and Curriculum Rationale

The DSA programme is jointly offered by the Department of Mathematics and the Department of Statistics and Applied Probability in the Faculty of Science, with the collaboration of the School of Computing.

Students will read modules in Mathematics, Statistics and Computer Science, and be exposed to the interplay between these three key areas in the practice of data science. In their third and fourth years of study, students will also delve deep into subject matters such as computation and optimisation, computer algorithms, database and data processing, data mining and machine learning, and high-dimensional statistics. Students will also undertake an industry-driven capstone project module, where they will work with real-life data, providing them with an opportunity to tackle real-life issues and problems in a workplace environment.

Co-Operative Education

The NUS Co-Operative (Co-Op) Education Programme formally integrates academic studies with relevant work experience, where students complete multiple internship stints alternating with regular academic semesters over their candidature at NUS thus forming an integral part of the student’s learning experience.

Students in the DSA programme have the option to participate in co-op education which comprises the following study/internship sequence:

Semester 1 Semester 2 Special Term
Year 1 Study Study Study
Year 2 Study Study Internship (full time)
Year 3 Study & Internship (full time) Internship (full time) Internship (full time)
Year 4 Study & Internship (part time) Study

The first three internship segments ride on the Undergraduate Professional Internship Programme (UPIP) of the Faculty of Science. The last two internship segments take the form of an Honours-level project (DSA4299).

Career Prospects

As the need for extensive data collection, processing and analyses increases across various sectors, DSA graduates can expect to build a career as data science professionals in both public and private firms, in industries ranging from technology to infocomm, transportation, telecommunications, e-commerce, etc.

Graduation Requirements

To be awarded a B.Sc. or B.Sc. (Hons.) with a primary major in Data Science and Analytics, candidates must satisfy the following:

Module Level Major Requirements Cumulative Major MCs

Level 1000


(16 MCs)


– CS1010/CS1010S/CS1010X Programming Methodology

– DSA1101 Introduction to Data Science

– MA1101R Linear Algebra I

– MA1102R Calculus

Level 2000

(24 MCs)


– CS2040 Data Structures and Algorithms

– DSA2101 Essential Data Analytics Tools: Data Visualisation

– DSA2102 Essential Data Analytics Tools: Numerical


– MA2311 Techniques in Advanced Calculus or MA2104 Multivariable Calculus

– ST2131/MA2216 Probability

– ST2132 Mathematical Statistics

Levels 3000

and 4000

(56 MCs)


– CS3244 Machine Learning

– DSA3101 Data Science in Practice

– DSA3102 Essential Data Analytics Tools: Convex Optimisation

– ST3131 Regression Analysis

– DSA4199 Honours Project in Data Science or

DSA4299 Applied Project in Data Science

– Six additional modules from List A and List B subject to the

following restrictions:

+ There must be at least two modules each from List A and

from List B1/ List B2

+ There must be at least four modules at level 4000


List A — DSA modules

DSA4211  High-Dimensional Statistical Analysis

DSA4212  Optimisation for Large-Scale Data-Driven Inference

List B1 — DSA-recognised modules (no hidden pre-requisites)

MA3236   Nonlinear Programming

MA3252   Linear and Network Optimisation

ST3232    Design and Analysis of Experiments

ST3233    Applied Time Series Analysis

ST3239    Survey Methodology

ST3240    Multivariate Statistical Analysis

ST3247    Simulation

ST3248    Statistical Learning I

ST4231    Computer Intensive Statistical Methods

ST4234    Bayesian Statistics

ST4248    Statistical Learning II

List B2 — DSA-recognised modules (with hidden pre-requisites) *

CS3210   Parallel Computing

CS3223   Database Systems Implementation

CS3230   Design and Analysis of Algorithms

CS4224   Distributed Databases

CS4225   Massive Data Processing Techniques in Data Science

CS4231   Parallel and Distributed Algorithms

CS4234   Optimisation Algorithms

MA4230   Matrix Computation

MA4270   Data Modelling and Computation

* Note: For List B2, i.e., the DSA-recognised modules with hidden pre-requisites, DSA students who wish to read these modules will be provided with academic advice by the Faculty/Department on their study plans where necessary, as such students would have to read ‘additional’ pre-requisite modules.

Summary of Requirements B.Sc. (Hons.)
University Requirements   20 MCs
Faculty Requirements   8 MCs*
Major Requirements 96 MCs
Unrestricted Elective Modules   36 MCs
Total 160 MCs

* 8 MCs of Faculty requirements are fulfilled through the reading of a CS-coded module and a ST/MA-coded module within the DSA curriculum.

Students are required to fulfill the remaining 8 MCs of Faculty requirements from any two of the following subject groups: Chemical Sciences, Life Sciences, Physical Sciences or Multidisciplinary & Interdisciplinary Sciences; but not from the following subject groups: Computing Sciences and Mathematical & Statistical Sciences.