Minor in Data Science

Minor
Data Science
Offering Academic Unit
Department of Data Science
Exclusive Majors
(Students who study the following majors are not allowed to choose this minor)
Data Science or Data and Systems Engineering
Minor Leader
Professor Yu YANG

Note: The following curriculum information is subject to periodic review and changes.

Aims of Minor

This minor aims to provide students with adequate education and training in data science for understanding the methodologies and applying techniques to various disciplines.

Intended Learning Outcomes of Minor (MINILOs)

Upon successful completion of this minor, students should be able to:

  1. Understand and recognise the fundamental topics of data sciences such as data mining, statistical learning and machine learning.
  2. Build skills and techniques of organising and analysing big data with a level of flexibility for different applications.
  3. Apply the data-driven modeling and learning algorithms to quantitatively solve practical problems in social, scientific, engineering and business applications.

Minor Requirements (16 credit units)

1. Core Courses (10 credit units, or 7 credit units for those exempted from taking SDSC2102)
Course Code Course Title Credit Units
DSC2001 Python for Data Science 4
DSC2102 Statistical Methods and Data Analysis* 3
DSC3006 Fundamentals of Machine Learning I 3

Remarks: *Students who have completed MS2602 and obtained a grade “B-” or above will be exempted from taking SDSC2102. These students will be required to take any course from the semi-core or elective course list to make up the 3 missing credit units.

2. Semi-core Courses (at least 3 credit units)
Course Code Course Title Credit Units
DSC2002 Convex Optimization 3
DSC2004 / GE2343 Data Visualization 3
DSC3002 Data Mining 3
DSC3007 Advanced Statistics 3
DSC4016 Fundamentals of Machine Learning II 3
3. Electives (at least 3 credit units)
Course Code Course Title Credit Units
GE1356 Introduction to Data Science 3
DSC2003 Human Contexts and Ethics in Data Science 3
DSC2005 Introduction to Computational Social Science 3
DSC3001 Big Data: The Arts and Science of Scaling 3
DSC3004 Computational Optimization 3
DSC3005 Computational Statistics 3
DSC3010 Digital Trace Analytics 3
DSC3011 Social Data Processing and Modelling 3
DSC3013 Introduction to Social Media Analytics 3
DSC3015 Knowledge Graph and Cognitive Computing 3
DSC3016 Social Network Analysis 3
DSC3017 Game Theory and Its Application 3
DSC3027 Smart Logistics and Transportation 3
DSC3105 Bayesian Analysis 3
DSC4001 Foundation of Reinforcement Learning 3
DSC4008 Deep Learning 3
DSC4009 Data Intelligence in Action 3
DSC4011 Experimental Research for Social Media 3
DSC4018 AI in Systematic Trading 3
DSC4019 Stochastic Processes and Applications 3
DSC4110 Statistical Design and Analysis of Experiments 3

Note:

  1. A student is required to obtain an average GPA of 2.0 or above for the courses from the Core, Semi-core and Elective course lists stated above, and Grade C- or above in all courses for the award of Minor in Data Science.
  2. A student who intends to take the above minor should seek approval from his/her home department and the Department of Data Science.
  3. Students who wish to take a Minor in Data Science should take note that they are required to fulfill the prerequisites of the required courses.

Application

Students are required to submit their declaration of minor request through AIMS under Course Registration. Information on the key dates, process and steps for the Declaration of Minors are available in the website of the Academic Regulations and Records Office.

If you only wish to enrol in specific DS courses to enrich your studies, you may try to add the courses on AIMS. Please do pay attention that approval by course leaders is subject to fulfilment of pre-requisites.

Last modified on 30 July, 2026