Master of Science in Data Science

Year of Entry


Application Deadline

Application Closed

Mode of Study


Mode of Funding


Indicative Intake Target


Minimum No. of Credits Required


Class Schedule

Weekday evening
(Evening classes normally start at 7:00 p.m.)

Normal Study Period

Full-time: 1 year;
Part-time: 2 years;

Maximum Study Period

Full-time: 2.5 years;
Part-time/Combined mode: 5 years;

Mode of Processing

Applications are processed on a rolling basis. Review of applications will start before the deadline and continue until all places are filled. Early applications are therefore strongly encouraged.
Programme Leader
Dr TAN Matthias Hwai-yong
BEng(UTM), MEng(NUS), PhD(Georgia Tech)
General Enquiries
+852 3442 7887
Programme Aims and Objectives

The programme aims to produce data-analytic and business-aware graduates to meet the growing demand for high-level data science skills and to prepare graduates to apply data science techniques to knowledge discovery and dissemination in organizational decision-making. It is also intended to help established data analytic professionals upgrade their technical management and development skills and to provide a solid path for students from diverse fields to rapidly transition to data science careers.

Programme Intended Learning Outcomes (PILOs):

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

  1. Apply knowledge of science and engineering appropriate to the data science discipline
  2. Understand theoretical foundation of contemporary techniques and apply them for managing, mining and analyzing data across multiple disciplines
  3. Comprehend computational tools and use data-driven thinking to discover new knowledge and to solve real-world problems with complex structures
  4. Recognize the need for and engage in continuous learning about emerging and innovative data science techniques and ideas
  5. Communicate ideas and findings in written, oral and visual forms and work in a diverse team environment
Entrance Requirements

Applicant must be a degree holder in Engineering, Science or other relevant disciplines, or its equivalent

Non-local candidates from an institution where medium of instruction is not English should fulfill one of the following English proficiency requirements.

  • a score of 550 (paper-based test) or 59 (revised paper-delivered test) or 79 (Internet-based test) in the Test of English as a Foreign Language (TOEFL)@#; or
  • an overall band score of 6.5 in International English Language Testing System (IELTS)@; or
  • a minimum score of 450 in band 6 in the Chinese mainland’s College English Test (CET6); or
  • other equivalent qualifications

@TOEFL and IELTS scores are considered valid for two years. Applicants are required to provide their English test results obtained within the two years preceding the commencement of the University's application period.

#Applicants are required to arrange for the Educational Testing Service (ETS) to send their TOEFL results directly to the University. The TOEFL institution code for CityU is 3401.

Fellowships Scheme

Fellowship awards are available for local students admitted to this programme under the Fellowships Scheme supported by the HKSAR Government. This programme in the priority area of “STEM” is one of the targeted programmes listed under the Fellowships Scheme with 9 fellowship awards. Local students admitted to the programme in full-time, part-time or combined study mode will be invited to submit applications for the fellowships. 

Course Description

Core Courses (15 credit units)

  • Exploratory Data Analysis and Visualization
  • Research Projects for Data Science
  • Statistical Machine Learning I
  • Statistical Machine Learning II
  • Storing and Retrieving Data

Electives (15 credit units)

  • Bayesian Data Analysis
  • Data Analytics for Smart Cities
  • Dynamic Programming and Reinforcement Learning
  • Experimental Design and Regression
  • Information Security for eCommerce
  • Machine Learning: Principles and Practice
  • Machine Learning at Scale
  • Natural Language Processing
  • Networked Life and Data Science
  • Optimization for Data Science
  • Privacy-enhancing Technologies
  • Social Foundations of Data Science
  • Statistical Methods for Categorical Data Analysis 
  • Time Series and Panel Data
  • Topics in Financial Engineering and Technology

Remarks: Course offering is subject to sufficient enrolment.


Our MSDS programme offers comprehensive and rigorous training for students seeking a profession in data science. Our graduates have embarked on exciting and highly rewarding careers such as data scientists and data analysts in finance, technology, and other industries, professional consultants, data engineers, AI engineers, managers, and other data professional positions in well-known corporations and companies that include members of the Big Four accounting firms, tech giants, retail giants, and international banks. Some of our graduates are also furthering their studies in PhD programmes at world renowned universities.


The full MSc degree award requires 30 credit units, with the completion of taught courses only, or taught courses plus the dissertation project.

Useful Links
† Combined mode: Local students taking programmes in combined mode can attend full-time (12-18 credit units per semester) or part-time (no more than 11 credit units per semester) study in different semesters without seeking approval from the University. For non-local students, they will be admitted to these programmes for either full-time or part-time studies. Non-local students must maintain the required credit load for their full-time or part-time studies and any changes will require approval from the University.