Master of Science in Data Science (MSDS)
Normal Period of Study
Full-time: 1 year;
Part-time: 2 years
Mode of Study
Mode of Funding
Dr. Matthias Hwai-yong TAN
The programme aims to produce data-analytic 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 organisational decision-making. It is also intended to help data analytic professionals upgrade their technical management and development skills, and to provide a solid path for students from related quantitative fields to rapidly transition to data science careers.
Programme Intended Learning Outcomes (PILOs):
Upon successful completion of this Programme, students should be able to:
- Apply knowledge of science and engineering appropriate to the data science discipline
- Understand theoretical foundation of contemporary techniques and apply them for managing, mining and analysing data across multiple disciplines
- Comprehend computational tools and use data-driven thinking to discover new knowledge and to solve real-world problems with complex structures
- Recognise the need for and engage in continuous learning about emerging and innovative data science techniques and ideas
- Communicate ideas and findings in written, oral and visual forms and work in a diverse team environment
Core Electives (15 credit units)
|Course Code||Course Title||Credit Units|
|SDSC5001||Statistical Machine Learning I||3|
|SDSC5002||Exploratory Data Analysis and Visualization||3|
|SDSC5003||Storing and Retrieving Data||3|
|SDSC6001||Statistical Machine Learning II||3|
|SDSC6002||Research Projects for Data Science||3|
Electives (15 credit units)
|Course Code||Course Title||Credit Units|
|CS5285||Information Security for eCommerce||3|
|CS5487||Machine Learning: Principles and Practice||3|
|CS6493||Natural Language Processing||3|
|SDSC6003||Bayesian Data Analysis||3|
|SDSC6004||Data Analytics for Smart Cities||3|
|SDSC6007||Dynamic Programming and Reinforcement Learning||3|
|SDSC6008||Experimental Design and Regression||3|
|SDSC6009||Machine Learning at Scale||3|
|SDSC6011||Optimization for Data Science||3|
|SDSC6012||Time Series and Recurrent Neural Networks||3|
|SDSC6013||Topics in Financial Engineering and Technology||3|
|SDSC6014||Networked Life and Data Science||3|
|SDSC6015||Stochastic Optimization and Online Learning||3|
|SDSC6016||Predictive Analytics and Financial Applications||3|
|SDSC8008||Data-driven Operations Research||3|
|SDSC8009||Data Mining And Knowledge Discovery||3|
|SDSC8011||Social Foundations of Data Science||3|
|SDSC8013||Statistical Methods for Categorical Data Analysis||3|
|SDSC8014||Online Learning and Optimization||3|
The full MSc degree award requires 30 credit units, with the completion of taught courses only; or taught courses plus the dissertation project.
Remarks: Programme electives will be offered subject to availability of resources and sufficient enrolment.
(Note: The handbooks are updated as at the beginning of the corresponding academic years.)
Applicant must be a degree holder in Engineering, Science or other relevant disciplines, or its equivalent
Non-local candidates from an institution where the medium of instruction is NOT English should fulfil one of the following English proficiency requirements.
- a TOEFL score of 59 (revised paper-delivered test) or 79 (Internet-based test) on 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.
HK$9,200 per credit (for local and non-local students admitted in 2021/22)
HK$8,700 per credit (for local and non-local students admitted in 2020/21)
Credit Units Required for Graduation: 30
|Duration of study:|
|Normal Study Period||Maximum Study Period|
|1 year (Full-time)||2.5 years (Full-time)|
|2 years (Part-time/Combined mode)||5 years (Part-time/Combined mode)|
Fellowship awards are available for local students admitted to this programme under the Fellowships Scheme supported by the HKSAR Government. Local students admitted to the programme in full-time, part-time or combined study mode will be invited to submit applications for the fellowships. Please click to view more details about the Fellowships Scheme.
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.
For application enquiries, please contact the School of Graduate Studies (SGS) at firstname.lastname@example.org
For student visa matters, please contact the Global Engagement Office (GEO) at email@example.com