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 analyzing 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
- Recognize 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
|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|
|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 Panel Data||3|
|SDSC6013||Topics in Financial Engineering and Technology||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.
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 TOEFL score of 550 (paper-based test) or 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
HK$8,700 per credit (for local and non-local students admitted in 2020/21)
Credit Units Required for Graduation: 30
|Normal Period||Maximum Period|
|- 1 year (full-time)||- 2.5 years (full-time)|
|- 2 years (part-time/combined)||- 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.
A strong demand of the data scientists and analysts has been recently observed in the worldwide job market. This programme aims at producing analytic and business-aware graduates to meet the growing demand by equipping them with big data analytics skills and nurturing their capability in applying data science techniques to address emerging complicated real-life problems. Upon successful completion of this programme, the student should be able to:
- Apply data processing skills to handle data of various formats and sizes.
- Conduct comprehensive data analytics with integrating techniques from various disciplines for knowledge discovery and dissemination in organizational decision-making.
- Utilize a variety of data visualization techniques to interpret data analytics results.
- Demonstrate strong quantitative capabilities as well as communication skills.
- Develop descriptive, prescriptive and predictive analytics solutions to tackle emerging challenges in contemporary problems.
Last modified on 21 February, 2020