Master of Data Science (MDASC) is a taught master programme jointly offered by Department of Statistics and Actuarial Science (host) and Department of Computer Science.
The programme’s interdisciplinarity promotes the applications of computer technology, operational research, statistical modelling, and simulation for problem-solving and decision-making in organisations and enterprises within the private and public sectors.
The curriculum of the MDASC programme adopts a well-balanced and comprehensive pedagogy of both statistical and computational concepts and methodologies, underpinning applications that are not limited to business or a single field alone.
The programme is ideal for
those interested in acquiring analytical skills in areas ranging from statistics to computational analytics, and
those who wish to pursue further studies in data science after having completed undergraduate studies in areas such as science, engineering, medical sciences, social sciences, information systems, computing and data analytics.
Solid foundation in statistical and computational analyses
Electives cover a broad range of contemporary topics about Computer Science and Statistics
Hands-on applications of methodologies with software
Capstone project with real-life applications
Course Highlight
The core courses of the proposed MDASC programme mainly focus on both predictive and prescriptive concepts and methodologies with an effort to equip students with a solid foundation in statistical and computational analyses, e.g.
Statistical modelling Computational intelligence
Time series forecasting Deep learning
The electives cover a broad range of contemporary topics and provide students with solid training in diverse and applied techniques used in data science, including but not limited to
Financial data analysis Blockchain data analytics
Multimedia technologies Natural language processing
Study Period
The normative study period of full-time students is 1.5 years, while that of part-time students is 2.5 years.
Programme Structure
The curriculum is the same for both full-time and part-time study mode. You may refer to the 2024 syllabuses and regulations (subject to approval). The curriculum is extracted below.
Compulsory Courses (24 credits)
COMP7404
Computational intelligence and machine learning (6 credits)
DASC7011
Statistical inference for data science (6 credits)
DASC7104
Advanced database systems (6 credits)
STAT7102
Advanced statistical modelling (6 credits)
Disciplinary Electives (36 credits)*
List A (at least 12 credits)
COMP7107
Management of complex data types (6 credits)
COMP7305
Cluster and cloud computing (6 credits)
COMP7409
Machine learning in trading and finance (6 credits)
COMP7503
Multimedia technologies (6 credits)
COMP7506
Smart phone apps development (6 credits)
COMP7507
Visualization and visual analytics (6 credits)
COMP7906
Introduction to cyber security (6 credits)
DASC7606
Deep learning (6 credits)
FITE7410
Financial fraud analytics (6 credits)
ICOM6044
Data science for business (6 credits)
List B (at least 12 credits)
STAT6008
Advanced statistical inference (6 credits)
STAT6013
Financial data analysis (6 credits)
STAT6015
Advanced quantitative risk management (6 credits)
STAT6016
Spatial data analysis (6 credits)
STAT6019
Current topics in statistics (6 credits)
STAT7008
Programming for data science (6 credits)
STAT8003
Time series forecasting (6 credits)
STAT8017
Data mining techniques (6 credits)
STAT8019
Marketing analytics (6 credits)
STAT8300
Career development and communication workshop (Non-credit-bearing)
STAT8306
Statistical methods for network data (3 credits)
STAT8307
Natural language processing and text analytics (3 credits)
STAT8308
Blockchain data analytics (3 credits)
Capstone requirement (12 credits)
DASC7600
Data science project (12 credits)
DASC8088
Data science practicum (6 credits) + a 6-credit course (from List A or List B)
Remarks:
The programme structure will be reviewed from time to time and is subject to change.
*Students who have completed the same courses in their previous studies in HKU, e.g. Master of Statistics or Master of Science in Computer Science may, on production of relevant transcripts, be permitted to select up to 36 credits of disciplinary electives from either List A or List B above if they are not able to find any untaken options from either of the lists of disciplinary electives.
Programme Fees (subject to approval)
This is a self-funded programme. The full tuition fee is HK$324,000 (for FT & PT students admitted in September 2025). The full fee would normally be paid by full-time students in 3 installments, and part-time students in 5 installments.
Master of Data Science (MDASC) is one of the Programmes sponsored by University Grants Committee (UGC) for Targeted Taught Postgraduate Programmes Fellowships Scheme. Local full-time or part-time offer recipients who will be students of MDASC in the academic year 2024-25 are eligible for application, and applicants are required to prepare a proposal on how they can contribute to the priority areas (i.e. Business and STEM) of Hong Kong after completing MDASC. More application details will be released to the eligible candidates by email in due course.
Successful applicants will each receive an award of HK$120,000. Please note that if the awardees cannot complete MDASC for any reasons or are not able to obtain satisfactory results, they will be required to refund the full amount of the award.
Master of Data Science (MDASC) is one of the Programmes sponsored by University Grants Committee (UGC) for