Programme Information

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.

Its interdisciplinarity promotes the applications of computer technology, operational research, statistical modelling, and simulation to decision-making and problem-solving in all organizations 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.

It is a programme ideal for

  1. those whose interest in high-level analytical skills straddles the disciplinary divide between statistics and computational analytics, and
  2. those who wish to pursue further study in the field of data science after studying science, social sciences, engineering, medical sciences, information systems, computing and data analytics in their undergraduate studies.

Programme Highlights

  • Interdisciplinary and comprehensive curriculum
  • 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 powerful software
  • Capstone project with real-life scenario

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 (36 credits)

COMP7404Computational intelligence and machine learning (6 credits)
DASC7011Statistical inference for data science (6 credits)
DASC7104Advanced database systems (6 credits)
DASC7606Deep learning (6 credits)
STAT7102Advanced statistical modelling (6 credits)
STAT8003Time series forecasting (6 credits)

Disciplinary Electives (24 credits)*

with at least 12 credits from List A and at least 12 credits from List B

List A

COMP7105Advanced topics in data science (6 credits)
COMP7305Cluster and cloud computing (6 credits)
COMP7409Machine learning in trading and finance (6 credits)
COMP7503Multimedia technologies (6 credits)
COMP7506Smart phone apps development (6 credits)
COMP7507Visualization and visual analytics (6 credits)
COMP7906Introduction to cyber security (6 credits)
FITE7410Financial fraud analytics (6 credits)
ICOM6044Data science for business (6 credits)

List B

STAT6008Advanced statistical inference (6 credits)
STAT6013Financial data analysis (6 credits)
STAT6015Advanced quantitative risk management (6 credits)
STAT6016Spatial data analysis (6 credits)
STAT6019Current topics in statistics (6 credits)
STAT7008Programming for data science (6 credits)
STAT8017Data mining techniques (6 credits)
STAT8019Marketing analytics (6 credits)
STAT8306Statistical methods for network data (3 credits)
STAT8307Natural language processing and text analytics (3 credits)
STAT8308Blockchain data analytics (3 credits)

Capstone requirement (12 credits)

DASC7600Data science project (12 credits)


  1. The programme structure will be reviewed from time to time and is subject to change.

  2. *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 24 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$309,000 (for FT & PT students admitted in September 2024). The full fee would normally be paid by full-time students in 3 installments, and part-time students in 5 installments.


Fellowships Scheme

Master of Data Science (MDASC) is one of the Programmes sponsored by University Grants Committee (UGC) for Targeted Taught Postgraduate Programmes Fellowships Scheme. Local offer recipients who will be students of MDASC in the academic year 2024-25 are eligible for application, full-time or part-time alike (other terms and conditions apply).

Local offer recipients who wish to apply for the Fellowship scheme should prepare a proposal on how they can contribute to the priority areas (i.e. Research and STEM) of Hong Kong after completing MDASC.

Interested local offer recipients should apply for the scheme themselves online soon after they receive the offer notifications. Please ask the Programme Secretary for the exact deadline of application.

Successful Fellowship Scheme applicants will each receive an award of HK$120,000. 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.


Continuing Education Fund (CEF)

The following courses have been included in the list of reimbursable courses for Continuing Education Fund (CEF) purposes:

All CEF applicants are required to attend at least 70% of the courses before they are eligible for fee reimbursement under the CEF.


Target Admission Number

The Programme plans to admit approximately 50 full-time students and 20 part-time students for the academic year 2024-25.