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

  • Joint programme offered by Department of Statistics and Actuarial Science and Department of Computer Science
  • Interdisciplinary and comprehensive curriculum
  • Solid foundation in statistical and computational analyses
  • Students can select electives from Computer Science, Mathematics and Statistics
  • Electives cover a broad range of contemporary topics
  • 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.

Data Science technology     Computational intelligence
       Machine 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     Marketing analytics     Quantitative risk
            Management and finance     Network security
    Cluster & cloud computing     Data mining techniques
   Multimedia technologies     Smart phone apps development

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. Please click HERE for detailed syllabuses and regulations (subject to approval).


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 12 credits from List B

List A

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

List B

MATH8502Topics in applied discrete mathematics (6 credits)
MATH8503Topics in mathematical programming and optimization (6 credits)
STAT6013Financial data analysis (6 credits)
STAT6015Advanced quantitative risk management and finance (6 credits)
STAT6016Spatial data analysis (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 and text analytics (3 credits)

*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.

Capstone requirement (12 credits)

DASC7600Data science project (12 credits)
Programme Fees

This is a self-funded programme. The full tuition fee is HK$252,000 (for FT & PT students admitted in September 2020). The full fee would normally be paid by full-time students in 3 installments, and part-time students in 5 installments.


Fellowships Scheme

MDASC is 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 2020-21 are eligible, full-time or part-time alike, for applications (other terms and conditions apply). Successful Fellowship Scheme applicants will each receive an award of HK$120,000. [Details]


Continuing Education Fund (CEF)

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

The mother programme (Master of Data Science) of these courses is recognized under the Qualification Framework (QF Level 6).



Suoxinda Scholarship in Data Science awards two scholarships of HK$20,000 annually to students entering the Master of Data Science programme based on academic merit and admission interview performance. Details


Target Admission Number

The Programme plans to admit approximately 45 full-time students and 30 part-time students for the academic year 2020-21.