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
those whose interest in high-level analytical skills straddles the disciplinary divide between statistics and computational analytics, and
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.
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
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
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
The normative study period of full-time students is 1.5 years, while that of part-time students is 2.5 years.
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)
Computational intelligence and machine learning (6 credits)
Statistical inference for data science (6 credits)
Advanced database systems (6 credits)
Deep learning (6 credits)
Advanced statistical modelling (6 credits)
Time series forecasting (6 credits)
Disciplinary Electives (24 credits)*
with at least 12 credits from List A and 12 credits from List B
Advanced topics in data science (6 credits)
Cluster and cloud computing (6 credits)
Multimedia technologies (6 credits)
Smart phone apps development (6 credits)
Visualization and visual analytics (6 credits)
Introduction to cyber security (6 credits)
Data science for business (6 credits)
Topics in applied discrete mathematics (6 credits)
Topics in mathematical programming and optimization (6 credits)
Financial data analysis (6 credits)
Advanced quantitative risk management and finance (6 credits)
Spatial data analysis (6 credits)
Programming for data science (6 credits)
Data mining techniques (6 credits)
Marketing analytics (6 credits)
Statistical methods for network data (3 credits)
Natural 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)
Data science project (12 credits)
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.
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.
Target Admission Number
The Programme plans to admit approximately 45 full-time students and 30 part-time students for the academic year 2020-21.