Skip to main content
Start main content

Taught Postgraduate Programme

Master of Data Science




It is a programme ideal for

  1. those who are interested in acquiring skills in big data analytics/artificial intelligence, 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.

Admission Requirements

To be eligible for admission to the programme, you should have:

  1. A Bachelor's degree with honours, or an equivalent qualification;
  2. Applicants should have taken at least one university or post-secondary certificate course in each of the following three subjects (calculus and algebra, computer programming and introductory statistics) or related areas; and
  3. Fulfil the University Entrance Requirements.
  • Main Round:  November 1, 2019 to December 15, 2019. Candidates apply within this period will have priority.
  • Clearing Round: December 16, 2019 to 12 noon January 31, 2020.

Applications can be submitted via our on-line application system here.

The tuition fee for the programme is HK$252,000* for the 2020-21 intake. The fee shall normally be payable in three instalments over 1.5 years for full-time study or in five instalments over 2.5 years for part-time study.

In addition, students are required to pay Caution Money (HK$350), refundable on graduation subject to no claims being made, and Graduation Fee (HK$350).

* Subject to approval

Reimbursable Course(s) by Continuing Education Fund (CEF) 

The following courses have been included in the list of reimbursable courses for CEF purposes:

STAT8017 Data mining techniques

STAT8019 Marketing analytics

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


Suoxinda Scholarship in Data Science

Two scholarship recipients, each receiving HK$20,000, would be selected from the students entering the Master of Data Science programme on the basis of academic merit and admission interview performance.

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 Highlights

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 technologyComputational 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 analysisMarketing analyticsQuantitative risk

Management and finance

Network security

Cluster & cloud computing

Data mining techniques
Multimedia technologies Smart phone apps development

Programme Curriculum

Commencing in September, the curriculum is composed of 72 credits of courses. Courses with 6 credits are offered in the first and second semesters while courses with 3 credits are normally offered in the summer semester. If a student selects a course whose contents are similar to a course (or courses) which he/she has taken in his/her previous study, the Department may not approve the selection in question. The curriculum is the same for both full-time and part-time study modes.


Compulsory Courses (36 credits)
COMP7404 Computational intelligence and machine learning (6 credits)
DASC7011  Statistical inference for data science (6 credits)


Advanced database systems (6 credits)
DASC7606 Deep learning (6 credits)


Advanced statistical modelling (6 credits)
STAT8003 Time series forecasting (6 credits)

Disciplinary Electives (24 credits)*

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

List A





Advanced topics in data science (6 credits)


 Cluster and Cloud Computing (6 credits)Cluster and cloud computing (6 credits)Cluster and Cloud Computing (6 credits)Cluster and Cloud Computing (6 credits)
COMP7503 Multimedia technologies (6 credits)


Smart phone apps development (6 credits)
COMP7507 Visualization and visual analytics (6 credits)
COMP7906 Introduction to cyber security (6 credits)


Data science for business (6 credits)
List B  


 Topics in applied discrete mathematics (6 credits)
MATH8503 Topics in mathematical programming and optimization (6 credits)
STAT6013 Financial data analysis (6 credits)
STAT6015 Advanced quantitative risk management and finance (6 credits)
STAT6016 Spatial data analysis (6 credits)
STAT7008 Programming for data science (6 credits)
STAT8017 Data mining techniques (6 credits)
STAT8019 Marketing analytics (6 credits)
STAT8306 Statistical methods for network data (3 credits)
STAT8307 Natural language processing and text analysis (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)
DASC7600 Data Science Project (12 credits)



Compulsory Courses

COMP7404 Computational intelligence and machine learning (6 credits)

This course will teach a broad set of principles and tools that will provide the mathematical, algorithmic and philosophical framework for tackling problems using Artificial Intelligence (AI) and Machine Learning (ML).  AI and ML are highly interdisciplinary fields with impact in different applications, such as, biology, robotics, language, economics, and computer science.  AI is the science and engineering of making intelligent machines, especially intelligent computer programs, while ML refers to the changes in systems that perform tasks associated with AI. Ethical issues in advanced AI and how to prevent learning algorithms from acquiring morally undesirable biases will be covered.

Topics may include a subset of the following: problem solving by search, heuristic (informed) search, constraint satisfaction, games, knowledge-based agents, supervised learning, unsupervised learning; learning theory, reinforcement learning and adaptive control and ethical challenges of AI and ML.


Pre-requisites:  Nil, but knowledge of data structures and algorithms, probability, linear algebra, and programming would be an advantage.


Assessment: One 2-hour written examination; 50% coursework and 50% examination

DASC7011 Statistical inference for data science (6 credits)​​​​​

Computing power has revolutionized the theory and practice of statistical inference. Reciprocally, novel statistical inference procedures are becoming an integral part of data science. By focusing on the interplay between statistical inference and methodologies for data science, this course reviews the main concepts underpinning classical statistical inference, studies computer-intensive methods for conducting statistical inference, and examines important issues concerning statistical inference drawn upon modern learning technologies. Contents include classical frequentist and Bayesian inferences, computer-intensive methods such as the EM algorithm, the bootstrap and the Markov chain Monte Carlo, large-scale hypothesis testing, high-dimensional modeling, and post-model-selection inference.


Assessment: One 2-hour written examination; 40% coursework and 60% examination

DASC7104 Advanced database systems (6 credits)

The course will study some advanced topics and techniques in database systems, with a focus on the aspects of big data analytics, algorithms, and system design & organisation.  It will also survey the recent development and progress in selected areas. Topics include: query optimization, spatial-spatiotemporal data management, multimedia and time-series data management, information retrieval and XML, data mining.


Assessment: One 2-hour written examination; 50% coursework and 50% examination

DASC7606 Deep learning (6 credits)​​​​​

Machine learning is a fast growing field in computer science and deep learning is the cutting edge technology that enables machines to learn from large-scale and complex datasets. Ethical implications of deep learning and its applications will be covered first and the course will focus on how deep neural networks are applied to solve a wide range of problems in areas such as natural language processing, image processing, financial predictions, game playing and robotics. Topics covered include linear and logistic regression, artificial neural networks and how to train them, recurrent neural networks, convolutional neural networks, deep reinforcement learning and unsupervised feature learning. Popular deep learning software, such as TensorFlow, will also be introduced.


Assessment:     One 2-hour written examination; 50% coursework and 50% examination

STAT7102 Advanced statistical modelling (6 credits)

This course introduces modern methods for constructing and evaluating statistical models and their implementation using popular computing software, such as R or Python.  It will cover both the underlying principles of each modelling approach and the model estimation procedures.  Topics from: (i) Linear regression models; (ii) Generalized linear models; (iii) Mixed models; (iv) Kernel and local polynomial regression; (v) Generalized additive models; (vi) Hidden Markov model and Bayesian networks.


Assessment: One 2-hour written examination; 50% coursework and 50% examination

STAT8003 Time series forecasting (6 credits)

A time series consists of a set of observations on a random variable taken over time.  Such series arise naturally in climatology, economics, finance, environmental research and many other disciplines.  In additional to statistical modelling, the course deals with the prediction of future behaviour of these time series.  This course distinguishes different types of time series, investigates various representations for them and studies the relative merits of different forecasting procedures.


Assessment:     One 3-hour written examination; 40% coursework and 60% examination

Disciplinary Electives

COMP7105 Advanced topics in data science (6 credits)

This course will introduce selected advanced computational methods and apply them to problems in data analysis and relevant applications. 

Assessment: One 2-hour written examination; 50% coursework and 50% examination

COMP7305 Cluster and cloud computing (6 credits)

This course offers an overview of current cloud technologies, and discusses various issues in the design and implementation of cloud systems.  Topics include cloud delivery models (SaaS, PaaS, and IaaS) with motivating examples from Google, Amazon, and Microsoft; virtualization techniques implemented in Xen, KVM, VMWare, and Docker; distributed file systems, such as Hadoop file system; MapReduce and Spark programming models for large-scale data analysis, networking techniques in hyper-scale data centers.  The students will learn the use of Amazon EC2 to deploy applications on cloud, and implement a novel cloud computing application on a Xen-enabled PC cluster as part of their term project.


Prerequisites:    Students are expected to install various open-source cloud software in their Linux cluster, and exercise the system configuration and administration. Basic understanding of Linux operating system and some programming experiences (C/C++, Java or Python) in a Linux environment are required.


Assessment:     One 2-hour written examination; 50% coursework and 50% examination

COMP7503 Multimedia technologies (6 credits)

This course presents fundamental concepts and emerging technologies for multimedia computing.  Students are expected to learn how to develop various kinds of media communication, presentation, and manipulation techniques.  At the end of course,students should acquire proper skill set to utilize, integrate and synchronize different information and data from media sources for building specific multimedia applications.  Topics include media data acquisition methods and techniques; nature of perceptually encoded information; processing and manipulation of media data; multimedia content organization and analysis; trending technologies for future multimedia computing. 

Assessment: One 2-hour written examination; 50% coursework and 50% examination

COMP7506 Smart phone apps development (6 credits)

Smart phones have become very popular in recent years.According to a study, by 2020, 70% of the world's population is projected to own a smart phone, an estimated total of almost 6.1 billion smartphone users in the world.


Smart phones play an important role in mobile communication and applications.


Smart phones are powerful as they support a wide range of applications (called apps).  Most of the time, smart phone users just purchase their favorite apps wirelessly from the vendors.  There is a great potential for software developer to reach worldwide users.

This course aims at introducing the design issues of smart phone apps.  For examples, the smart phone screen is usually much smaller than the computer monitor.  We have to pay special attention to this aspect in order to develop attractive and successful apps.  Various modern smart phone apps development environments and programming techniques (such as Java for Android phones, and Swift for iPhones) will also be introduced to facilitate students to develop their own apps.

Prerequisites:  Students should have basic programming knowledge

Assessment: One 2-hour written examination; 50% coursework and 50% examination

COMP7507 Visualization and visual analytics (6 credits)

This course introduces the basic principles and techniques in visualization and visual analytics, and their applications.  Topics include human visual perception; color; visualization techniques for spatial, geospatial and multivariate data, graphs and networks; text and document visualization; scientific visualization; interaction and visual analysis.


Assessment: One 2-hour written examination; 50% coursework and 50% examination

COMP7906 Introduction to cyber security (6 credits)

The aim of the course is to introduce different methods of protecting information and data in the cyber world, including the privacy issue. Topics include introduction to security; cyber attacks and threats; cryptographic algorithms and applications; network security and infrastructure.

Pre-requisites:  Students should not have taken ICOM6045 Fundamentals of e-commerce security or equivalent

Assessment: One 2-hour written examination; 50% coursework and 50% examination

ICOM6044 Data science for business (6 credits)

The emerging discipline of data science combines statistical methods with computer science to solve problems in applied areas.  In this case we focus on how data science can be used to solve business problems especially those in electronic commerce.  By its very nature e-commerce is able to generate large amounts of data and data mining methods are quite helpful for managers in turning this data into knowledge which in turn can be used to make better decisions.  These data sets and their accompanying quantitative methods have the potential to dramatically change decision making in many areas of business.  For example, ideas like interactive marketing, customer relationship management, and database marketing are pushing companies to utilize the information they collect about their customers in order to make better marketing decisions.

This course focuses on how data science methods can be applied to solve managerial problems in marketing and electronic commerce.  Our emphasis is developing a core set of principles that embody data science: empirical reasoning, exploratory and visual analysis, and predictive modeling.  We use these core principles to understand many methods used in data mining and machine learning.  Our strategy in this course is to survey several popular techniques and understand how they map into these core principles.  These techniques are illustrated with case studies.  However, the emphasis is not on the software for implementing these techniques but on understanding the inputs and outputs of these techniques and how they are used to solve business problems.  

Pre-requisites: Students should not be taking or have taken STAT8017 Data mining techniques or equivalent

Assessment: One 2-hour written examination; 65% coursework and 35% examination

MATH8502 Topics in Applied Discrete Mathematics (6 credits)

This course aims to provide students with the opportunity to study some further topics in applied discrete mathematics.  A selection of topics in discrete mathematics applied in combinatorics and optimization (such as algebraic coding theory, cryptography, discrete optimization, etc.) The selected topics may vary from year to year. 


Pre-requisites: Knowledge in introductory discrete mathematics. Students may be asked to present appropriate evidence of having met the pre-requisites for enrolling in this course.


Assessment: One 2.5-hour written examination; 50% coursework and 50% examination 

MATH8503 Topics in Mathematical Programming and Optimization (6 credits)

A study in greater depth of some special topics in mathematical programming or optimization. It is mainly intended for students in Operations Research or related subject areas. This course covers a selection of topics which may include convex programming, nonconvex programming, saddle point problems, variational inequalities, optimization theory and algorithms suitable for applications in various areas such as machine learning, artificial intelligence, imaging and computer vision. The selected topics may vary from year to year.


Pre-requisites: Knowledge in introductory mathematical programming and optimization. Students may be asked to present appropriate evidence of having met the pre-requisites for enrolling in this course.   


Assessment: 100% coursework

STAT6013 Financial data analysis (6 credits)

This course aims at introducing statistical methodologies in analyzing financial data.  Financial applications and statistical methodologies are intertwined in all lectures. Contents include: recent advances in modern portfolio theory, Copula, market microstructure and high frequency data analysis, FinTech applications with various computational tools such as artificial neural networks, Kalman filters and blockchain data analysis.


Assessment: One 2-hour written examination; 40% coursework and 60% examination

STAT6015 Advanced quantitative risk management and finance (6 credits)

This course covers statistical methods and models of importance to risk management and finance and links finance theory to market practice via statistical modelling and decision making.  Emphases will be put on empirical analyses to address the discrepancy between finance theory and market data.  Contents include: Elementary Stochastic Calculus; Basic Monte Carlo and Quasi-Monte Carlo Methods; Variance Reduction Techniques; Simulating the value of options and the value-at-risk for risk management; Review of univariate volatility models; multivariate volatility models; Value-at-risk and expected shortfall; estimation, back-testing and stress testing; Extreme value theory for risk management. 


Assessment: One 2-hour written examination; 25% coursework and 75% examination

STAT6016 Spatial data analysis (6 credits)

This course covers statistical concepts and tools involved in modelling data which are correlated in space.Applications can be found in many fields including epidemiology and public health, environmental sciences and ecology, economics and others. Covered topics include: (1) Outline of three types of spatial data: point-level (geostatistical), areal (lattice), and spatial point process. (2) Model-based geostatistics: covariance functions and the variogram; spatial trends and directional effects; intrinsic models; estimation by curve fitting or by maximum likelihood; spatial prediction by least squares, by simple and ordinary kriging, by trans-Gaussian kriging. (3) Areal data models: introduction to Markov random fields; conditional, intrinsic, and simultaneous autoregressive (CAR,IAR, and SAR) models.(4) Hierarchical modelling for univariate spatial response data, including Bayesian kriging and lattice modelling. (5) Introduction to simple spatial point processes and spatio-temporal models. Real data analysis examples will be provided with dedicated R packages such as geoR.


Assessment: One 2-hour written examination; 50% coursework and 50% examination

STAT7008 Programing for data science(6 credits)

In the big data era, it is very easy to collect huge amounts of data. Capturing and exploiting the important information contained within such datasets poses a number of statistical challenges. This course aims to provide students with a strong foundation in computing skills necessary to use R or Python to tackle some of these challenges.  Possible topics to be covered may include exploratory data analysis and visualization, collecting data from a variety of sources (e.g. excel, web-scraping, APIs and others), object-oriented programming concepts and scientific computation tools.  Students will learn to create their own R packages or Python libraries.


Assessment:     100% coursework

STAT8017 Data mining techniques (6 credits)

With the rapid developments in computer and data storage technologies, the fundamental paradigms of classical data analysis are mature for change. Data mining techniques aim at helping people to work smarter by revealing underlying structure and relationships in large amounts of data.  This course takes a practical approach to introduce the new generation of data mining techniques and show how to use them to make better decisions. Topics include data preparation, feature selection, association rules, decision trees, bagging, random forests and gradient boosting, cluster analysis, neural networks, introduction to text mining.  


Pre-requisites: Students should not be taking or have taken ICOM6044 Data science for business or equivalent

Assessment: 100% coursework

STAT8019 Marketing analytics (6 credits)

This course aims to introduce various statistical models and methodology used in marketing research. Special emphasis will be put on marketing analytics and statistical techniques for marketing decision making including market segmentation, market response models, consumer preference analysis and conjoint analysis.  Contents include market response models, statistical methods for segmentation, targeting and positioning, statistical methods for new product design.

Assessment: One 3-hour written examination; 40% coursework and 60% examination

STAT8306 Statistical methods for network data (3 credits)

The six degree of separation theorizes that human interactions could be easily represented in the form of a network. Examples of networks include router networks, the World Wide Web, social networks (e.g. Facebook or Twitter), genetic interaction networks and various collaboration networks (e.g. movie actor coloration network and scientific paper collaboration network). Despite the diversity in the nature of sources, the networks exhibit some common properties. For example, both the spread of disease in a population and the spread of rumors in a social network are in sub-logarithmic time. This course aims at discussing the common properties of real networks and the recent development of statistical network models. Topics may include common network measures, community detection in graphs, preferential attachment random network models, exponential random graph models, models based on random point processes and the hidden network discovery on a set of dependent random variables.


Assessment: One 1.5-hour written examination; 50% coursework and 50% examination

STAT8307 Natural language processing and text analytics (3 credits)

The textual data constitutes an enormous proportion of unstructured data which is characterized as one of ‘V’s in Big Data. The logical and computational reasonings are applied to transform large collection of written resources to structured data for use in further analysis, visualization, integration with structured data in database or warehouse, and further refinement using machine learning systems. This course introduces the methodology of text analytics. Topics include natural language processing, word representation, text categorization and clustering, topic modelling and sentiment analysis. Students are required to possess basic understanding of Python language.

Pre-requisites:   Pass in STAT8017 Data mining techniques or equivalent

Assessment: 100% coursework

Capstone Requirement

DASC7600 Data science project (12 credits)

Candidate will be required to carry out independent work on a major project under the supervision of individual staff member.  A written report is required.


Assessment: 75% written report and 25% oral presentation


Programme Director

Prof G S Yin

Professor G S Yin
MA Temple; MSc, PhD N Carolina
Patrick S C Poon Professor in Statistics and Actuarial Science
Department of Statistics and Actuarial Science


Staff List

Department of Statistics and Actuarial Science

Department of Computer Science

  • Dr A Benchimol, BSc UBA; MA UAH; MPhil, PhD UC3M
  • Dr K C Cheung, BSc(ActuSc), PhD HK; ASA
  • Dr S K C Cheung, BSc HK, MSc ANU; PhD CUHK
  • Ms O T K Choi, BSc UBC; MSc Oxford
  • Dr Y K Chung, BSc, MPhil CUHK; PhD HK
  • Professor T W K Fung, BSocSc HK; MSc Lond; PhD HK; DIC
  • Professor F W H Ho, BSc, MSocSc HK
  • Mrs G M Jing, BSc, MA, DipEd Syd
  • Dr C W Kwan, BSc, PhD HK
  • Dr E K F Lam, BA St. Thomas; MA New Brunswick; PhD HK
  • Professor K Lam, BA HK; PhD Wisconsin
  • Dr D Lee, BSc(ActuSc), MPhil HK; PhD British Columbia
  • Professor S M S Lee, BA, PhD Cantab
  • Professor B Cautis, PhD INRIA & University of Paris South, Orsay
  • Dr B M Y Chan, MS UC San Diego, PhD HK
  • Dr K P Chow, MA, PhD, UC Santa Barbara
  • Dr T W Chim, PhD HK
  • Dr L Y K Choi, PhD HK
  • Dr R H Y Chung, PhD HK
  • Dr W Y Chung, PhD The University of Arizona
  • Professor A Montgomery, MBA, PhD University of Chicago
  • Dr D Schnieders, PhD HK
  • Dr M Sozio, PhD Sapienza University of Rome
  • Professor C L Wang, PhD USC
  • Dr R S W Yiu, PhD UC Berkeley
  • Dr S M Yiu, PhD HK
  • Mr D K T Leung, BA, MBA HK
  • Dr E A L Li, BSc HK; MEcon, PhD Syd
  • Dr G D Li, BSc MSc Peking; PhD HK
  • Dr W T Li, BSc USTC; PhD Rutgers
  • Dr Z H Liu, ScD Harvard
  • Mr P K Y Pang, BSc HK; MBA NSW
  • Dr C Wang, PhD NUS
  • Dr K P Wat, BSc(ActuSc), PhD HK; FSA; CERA; FRM
  • Dr J T Y Wong, BSc(ActuSc), MPhil HK; PhD Waterloo; FSA
  • Dr R W L Wong, BSc, MPhil CUHK; MA, PhD Pittsburg; ASA
  • Professor S P S Wong, BSc, MPhil HKU; PhD Stanford
  • Dr J F Xu, BSc, USTC; MPhil, PhD Columbia
  • Professor H L Yang, BSc Inner Mongolia; MMath Waterloo; PhD Alberta; ASA; HonFIA
  • Professor J J F Yao, BSc, MSc, PhD Paris-Sud Orsay
  • Professor G S Yin, MA Temple; MSc, PhD N Carolina
  • Dr P L H Yu, BSc, PhD HK
  • Professor K C Yuen, BSc, MSc, PhD Calgary; ASA
  • Dr A J Zhang, BSc, MPhil HKBU; MSc, PhD Michigan
  • Dr D Y Zhang, BSc Nankai; MSc, PhD NCSU
  • Dr Z Q Zhang, BSc Nankai, MSc E China Normal; PhD HK
  • Dr K Zhu, BSc, USTC; PhD HKUST

    Graduate/ Student Sharing

    Cheung Ting Hin

    Ting Hin CHEUNG 

    Part-time student


    "Being a part-time MDASC student with no statistic nor programming background, it was definitely a challenging yet fruitful experience so far. After over a decade working in the sales and trading industry, I felt refreshing back to the campus and studying all the fancy formula and symbols again.


    I would say the courses are much more demanding than I expected, but thanks to the summer preparation classes, it helped to recall my memories on some basic concepts."




    Ms Aka Lee

    Department of Statistics & Actuarial Science

    Faculty of Science

    The University of Hong Kong

    • G/F Chong Yuet Ming Physics Building
    • (852) 3917 5287
    • (852) 2858 4620