Skip to main content
Start main content

Taught Postgraduate Programme

Master of Statistics


Master of Statistics Poster

It is a programme ideal for

1. those whose wish to advance their quantitative and analytical skills to prepare for a data-focused career path, and

2. those who wish to pursue further study in the field of statistics after studying science, social sciences, engineering, medical sciences, information systems, business and finance in their undergraduate studies.

Admission Requirements

To be eligible for admission to the programme, you should have a Bachelor's degree, or an equivalent qualification, with knowledge of matrices and calculus, introductory statistics and linear modelling.

  • On-line application is open in early November 2023.
  • Main Round Deadline: 12:00 noon (GMT +8), November 20, 2023. Candidates who apply within this period will have priority.
  • Clearing Round Deadline: 12:00 noon (GMT +8), January 8, 2024

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


#Please submit your supporting documents via here. Note that you will receive an email with the login instructions within 48 hours after your application is submitted.

The tuition fee for the full-time programme is HK$218,000* for the 2024-25 intake and that for the part-time programme is HK$109,000* per year for two years. The fee shall be payable in two instalments over one year for full-time study or in four instalments over two 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). With effect from 2022-23, all full-time students will be charged a student activity fee of HK$100 per annum to provide support for activities of student societies and campus wide student events.

The University allows Occasional Students to enroll in individual courses without registering in any particular programme of study. Tuition fee for an Occasional Student is HK$3,630* per credit in the academic year 2024-25. For enquiries, please contact the Department of Statistics & Actuarial Science (Tel: 3917 6042; Email:

* Subject to approval

Master of Statistics Outstanding Performance Scholarship 

One scholarship of HK$50,000 shall be awarded annually to an MStat student on the basis of academic merit and quality of coursework.


Lifelong Learning Prizes in Statistics 

There are Lifelong Learning Prizes in Statistics, each from $5,000 to $10,000, awarded to students on the basis of academic achievement.


Belt and Road Scholarship in Statistics and Data Science (Taught Postgraduate) 

Belt and Road Scholarship in Statistics and Data Science (Taught Postgraduate) is awarded annually to outstanding new students from participating Belt and Road countries. Composition fees of MStat could be waived for awardees, and additional allowance of HK$10,000 will be provided to support their studies.


Entrance Scholarship for the Master of Statistics 

There is an Entrance Scholarship for Master of Statistics of HK$20,000, awarded annually to new MStat students on the basis of academic merit, financial need upon admission and, if necessary, interview performance.


Targeted Taught Postgraduate Programmes Fellowships SchemeTargeted Taught Postgraduate Programmes Fellowships Scheme (TPgFS)

Master of Statistics (MStat) 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 MStat 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. Business and STEM) of Hong Kong after completing MStat. Successful Fellowship Scheme applicants will each receive an award of HK$120,000.


Reimbursable Courses by Continuing Education Fund (CEF)* CEF

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

  • STAT6013 Financial Data Analysis
  • STAT7008 Programming for Data Science
  • STAT8003 Time Series Forecasting
  • STAT8007 Statistical methods in economics and finance
  • STAT8017 Data mining techniques
  • STAT8019 Marketing Analytics


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

* The mother programme (Master of Statistics) of these courses is recognised under the Qualifications Framework (QF Level 6).

The degree of Master of Statistics is a one-year full-time / two-year part-time programme, which has been restructured from the previous degree of Master of Social Sciences in Applied Statistics that was launched in September 1987. Since the first graduation in 1989, we expect to have over 1,000 graduates when the present cohort completes the programme. This programme is designed to provide a rigorous training in the principles and the practice of statistics. It emphasizes in applications and aims to prepare candidates for further study, research, consulting work and administration in various fields through computer-aided and hands-on experience.

Programme Highlights

  • Be a knowledgeable statistician in principles and practice
  • Experience hands-on applications of methodologies with powerful statistical software
  • Could select electives from the Department’s research postgraduate courses
  • Join the programme of more than 30 years in curriculum development and delivery
  • Select a theme of your interest (Risk Management theme / Data Analytics theme / Financial Statistics)


Programme Learning Outcomes 

1. To acquire advanced knowledge in statistics and practical skills of applying appropriate statistical methods, models and techniques, and develop new knowledge and skills through life-long learning

2. To equip with hands-on experience in statistical and risk analyses using commercial statistical software and be competent for data-analytic jobs which require advanced computational skills

3. To make informed decisions on complex real-life problems encountered in the data explosion era

4. To communicate effectively with the layman on statistical issues

5. To critically evaluate and to make proper use of models and techniques for data analyses and risk management, and to appraise the related ethical issues

6. To prepare to be confident statisticians for providing professional view on statistical issues

Programme Curriculum 

Commencing in September, the curriculum is composed of a total of 60 credits of courses in either one year for full-time study, or two years for part-time study. The programme offers great flexibilities for students who wish to take a general approach or a specialised theme in Risk Management, Data Analytics or Financial Statistics. A student may choose to have his/her theme printed on the transcript if he/she has satisfied the requirement of one of the themes. If a student selects an MStat 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. Students must obtain a cumulative GPA of at least 2.0 to graduate.

Curriculum study (applicable for both full-time and part-time modes)

Two compulsory courses (12 credits)

STAT7101   Fundamentals of statistical inference (6 credits)

STAT7102   Advanced statistical modelling (6 credits)

Theme-specific elective courses (24 Credits)
Risk Management theme
Data Analytics themeFinancial Statistics theme
STAT6015 Advanced quantitative risk management (6 credits)STAT6011 Computational statistics and Bayesian learning (6 credits)STAT6013 Financial data analysis (6 credits)
STAT6017 Operational risk and insurance analytics (6 credits)STAT6016 Spatial data analysis (6 credits)STAT7009 Stochastic dependence modelling (6 credits)
STAT7009 Stochastic dependence modelling (6 credits)STAT7005 Multivariate methods (6 credits)STAT8003 Time series forecasting (6 credits)
STAT8003 Time series forecasting (6 credits)STAT7007 Categorical data analysis (3 credits)STAT8007 Statistical methods in economics and finance (6 credits)

STAT8007 Statistical methods in economics and finance (6 credits)

STAT8003 Time series forecasting (6 credits)STAT8015 Actuarial statistics (6 credits)

STAT8015 Actuarial statistics (6 credits)

STAT8016 Biostatistics (6 credits)STAT8017 Data mining techniques (6 credits)
STAT8017 Data mining techniques (6 credits)STAT8017 Data mining techniques (6 credits)STAT8020 Quantitative strategies and algorithmic trading (6 credits)
STAT8308 Blockchain data analytics (3 credits)STAT8019 Marketing analytics (6 credits)STAT8021 Big data analytics (6 credits)

STAT8021 Big data analytics (6 credits)STAT8309 Monte Carlo Simulation and Finance (3 credits)

STAT8302 Structural equation modelling (3 credits) 
 STAT8306 Statistical methods for network data (3 credits) 

Other elective courses (18 credits)


STAT6009    Research methods in statistics (6 credits)

STAT6010    Advanced probability (6 credits)

STAT6019    Current topics in statistics (6 credits

STAT7006    Design and analysis of sample surveys (6 credits)

STAT7008    Programming for data science (6 credits)

STAT8000    Workshop on spreadsheet modelling and database management (3 credits)

STAT8300    Career development and communication workshop (Non-credit bearing)


Capstone requirement (6 credits)


STAT8017   Data mining techniques (6 credits)

STAT8088   Statistical Practicum  (6 credits)

STAT8089   Capstone Project (6 credits)




1. Apart from the two compulsory courses and capstone requirement, candidates may choose not to follow any theme and may take 42 credits of elective courses in any order, whenever feasible.

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



Compulsory Course Replacement
Students with prior background has to take a more advanced course from the same area as replacement:
Replace ...With
STAT7101 Fundamentals of statistical inference (6 credits)

STAT6009 Research methods in statistics (6 credits)


STAT7005 Multivariate methods (6 credits)

STAT7102 Advanced statistical modelling (6 credits)

Any other course


B. Course contents


Compulsory Courses

STAT7101 Fundamentals of statistical inference (6 credits)

Motivated by real problems involving uncertainty and variability, this course introduces the basic concepts and principles of statistical inference and decision-making. Contents include: large-sample theories; estimation theory; likelihood principle; maximum likelihood estimation; hypotheses testing; likelihood ratio tests; nonparametric inference; computer-intensive methods such as EM algorithm and bootstrap methods. (Only under exceptional academic circumstances can this compulsory course be replaced by an elective course.)


Assessment: coursework (40%) and examination (60%)

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) Model selection and regularization; (iv) Kernel and local polynomial regression; selection of smoothing parameters; (v) Generalized additive models; (vi) Hidden Markov models and Bayesian networks.


Assessment: coursework (50%) and examination (50%)


STAT6009 Research methods in statistics (6 credits)

This course introduces some statistical concepts and methods which potential graduate students will find useful in preparing for work on a research degree in statistics.  Focus is on applications of state-of-the-art statistical techniques and their underlying theory.  Contents may be selected from: (1) Basic asymptotic methods: modes of convergence; stochastic orders; laws of large numbers; central limit theorems; delta method; (2) Parametric and nonparametric likelihood methods: high-order approximations; profile likelihood and its variants; signed likelihood ratio statistics; empirical likelihood; (3) Nonparametric statistical inference: sign and rank tests; Kolmogorov-Smirnov test; nonparametric regression; density estimation; kernel methods; (4) Computationally-intensive methods: cross-validation; bootstrap; permutation methods;  (5) Robust methods: measures of robustness; M-estimator; L-estimator; R-estimator; estimating functions; (6) Other topics as determined by the instructor.


Assessment: coursework (40%) and examination (60%)

STAT6010 Advanced probability (6 credits)

This course provides an introduction to measure theory and probability, with focus on mathematical concepts in probability important for students to conduct research in probability, statistics and actuarial science. Contents include: sigma-algebra, measurable spaces, measures and probability measures, , measurable functions, random variables, integration theory, characteristic functions, modes of convergence of random variables, conditional expectations, martingales.


Assessment:  coursework (40%) and examination (60%)

STAT6011 Computational statistics and Bayesian learning (6 credits)

This course aims to give undergraduate and postgraduate students and introduction on modern computationally intensive methods in statistics. It emphasizes the role of computation as a fundamental tool of discovery in data analysis and statistical inference, and for development of statistical theory and methods. Contents include: Bayesian statistics, Markov chain Monte Carlo methods such as Gibbs sampler, Metropolis-Hastings algorithm, and data augmentation; generation of random variables using the inversion methods, rejection sampling, the sampling/importance resampling method; optimization techniques including Newton’s method, expectation-maximization (EM) algorithm and its variants, and minorization-maximization (MM) algorithm; integration including Laplace approximation, Gaussian quadrature, the importance sampling method, Monte Carlo integration, and other topics such as hidden Markov models, and Bootstrap methods. More advanced Bayesian learning methods cover approximate Bayesian computation, the Hamiltonian Monte Carlo algorithm, hierarchical models and nonparametric Bayes.


Assessment: coursework (50%) and examination (50%)

​​​​​​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: classical portfolio theory, portfolio selection in practice, single index market model, robust parameter estimation, copula and high frequency data analysis.


Assessment: coursework (40%) and examination (60%)

STAT6015 Advanced quantitative risk management (6 credits)

This course covers statistical methods and models of risk management, especially of Value-at-Risk (VaR). Contents include: Value-at-risk (VaR) and Expected Shortfall (ES); univariate models (normal model, log-normal model and stochastic process model) for VaR and ES; models for portfolio VaR; time series models for VaR; extreme value approach to VaR; back-testing and stress testing.


Assessment: coursework (50%) and examination (50%)

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: coursework (50%) and examination (50%)

STAT6017 Operational risk and insurance analytics (6 credits)

This course aims to provide the foundation of operational risk management and insurance. Special emphasis will be put on the analytical and modeling techniques for operational risk and insurance. Contents include fundamentals of operational risk and Basel regulation, loss distribution, estimation of risk models, copula and modeling dependence, insurance and risk transfer for operational risk.


Assessment: coursework (50%) and examination (50%)

STAT6019 Current topics in statistics (6 credits)

This course includes two modules. The first module, Causal Inference, is an introduction to key concepts and methods for causal inference. Contents include 1) the counterfactual outcome, randomized experiment, observational study; 2) Effect modification, mediation and interaction; 3) Causal graphs; 4) Confounding, selection bias, measurement error and random variability; 5) Inverse probability weighting and the marginal structural models; 6) Outcome regression and the propensity score; 7) The standardization and the parametric g-formula; 8) G-estimation and the structural nested model; 9) Instrumental variable method; 10) Machine learning methods for causal inference; 11) Other topics as determined by the instructor.

The second module, Function data analysis, cover topics from: 1) Base Functions; 2) Least squares estimation; 3) Constrained Functions; 4) Functional PCA; 5) Regularized PCA; 6) Functional linear model; 7) Other topics as determined by the instructor.


Assessment: coursework (100%)

STAT7005 Multivariate methods (6 credits)

In many disciplines the basic data on an experimental unit consist of a vector of possibly correlated measurements.  Examples include the chemical composition of a rock; the results of clinical observations and tests on a patient; the household expenditures on different commodities.  Through the challenge of problems in a number of fields of application, this course considers appropriate statistical models for explaining the patterns of variability of such multivariate data.  Topics include: multiple, partial and canonical correlation; multivariate regression; tests on means for one-sample and two-sample problems; profile analysis; test for covariances structure; multivariate ANOVA; principal components analysis; factor analysis; discriminant analysis and classification.  


Assessment: coursework (40%) and examination (60%)

STAT7006 Design and analysis of sample surveys (6 credits)

Inferring the characteristics of a population from those observed in a sample from that population is a situation often forced on us for economic, ethical or technological reasons. This course considers the basic principles, practice and design of sampling techniques to produce objective answers free from bias. This course will cover design and implementation of sample surveys and analysis of statistical data thus obtained. Survey design includes overall design, design of sampling schemes and questionnaires, etc. Sampling methods include sample size determination, sampling and non-sampling errors and biases, methods of estimation of parameters from survey data, imputation for missing data etc.


Assessment: coursework (50%) and examination (50%)

STAT7007 Categorical data analysis (3 credits)

Many social and medical studies, especially those involving questionnaires, contain large amounts of categorical data. Examples of categorical data include presence or absence of disease (yes / no), mode of transportation (bus, taxi, railway), attitude toward an issue (strongly disagree, disagree, agree, strongly agree). This course focuses on analyzing categorical response data with emphasis on hands-on training of analyzing real data using statistical software SAS. Consulting experience may be presented in the form of case studies. Topics include: classical treatments of contingency tables; measures of association; logistic linear models and log-linear models for binary responses; and log-linear models for Poisson means.


Assessment: coursework (50%) and examination (50%)

STAT7008 Programming for data science (6 credits)

Capturing and utilising essential information from big datasets poses both statistical and programming challenges. This course is designed to equip students with the fundamental computing skills required to use Python for addressing these challenges. The course will cover a range of topics, including programming syntax, files IO, object-oriented programming, scientific data processing and analysis, data visualization, data mining and web scraping, programming techniques for machine learning, deep learning, computer vision, and natural language processing, etc. 


Assessment: coursework (100%)

STAT7009 Stochastic dependence modelling (6 credits)

This course provides an introduction to the stochastic modelling of dependence, with computational highlights. Topics include univariate distribution functions, quantile functions, multivariate distribution functions, copulas, properties of copulas, measures of dependence, parametric and nonparametric copulas, simulation, applications, aspects of estimation and goodness-of-fit.


Assessment: coursework (40%) and examination (60%)  

STAT8000 Workshop on spreadsheet modelling and database management (3 credits)

This course aims to enhance students' IT knowledge and skills which are essential for career development of statistical and risk analysts. The course contains a series of computer hands-on workshops on Excel VBA programming, MS-Access and SQL and C++ basics.


Assessment: coursework (100%), assessment of this course is on a pass or fail or distinction basis

STAT8003 Time series forecasting (6 credits)

Discrete time series are integer-indexed sequences of random variables. Such series arise naturally in climatology, economics, finance, environmental research and many other disciplines. The course covers statistical modelling and forecasting of time series. Topics may include stationary and nonstationary time series, ARMA models, identification based on autocorrelation and partial autocorrelation, GARCH models, goodness-of-fit, forecasting, and nonlinear time series modelling.


Assessment: coursework (50%) and examination (50%)  

STAT8007 Statistical methods in economics and finance (6 credits)

This course provides a comprehensive introduction to state-of-the-art statistical techniques in economics and finance, with emphasis on their applications to time series and panel data sets in economics and finance. Topics include: regression with heteroscedastic and/or autocorrelated errors; instrumental variables and two stage least squares; panel time series model; unit root tests, co-integration, error correction models; and generalized method of moments.


Assessment: coursework (40%) and examination (60%)

STAT8015 Actuarial statistics (6 credits)

The main focus of this module will be on financial mathematics of compound interest with an introduction to life contingencies and statistical theory of risk.  Topics include simple and compound interest, annuities certain, yield rates, survival models and life tables, population studies, life annuities, assurances and premiums, reserves, joint life and last survivor statuses, multiple decrement tables, expenses, individual and collective risk theory.


Assessment: coursework (40%) and examination (60%)

STAT8016 Biostatistics (6 credits)

Statistical methodologies and applications in fields of medicine, clinical research, epidemiology, public health, biology and biomedical research are considered. The types of statistical problems encountered will be motivated by experimental data sets. Important topics include design and analysis of randomized clinical trials, group sequential designs and crossover trials; survival studies; diagnosis; risks; statistical analysis of the medical process.


Assessment: coursework (40%) and examination (60%)

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 STAT8089 Capstone Project or equivalent


Assessment: coursework (100%)

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, conjoint analysis and extracting insights from text data. Contents include statistical methods for segmentation, targeting and positioning, statistical methods for new product design, text mining techniques and market response models.


Assessment: coursework (50%) and examination (50%)

STAT8020 Quantitative strategies and algorithmic trading (6 credits)

Quantitative trading is a systematic investment approach that consists of identification of trading opportunities via statistical data analysis and implementation via computer algorithms. This course introduces various methodologies that are commonly employed in quantitative trading.

The first half of the course focuses at strategies and methodologies derived from the data snapshotted at daily or minute frequency. Some specific topics are: (1) techniques for trading trending and mean-reverting instruments, (2) statistical arbitrage and pairs trading, (3) detection of time-series” mean reversion or stationarity, (4) cross-sectional momentum and contrarian strategies, (5) back-testing methodologies and corresponding performance measures, and (6) Kelly formula, money and risk management. The second half of the course discusses statistical models of high frequency data and related trading strategies. Topics that planned to be covered are: (7) introduction of market microstructure, (8) stylized features and models of high frequency transaction prices, (9) limit order book models, (10) optimal execution and smart order routing algorithms, and (11) regulation and compliance issues in algorithmic trading.


Pre-requisites: Pass in STAT6013 Financial data analysis or equivalent


Assessment: coursework (50%) and examination (50%)

STAT8021 Big data analytics (6 credits)

The recent explosion of social media and the computerization of every aspect of life resulted in the creation of volumes of mostly unstructured data (big data): web logs, e-mails, videos, speech recordings, photographs, tweets and others. This course aims to provide students with knowledge and skills in big data analytics, especially natural language processing (NLP). Topics will include basic information retrieval, text classification, word embedding, neural networks, sequence models, encoder-decoder, transformer, contextualized world representation, and language model. Students are required to be familiar with Python language.


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


Assessment: coursework (100%)

STAT8088 Statistical Practicum (6 credits)

It provides students with first-hand experience in applying academic knowledge in a real-life work environment. To be eligible, students should be undertaking a statistics-related or risk-management-related practicum with no less than 160 hours in at least 20 working days spent in a paid or unpaid position. It is allowed for part-time students to complete their practicum within their current place of employment. The practicum will normally take place in the second semester or summer semester for full-time students or during the second year for part-time students.


Pre-requisites: Students should not be taking or have taken STAT8089 Capstone project


Assessment: Upon completion of the practicum, each student is required to submit a written report (60%) and to give an oral presentation (40%) on his/her practicum experience. Supervisors will assess the students based on their performance during the practicum period. Assessment of this course is on a Pass , Fail or Distinction basis with three criteria: (1) supervisor's evaluation, (2) written report, (3) oral presentation. Failing in fulfilling any of the three criteria satisfactorily leads to a "Fail" grade in the course.

STAT8089 Capstone project (6 credits)

This project-based course aims to provide students with capstone experience to work on a real-world problem and carry out a substantial data analysis project which requires integration of the knowledge they have learnt in the curriculum. Students will work in small groups under the guidance of their supervisor(s).  The project topic is not limited to academic context, but can also be extended to a community or corporate outreach project. Students will need to find an interesting topic of their own, conduct literature search regarding the most recent research related to the problem, make suggestions to improve the current situations or even solve the problem identified in their project.  A substantial written report is required.


Assessment: project proposal (15%);  written report (55%) and  oral presentation (30%)

STAT8300 Career development and communication workshop (Non-credit-bearing)

The course is specially designed for students who wish to sharpen their communication and career preparation skills through a variety of activities including lectures, skill-based workshops, small group discussion and role plays. All of which aim to facilitate students in making informed career choices, provide practical training to enrich communication, presentation, time management and advanced interview skills, and to enhance students' overall competitiveness in the employment markets.


Assessment: coursework (100%), assessment of this course is on a pass or fail or distinction basis

STAT8302 Structural equation modelling (3 credits)

Structural Equation Modelling (SEM) is a general statistical modelling technique to establish relationships among variables.  A key feature of SEM is that observed variables are understood to represent a small number of "latent constructs" that cannot be directly measured, only inferred from the observed measured variables.  This course covers the theories of structural equation models and their applications.  Topics may include path models, confirmatory factor analysis, structural equation models with latent variables, Sub-models including multiple group analysis, MIMIC model, second order factor analysis, two-wave model, and simplex model, model fitness, model identification, and Comparison with competing models.

Pre-requisites: Pass in STAT7005 Multivariate methods or equivalent

Assessment: coursework (50%) and examination (50%)

STAT8306 Statistical methods for network data (3 credits)

The six degrees 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:    coursework (100%)

STAT8308 Blockchain data analytics (3 credits)

In this course, we start by studying the basic architecture of a blockchain. Then we move on to several major applications including (but not limited to) cryptocurrencies, fintech and smart contracts. We conclude by examining the cybersecurity issues facing the blockchain ecosystems.


Assessment: coursework (100%)

STAT8309 Monte carlo simulation and finance (3 credits)

This course covers basic knowledge of Monte Carlo simulation and its application to finance. Course contents include random variate generation, Monte Carlo methods, options pricing, variance reduction techniques and efficiency improvement.


Assessment: coursework (50%) and examination (50%)


Programme Director

Dr. Olivia T.K. CHOI

Dr. Olivia T.K. CHOI
BSc UBC; MSc Oxon; PhD ISM
Department of Statistics and Actuarial Science



Staff List

  • Professor T J Boonen, BSc, MSc, PhD Tilburg
  • Professor Y Cao, BS Fudan; MS, PhD Princeton
  • Professor K C Cheung, BSc(ActuSc), PhD HK; ASA
  • Dr O T K Choi, BSc UBC; MSc Oxon; PhD ISM 
  • Professor L Feng, BS Renmin U; PhD Rutgers
  • Professor E C H Fong, BA, MEng Cantab; DPhil Oxon 
  • Professor Y Gu, BSc USTC; PhD N Carolina 
  • Professor K Han, PhD HK
  • Professor M Hofert, MSc Syracuse; Dipl.-Math. oec., Dr. rer. nat. Ulm
  • Dr C W Kwan, BSc, PhD HK
  • Professor E K F Lam, BA St. Thomas; MA New Brunswick; PhD HK
  • Dr A S M Lau, BEng City; MSc HK; PhD CUHK
  • Dr D Lee, BSc(ActuSc), MPhil HK; PhD British Columbia, ASA
  • Professor S M S Lee, BA, PhD Cantab
  • Dr E A L Li, BSc HK; MEcon, PhD Syd
  • Professor G D Li, BSc MSc Peking; PhD HK
  • Professor W Y Li, BSc, BEc, MEc SWUFE; PhD Waterloo 
  • Professor L Q Qu, BEng CSU; PhD UCAS; CityU 
  • Professor C Wang, PhD NUS
  • Dr K P Wat, BSc(ActuSc), PhD HK; SFHEA; FSA; FASHK; CERA; FRM
  • Professor L Q Yu, BEng ZJU; PhD CUHK
  • Professor K C Yuen, BSc, MSc, PhD Calgary; ASA
  • Dr C Y Zhang, PhD HK
  • Professor D Y Zhang, BSc Nankai; MSc, PhD NCSU
  • Professor M M Y Zhang, BS UCSB; MS, PhD UT Austin
  • Dr Z Q Zhang, BSc Nankai, MSc E China Normal; PhD HK
  • Professor K Zhu, BSc, USTC; PhD HKUST
  • Dr H Y Y Cheung, BSc UCL; MSc Imperial College London


Graduate/ Student Sharing

Jiada Jedi HUANG

Class of July 2022


Wing Ho Ronald CHAN

Wing Ho Ronald CHAN

Class of 2022


“MStat program offers a wide variety of statistical course for students to choose, from conventional statistical inference and modelling to more heated topics such as data mining and blockchain analysis. The data mining and big data analytics courses I took were not only inspirational from the theoretical perspective, but also helpful by offering hands-on experience useful for my everyday work as an actuary. With the adoption of new accounting standards in the industry, revamp of existing data structure and models is essential. The techniques and mindset of big data analytics learnt from the program can certainly be put to good use. The program serves as a great opportunity for students to develop the necessary skills under the big data era.”

Cheuk Wing YIU

Cheuk Wing YIU


"I have worked in the field of official statistics for several years. The depth and breadth of the MStat program, both in its structure and content, are truly unique. The program provides a valuable platform to further my cutting-edge knowledge across diverse domains of Statistics. Its comprehensive curriculum strikes a very good balance between a strong theoretical foundation and practical programming training. This combination enhances my understanding of new methodologies and hands-on data analytical skills, which are advantageous for developing and organizing a wide range of statistical projects in my professional endeavors, particularly in rapidly evolving areas such as big data and machine learning."




“The esteemed MStat program has played an instrumental role in shaping my career. MStat program provided me with a comprehensive education in statistical theory and applications, which have been beneficial in my actuarial career. One of the standout features of the program was the exceptional professors who possess extensive knowledge and expertise in their respective fields. The program also provided numerous opportunities for hands-on experience through practical projects and internships, during the study, I applied for an EY internship position through our programme’s internship/job online application system, interviewed, and received offer. I am immensely grateful for the exceptional education and experience I gained through MStat at HKU. It is a programme that I would highly recommend to anyone seeking a comprehensive and rigorous education in statistics.”




Department of Statistics & Actuarial Science

The University of Hong Kong

Faculty of Science

The University of Hong Kong

  • G/F Chong Yuet Ming Physics Building
  • (852) 3910 3319
  • (852) 2858 4620