Master of Statistics (2019 Admission)

Curriculum for part-time study

Commencing in September, the curriculum is composed of a total of 60 credits of courses in 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 or Data Analytics. 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.

MStat Curriculum

Two compulsory courses (12 credits):

STAT7003 Foundations of statistics (6 credits)
STAT7004 Linear modelling (6 credits)

Students with prior background may replace each course with a more advanced course from the same area:

REPLACE�K
STAT7003 Foundations of statistics (6 credits)
STAT7004 Linear modelling (6 credits)

WITH�K
STAT7005 Multivariate methods (6 credits)
STAT6014 Advanced statistical modelling (6 credits)

Theme-specific elective courses (24 Credits):

Risk Management theme

plus 24 credits from:

STAT6013 Financial data analysis (6 credits)
STAT6015 Advanced quantitative risk management and finance (6 credits)
STAT6017 Operational risk and insurance analytics (6 credits)
STAT8003 Time series forecasting (6 credits)
STAT8007 Statistical methods in economics and finance (6 credits)
STAT8014 Risk management and Basel Accords (6 credits)
STAT8015 Actuarial statistics (6 credits)
STAT8017 Data mining techniques (6 credits)
STAT8020 Quantitative strategies and algorithmic trading (6 credits)
STAT8021 Big data analytics (6 credits)
STAT8305 Bayesian statistics (3 credits)
Data Analytics theme

plus 24 credits from:

STAT6011 Computational statistics (6 credits)
STAT6014 Advanced statistical modelling (6 credits)
STAT6016 Spatial data analysis (6 credits)
STAT7005 Multivariate methods (6 credits)
STAT7007 Categorical data analysis (6 credits)
STAT7008 Programming for data science (6 credits)
STAT8003 Time series forecasting (6 credits)
STAT8016 Biostatistics (6 credits)
STAT8017 Data mining techniques (6 credits)
STAT8019 Marketing analytics (6 credits)
STAT8021 Big data analytics (6 credits)
STAT8302 Structural equation modelling (3 credits)
STAT8305 Bayesian statistics (3 credits)
STAT8306 Statistical methods for network data (3 credits)
STAT8307 Text analytics (3 credits)

Other elective courses (18 credits)

plus at least 18 credits from:

STAT6008 Advanced statistical inference (6 credits)
STAT6009 Research methods in statistics (6 credits)
STAT6010 Advanced probability (6 credits)
STAT7006 Design and analysis of sample surveys (6 credits)
STAT7301 Socio-economic statistics for business and public policies (3 credits)
STAT8000 Workshop on spreadsheet modelling and database management (3 credits)
STAT8300 Career development and communication workshop (3 credits)
STAT8304 Current topics in Statistics (3 credits)

Any theme-specific elective courses
Any capstone courses

Capstone requirement (6 credits)

plus 6 credits from

STAT8002 Project (6 credits)
STAT8017 Data mining techniques (6 credits)
STAT8088 Practicum (6 credits)
STAT8089 Capstone project (6 credits)

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.