Curriculum

Students studying for MSc* award must complete:

(Option 1): 6 compulsory subjects (18 credits) and 4 elective subjects (12 credits); OR

(Option 2): 6 compulsory subjects (18 credits), 1 elective subject (3 credits) and a dissertation (9 credits)

Students who opted for the dissertation should have completed 6 compulsory subjects with good academic results. Normally, only students who have completed 6 compulsory subjects (18 credits) with GPA 3.0 or above at the end of Semester 2 will be considered for (Option 2).

*Students who do not complete the programme but have passed 6 compulsory subjects (18 credits) and 1 elective (3 credits) will be awarded a postgraduate diploma.

6 Compulsory subjects (18 credits):
  • Subject Code Subject Name
  • AMA505 Optimization Methods
  • AMA563 Principles of Data Science
  • AMA564 Deep Learning
  • AMA565 Advanced High Dimensional Data Analysis
  • COMP5434 Big Data Computing
  • COMP5112 Data Structures and Database Systems

 

Elective subjects (Each subject carries 3 credits):
    • Subject Code Subject Name
    • AMA502 Operations Research Methods
    • AMA506 Graphs and Networks
    • AMA507 Mathematical Modelling for Science and Technology
    • AMA514A Applied Linear Models
    • AMA515A Forecasting and Applied Time Series Analysis
    • AMA523 Optimal Control with Management Science Applications
    • AMA524 Scientific Computing
    • AMA527 Decision Analysis
    • AMA528 Probability and Stochastic Models
    • AMA529 Statistical Inference
    • AMA531 Loss Models and Risk Analysis
    • AMA532 Investment Science
    • AMA541 Simulation and Risk Analysis
    • AMA542 Advanced Operations Research Methods
    • AMA544 Multi‐criteria Optimization
    • AMA546 Statistical Data Mining
    • AMA566 Advanced Topics in High Frequency Trading
    • AMA592 Dissertation
    • COMP5152 Advanced Data Analytics
    • COMP5511 Artificial Intelligence Concepts
 
Continuing Education Fund (CEF)
The list of Reimbursable Courses for the purpose of Continuing Education Fund is as follows. Note that (i) These courses have been included in the list of reimbursable courses under the Continuing Education Fund (這些課程已加入持續進修基金可獲發還款項課程名單內) AND (ii) These courses / The mother course (The Master of Science in Data Science and Analytics) of this module are recognised under the Qualifications Framework (QF Level[6]) (這些課程 / 本單元所屬之主體課程(數據科學及分析理學碩士)在資歷架構下獲得認可(資歷架構第[6]級).
No. CEF Course Code. HKCAA Ref. Sector Institution Course Title
1 42Z129569 POLYU/0324 Science The Hong Kong Polytechnic University-Department of Applied Mathematics Optimization Methods
[Module from Master of Science in Data Science and Analytics]
2 42Z129577 POLYU/0325 Science The Hong Kong Polytechnic University-Department of Applied Mathematics Principles of Data Science
[Module from Master of Science in Data Science and Analytics]
3 42Z129585 POLYU/0326 Science The Hong Kong Polytechnic University-Department of Applied Mathematics Deep Learning
[Module from Master of Science in Data Science and Analytics]
4 42Z129593 POLYU/0327 Science The Hong Kong Polytechnic University-Department of Applied Mathematics Advanced High Dimensional Data Analysis
[Module from Master of Science in Data Science and Analytics]

Students of these CEF-reimbursable courses may apply for subsidies from the government via https://www.wfsfaa.gov.hk/cef/en/index.htm

 

Subjects

List of credit-based subjects in the academic programmes offered by the Department.

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