MSc in Data Science and Analytics
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.
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 |
| 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 |
| AMA567 | Quantum Computing for Data Science |
| AMA592 | Dissertation |
| COMP5152 | Advanced Data Analytics |
| COMP5511 | Artificial Intelligence Concepts |
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 Title1 |
|---|---|---|---|---|---|
| 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
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