Faculty of Science
CODE
63027
SUBCODE
63027-DFM-DPM
ENTRY

Sept 2024 Entry

STUDY MODE
Mixed Mode
DURATION

1.5 years (Full-time)
3 years (Part-time)

CREDIT REQUIRED

30

FUND TYPE
Self-Financed

Non-local applicants must be registered as full-time students.

Application Deadline
Local - Mixed Mode
Sept 2024 Entry - 30-Apr-2024
Non-local - Mixed Mode
Sept 2024 Entry - 30-Apr-2024
About Programme
How to Apply
Aims & Characteristics

Programme Aims

In today’s era of big data, large data sets are generated every day in various areas of society and industry, from social networking to finance. It is challenging to extract and analyse information from such an unprecedentedly large volume of data. To create value from such data, one must combine techniques from mathematics, statistics and computer science. This programme nurtures graduates with expertise across the core disciplines of mathematics, statistics and computer science. It develops students’ analytical and critical thinking as well as their problem-solving skills. This enables graduates to pursue careers as data analysts in various industries, such as finance and information technology.

 

Characteristics

Data science and analytics involve the use of mathematical, statistical and computing techniques to extract useful information from large-scale data and make decisions accordingly. Statistics, optimisation methods and computer science are widely acknowledged to form the three pillars of modern data science. This programme is designed to provide a balanced treatment of these three pillars, with the aim of cultivating future data analysts.

 

Graduates with highly developed mathematical, statistical and computing skills are in great demand globally, in both industry and academic settings.

Curriculum

Programme Structure

Students studying for the 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 opt 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 a GPA of 3.0 or above at the end of Semester Two of their first year will be considered for Option 2.

 

For details of the programme structure, please refer to https://www.polyu.edu.hk/ama/study/pg/master-data-science-and-analytics/.

 

Core Areas of Study

6 Compulsory Subjects (18 credits)

  • Big Data Computing
  • Data Structures and Database Systems
  • Deep Learning
  • Optimisation Methods
  • Principles of Data Science
  • Statistical Data Mining


Elective Subjects (3 credits each)

  • Advanced Data Analytics
  • Advanced High Dimensional Data Analysis
  • Advanced Operations Research Methods
  • Advanced Topics in Quantitative Finance
  • Applied Linear Models
  • Artificial Intelligence Concepts
  • Decision Analysis
  • Forecasting and Applied Time Series Analysis
  • Graphs and Networks
  • Investment Science
  • Loss Models and Risk Analysis
  • Mathematical Modelling for Science and Technology
  • Multi‐criteria Optimisation
  • Natural Language Processing
  • Operations Research Methods
  • Optimal Control with Management Science Applications
  • Probability and Stochastic Models
  • Quantum Computing for Data Science 
  • Scientific Computing
  • Simulation and Risk Analysis
  • Statistical Inference


Information on the subjects offered can be obtained at https://www.polyu.edu.hk/ama/study/pg/master-data-science-and-analytics/curriculum/.

Credit Required for Graduation

30

Programme Leader(s)

Programme Leader
Dr Jiang Binyan
BSc, PhD

Deputy Programme Leader
Dr Zhang Zaikun
BSc, PhD

Assistant Programme Leader
Dr Li Ting
BSc, MPhil, PhD

Subject Area
Data Science and Analytics
Entrance Requirements
  • A Bachelor’s degree with Honours in mathematics, statistics, computer science, IT, engineering, science or another relevant subject. Applicants with a Bachelor’s degree in another discipline and an adequate background in mathematics or IT will also be considered.


If you are not a native speaker of English, and your Bachelor's degree or equivalent qualification is awarded by institutions where the medium of instruction is not English, you are expected to fulfil the University’s minimum English language requirement for admission purpose. Please refer to the "Admission Requirements" section for details.

Enquiries

For further information, please contact:
Dr Jiang Binyan, Programme Leader
(tel: (852) 2766 6349; email: by.jiang@polyu.edu.hk);
or Dr Zhang Zaikun, Deputy Programme Leader
(tel: (852) 2766 4592; email: zaikun.zhang@polyu.edu.hk);
or Dr Li Ting, Assistant Programme Leader
(tel: (852) 2766 4032; email: tingeric.li@polyu.edu.hk);
or Ms Elki Wong, Programme Secretary
(tel: (852) 3400 3747; email: ka-ying-elki.wong@polyu.edu.hk).

Initial Registration Credits

3 for local students
9 for non-local students

Tuition Fee

HK$10,850 per credit for local and non-local students

Student Message

I feel so glad and lucky to be a graduate of the MSc in Data Science and Analytics at PolyU. This programme is very practical and well suited to those who want to work as data analysts, data scientists or even AI engineers. The programme is well-designed and includes subjects such as mathematics, machine learning, and computer science, which help students develop in-depth theoretical knowledge and reinforce their learning through lab work and other hands-on exercises. The richness and diversity of the teaching content facilitate students’ exploration of other areas of interest, potentially increasing work opportunities. In addition, the professors are very patient and conscientious and provide lots of assistance on our coursework. I believe that this programme is a good choice if you aspire to work in the tech industry.

LIAO Chaoqun

I graduated from the MSc in Data Science and Analytics offered by the Department of Applied Mathematics (AMA) in 2022. Now I am a research postgraduate student at the Hong Kong University of Science and Technology (Guangzhou). AMA’s Data Science and Analytics programme provides comprehensive coverage of artificial intelligence and statistics, which are closely related to interdisciplinary areas such as bioengineering, financial engineering and pharmaceutical engineering. The subjects learned in our programme are highly relevant and applicable to all of these fields. Because some subjects are co-taught by AMA and the Department of Computing, they provide a good balance between mathematical theory and actuarial practice. Most of my classmates studied mathematics or computing at undergraduate level. Therefore, we learned from each other and made progress together during our learning. After completing this programme, I had a very solid foundation in statistics and I was ready to start materialising most deep learning algorithms.

LI Jiawei
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