Centre for Data Science


Follow the general admission rules and requirements of the University.

Priority will be given to applicants with relevant majors or qualifications.

  • Specialization A: Artificial Intelligence Applications

1)   GPA 2.8 (75 out of 100) or above in the Bachelor’s degree studies;

2)   Bachelor’s degree related to computer science, engineering or mathematics is preferred, but not necessary;

3)   English proficiency should be in line with that stipulated in the UM Admission Guidelines Governing Master’s Degree and Postgraduate Certificate/Diploma Programmes.

  • Specialization B: Big Data and Analytics in Marketing

1)     Applicants should have achieved the equivalent* of an overall result of Grade C+ or better in the Bachelor’s degree studies. (*The equivalent means 2.3 on the 4.0 GPA scale, 14 on the 20-point scale or 70 out of 100.)

2)     Interview performance.

  • Specialization C: Financial Technology

1)     Applicants should have achieved the equivalent* of GPA 2.8 or above in the Bachelor’s degree studies (*The equivalent means 75-77 out of 100.)

2)     Interview performance.

  • Specialization D: Data Strategy and Compliance

1)     Applicants must possess a Bachelor degree satisfying the Admission Requirements for Master’s Degree Programmes under the relevant guidelines and regulations of the University of Macau.

2)     Applicants are subject to the English proficiency requirements as stipulated in the UM Admission Guidelines Governing Master’s Degree and Postgraduate Certificate/Diploma Programmes.

3)     Applicants who graduated from Mainland China should hold a Bachelor degree issued by any University recognized by the Ministry of Education.

4)     Admission by merit and the graduates of Categories 211 and 985 universities will be given priority.

5)     In compliance with the Admission Rules Governing Master’s Degree and Postgraduate Certificate / Diploma Programmes and General Rules Governing Master Degree and Postgraduate Certificate / Diploma Programmes.

  • Specialization E: Precision Medicine

1)     Same as the general admission requirements for Master’s Degree / Postgraduate Certificate Programmes

2)     No additional prerequisites on education background

No need to have any relevant background on biology or biomedical sciences

No need to have any relevant background on computer sciences

  • Specialization F: Second Language Studies

1)     Bachelor’s degree in English or Chinese, or a related subject from a recognized university.

2)     Familiarity with both linguistics (either Chinese or English) and technology background would be considered first.

3)     For non-native Chinese who target at Chinese: at least with HSK Level VI Certificate (National Chinese exam, higher level) or equivalent;

4)     Below is the GPA and English Language Proficiency requirement for application:

  AreaGPATOEFL iBTIELTSTEM8TEM4CET6 [percentile]GEPT
English2.7926.5GoodGood610High-intermediate
Chinese2.7806.0PassPass530High-intermediate
  • Specialization G: Intelligent Education

1)     Applicants who have passed the first round of the selection process might be required to attend an interview.

2)     For applicants who are unable to submit their proof of English proficiency, the assessment will be made during the interview.

A priority will be given to education degree holders in related disciplines such as Education, Psychology.


FUNDAMENTAL COURSES IN 2019-2020

Master of Science in Data Science

FST offers 4 fundamental data science courses which enable students to:

-use programming language to process data, produce visualizations, and interpret these visualizations;

-learn the database and data mining concepts and techniques for big data analytics and development in different domains;

-learn the significance of data visualization in data science and  big data analytics, and develop knowledge and skills to present quantitative data using data visualization tools;

-basic deep learning approaches; and

-Bayesian Networks; and

-CNN (convolutional neural networks); and

-representation learning and reinforcement learning.