Master of Data Science - Faculty of Data Science

Master of Data Science Programme

Introduction

1. Programme background

The Master of Data Science (Chinese) is set up according to the trends and needs of international, national and regional development. Its professional scope belongs to data science, computer science, artificial intelligence, database, etc. The goal is to cultivate outstanding students with a solid theoretical foundation A new generation of high-level, compound talents with innovative capabilities in data analysis and processing. City University of Macau already has a good foundation and extensive resources in the field of data science. In 2016, it established the "Urban Governance Big Data Research Center" and started to carry out "three-dimensional urban big data platform", "smart transportation", "smart tourism", " Scientific research such as "Smart Building" has formed a core academic group that is interdisciplinary and collaborative.

2. Cultivation Objectives

Through professional knowledge learning, research practice, paper writing, etc., students master the basic knowledge and professional skills of data analysis, management, and application, and have the ability to use data analysis methods to solve problems in specific fields. In the future, students can engage in industry data analysis and management, professional research, etc. Research contents include Data processing technology, Urban data processing, Data mining and analysis, Artificial intelligence data processing, etc. In order to realize the concept of cultivating a new generation of high-level talents in compound data analysis and processing with a solid theoretical foundation and excellent innovative capabilities, the Master of Data Science Programme has set the following goals:

1. In view of the current situation of the industry and the development requirements of the times, based on solid theory and oriented by practical value, we cultivate high-level and compound talents needed by the times.

2. Adhere to cross-disciplinary learning and case study teaching, and through exchanges and co-operation with the industry, we make use of the rich social resources to cultivate enterprise management and technical talents with a big data mindset.

3. On the basis of adhering to the combination of theory and practice, business and technology, it focuses on cultivating students' data insight, prediction, analysis and problem-solving abilities.

3. Programme Study Plan

Table 1 Basic compulsory courses

Course	Type	Class Time	Credit
Introduction to Data Science	Compulsory	45	3
Time Series Analytics	Compulsory	45	3

Table 2 Core Compulsory majors

Course	Type	Class Time	Credit
Hadoop Fundamentals for Big Data Processing and Applications	Compulsory	45	3

Table 3 Elective courses (take six courses from the following courses)

Course	Type	Class Time	Credit
Data Analytics and Statistical Inference	Elective	45	3
An Introduction to Big Data Programming	Elective	45	3
Cloud Computing and Big Data Analytics	Elective	45	3
Parallel Computing	Elective	45	3
Artificial Neural Networks	Elective	45	3
Big Data Analytics and Applications	Elective	45	3
Special Topic for Education Big Data	Elective	45	3
Special Topic for Financial Market Data Analytics and Data Mining	Elective	45	3
Special Topic for the Accurate Marketing of the Era of Big Data	Elective	45	3

Table 4 Others

Course	Type	Class Time	Credit
Special Academic Topics	Compulsory	N/A	1

Table 5 Dissertation Writing

Course	Type	Class Time	Credit
Thesis Writing	Compulsory	N/A	9

Illustration

1. During their programme, students must complete the basic and core compulsory credits, elective credits and various training requirements specified in the programme, and obtain 37 credits. The specific distribution is as follows: a total of 6 credits for the basic compulsory courses in Table 1; the core compulsory courses in Table 2. A total of 3 credits; a total of 9 elective courses in Table 3, students choose 6 from them, a total of 18 credits; participate in academic activities recognized by the academic committee of the faculty at least 5 times, a total of 1 credit; thesis writing total 9 credits.

2. The specific course arrangement in the student's personal training plan is determined by the supervisor and the student based on the professional scope and research direction.

3. After completing the compulsory and elective courses, student can start writing thesis, project report and proposal reports. After the proposal report is approved, student can start writing thesis and project design report.

4. Students must publish or have at least one paper published or accepted in an academic journal recognized by City University of Macau during their master’s degree programme; (Updated on September 1, 2023)

Publish as first author or *second author or corresponding author in the following list:

SCI Journal or
EI Journal or
EI conference papers (included in IEEE Xplore, ACM, Springer) 1 article or
Peking University Chinese Core N/Q, T/X Comprehensive Science and Technology refers to 120 Journals or
The Computer Federation of China recommends international academic conferences and journals, and the Computer Federation of China recommends a directory of Chinese science and technology journals.
Obtained an international or domestic invention patent copyright while studying

Note:

a. If the student is the second author, the supervisor must be the first author;

b. All published articles must be related to the research of the dissertation, also have City University of Macau as the first author, and be published publicly;

c. Students must submit their acceptance certificate or email of article acceptance, and the faculty will review the publication of relevant graduation requirements;

d. The above are the basic requirements of this faculty.

5. If student pass the dissertation oral defense, obtain a total of 37 credits, and do not violate the relevant regulations of the university, student can apply for the award of requirements degree in accordance with the master application procedures of the City University of Macau.

	Study Plan Course Framework	Programme Credit
	Basic compulsory courses	6
	Core compulsory for majors	3
	Electives	18
	Academic activities	1
	Thesis	9
	Total credits	37

Note: The teaching language is Chinese/English

4. Teaching requirements

Students are required to complete 3 compulsory courses and 6 elective courses during the study period and sit for the examinations. and those who pass the courses will be awarded credits for the courses, totaling 27 credits. Participate in academic activities and special research recognized by the academic committee of the faculty at least 5 times, totaling 1 credit.

After completing the required and elective courses, student can start writing the proposal report and graduation thesis. If student pass the dissertation oral defense, obtain a total of 37 credits, and do not violate the relevant regulations of the university, student can apply for the award of master degree in accordance with the master application procedures of the City University of Macau.

5. Admission criteria

Applicants must have a bachelor's degree or equivalent. Priority will be given to those with pre-requisite academic qualifications in computer science, data analysis, data management, information science, mathematics, statistics, machine learning, statistics, machine learning, data mining, database and other fields and related majors. Applicants are required to take the entrance examination organized by City University of Macau and will be admitted on the basis of merit.

2024 Data Science Graduate Admissions Rules

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