The Hong Kong University of Science and Technology (Guangzhou)
MPhil / PhD in Data Science and Analytics
Guangzhou, China
PhD
DURATION
8 years
LANGUAGES
English
PACE
Full time, Part time
APPLICATION DEADLINE
15 Jun 2026*
EARLIEST START DATE
TUITION FEES
CNY 40,000 / per year **
STUDY FORMAT
On-Campus
* for international students | 15 Jul 2026 - for Chinese students
** for full-time students | CNY 150,000 - for part-time students
Key Summary
In the digital era, following advancements made in innovative technologies, data handling is growing at an unprecedented pace. The data-driven world opens tremendous possibilities and opportunities for companies and businesses across all industries, as they can make use of the data to create value for their business. As a disruptive consequence of the digital revolution, data science and analytics have become an emerging and cross-disciplinary field that requires knowledge and skills in many areas, such as computer science, statistics, and mathematics.
The Master of Philosophy (MPhil) and Doctor of Philosophy (PhD) Programs in Data Science and Analytics aim to facilitate close integration of statistical analytics, logical reasoning, and computational intelligence in the study of data processing and analytics. The programs will provide rigorous research training that prepares students to become knowledgeable researchers who are conversant in applying logic, mathematics, algorithms, and computing power in the process of examining and analyzing data in academia or industry so as to derive valuable insights for making better decisions.
The MPhil Program aims to expose students to issues involved in the development of scientific, educational and commercial applications of data science and analytics. A graduate of the MPhil program should demonstrate a good working knowledge of issues in the discipline. He or she should be capable of synthesizing and creating new knowledge, making contribution to the field.
The PhD Program aims to develop the skills needed for students to identify theoretical research issues related to practical applications, formulate and undertake research that addresses issues identified, and independently find a data science and analytics-related solution. A PhD graduate is expected to demonstrate mastery of knowledge in the discipline and to synthesize and create new knowledge, making an original and substantial scientific contribution to the discipline.
- Studentship for full-time PhD students: CNY 180,000 per year without additional application
Cross-disciplinary Core Courses
- Cross-disciplinary Research Methods I
- Cross-disciplinary Research Methods II
- Cross-disciplinary Design Thinking I
- Cross-disciplinary Design Thinking II
- Project-driven Collaborative Design Thinking
Hub Core Courses
Students are required to complete at least one Hub core course from the Information Hub and at least one Hub core course from other Hub.
Information Hub Core Course
- Information Science and Technology: Essentials and Trends
Other Hub Core Courses
- Introduction to Function Hub for a Sustainable Future
- Technological Innovation and Social Entrepreneurship
- Model-Based Systems Engineering
Courses on Domain Knowledge
Under this requirement, each student is required to take one required course and other electives to form an individualized curriculum relevant to the cross-disciplinary thesis research. Only one Independent Study course may be used to satisfy the course requirements. To ensure that students will take appropriate courses to equip them with needed domain knowledge, each student has a Program Planning cum Thesis Supervision Committee to approve the courses to be taken soonest after program commencement and no later than the end of the first year. Depending on the approved curriculum, individual students may be required to complete additional credits beyond the minimal credit requirements.
Required Course List
- Data Mining and Knowledge Discovery in Data Science
Sample Elective Course List
- Automatic Machine Learning
- Deep Learning in Data Science
- Advanced Database Management for Data Science
- Advanced Machine Learning
- Parallel Programming for Data Science and Analytics
- Foundation of Data Science and Analytics
- Data Science Computing
- Data Analysis and Privacy Protection in Blockchain
- Data Exploration and Visualization
- Spatio-Temporal Data Analysis
- Introduction to Graph Learning
- Special Topics
- Independent Study
- Computer Vision and Its Applications
- Convex and Nonconvex Optimization I
Graduate Teaching Assistant Training
- Introduction to Teaching and Learning in Higher Education
Professional Development Course Requirement
- Professional Development for Research Postgraduate Students
- Career Development for Information Hub Students
English Language Requirement
- Foundation in Listening & Speaking for Postgraduate Students
- Communicating Research in English
Postgraduate Seminar
- Data Science and Analytics Program Seminar I
- Data Science and Analytics Program Seminar II
Thesis Research
- MPhil Thesis Research
- Doctoral Thesis Research
On successful completion of the MPhil program, graduates will be able to:
- Demonstrate critical thinking and analytical skills essential for solving real data science problems;
- Apply a range of qualitative and quantitative research methods for data science and analytics; and
- Translate and transform advanced research techniques effectively into data science practice in academic fields or industry.
On successful completion of the PhD program, graduates will be able to:
- Identify scientific and engineering correlations, significances, and insights in new data science and analytics models, algorithms, tools, principles, frameworks, solutions, and techniques;
- Demonstrate critical thinking and analytical skills from the perspective of data science and analytics;
- Apply a range of qualitative and quantitative research methods for data science and analytics;
- Translate and transform fundamental research insights effectively into data science practice in academic fields and industry;
- Exercise independent thinking and demonstrate effective communication skills in presenting and publishing scientific findings; and
- Conduct original research independently and competently, showing in-depth knowledge in the field of data science and analytics.