Master of Science in Data-Centric Artificial Intelligence Technology
Guangzhou, China
MSc
DURATION
3 years
LANGUAGES
English
PACE
Full time, Part time
APPLICATION DEADLINE
15 Jun 2026*
EARLIEST START DATE
TUITION FEES
CNY 398,000
STUDY FORMAT
On-Campus
* for international students | 15 Jul 2026 - for Chinese students
Key Summary
The Master of Science (MSc) Program in Data-Centric Artificial Intelligence Technology is an elite program providing students with unparalleled academic experience through the interplay of advanced domain knowledge and practice in big data and AI. Not only will the program teach students the state-of-the-art knowledge in big data and AI through courses delivered by world-class faculty of the University, but it will also offer students opportunities to work on industry-level independent projects. In addition, students will be exposed to real-world industry problems and trained with hands-on experience and an essential skillset to apply the cutting-edge knowledge learnt in the courses to tackle the contemporary technology problems facing the industry in the one-year mandatory internship.
Along with the exponential growth in volume and availability of data arising from various forms of new innovations and technologies like 5G, Internet of Things (IoT), mobile devices, etc., enterprises are seeking to leverage the power of data and analysis for driving their businesses strategies and operations, leading to continuous growing demands for highly qualified professionals in the field of data science and AI. This program aims to educate students with academic literacy in big data and AI, as well as provide students with hands-on experience to work on independent projects and internships in the industry. The program enables students to apply learning to practice, empowering them to be skilled technology leaders and successful big data and AI professionals in the fast-changing business environment.
Application-based scholarship of CNY 10,000
Required Courses
- Industry Round Table
- Practical Lab Course
Elective Courses
- Data Mining and Knowledge Discovery in Data Science
- Deep Learning in Data Science
- Advanced Database Management for Data Science
- Advanced Machine Learning
- Foundation of Data Science and Analytics
- Data Science Computing
Independent Project
- Independent Project
Students are required to complete a six-month independent project on real-world industry problems. Each independent project will be supervised by one academic faculty member. The topics of the projects will come from industry, with a focus on data analytics. Full-time students are expected to complete the project in the second term of their first academic year. Part-time students are expected to complete the project in the second term of their second academic year.
Internship
- Industry Internship I
- Industry Internship II
- Industry Internship III
Students are required to participate in a year-long internship in the industry arranged by the Program Office. Full-time students are expected to complete the internship in the second year of their studies. Part-time students are expected to complete the internship in the third year of their studies. Each internship will be supervised by one academic faculty member and one industry faculty member as a pair.
The internship consists of three parts. Students are required to pass oral examinations consisting of one Open Topic report, one Intermediate report, and one Final report. Each oral presentation should normally take approximately 2 hours. The Open Topic will be examined based on the scientific value, feasibility and technical challenges of the proposed topic; the Intermediate report will assess the student’s progress and industry collaboration progress; the Final report will examine the final output from the internship project and whether the student indeed knows how to apply AI techniques to concrete data science applications.
On successful completion of the program, graduates will be able to:
- Develop advanced knowledge and understanding of state-of-the-art big data and AI technologies and analysis methods, such as Data Mining, AI/ Deep Learning, Data Science and Engineering (Database, Hadoop, HDFS, MLOps), Algorithms, Probabilistic Graphical Models (GNN);
- Perform various industry data analytics tasks using big data, AI, and computing techniques;
- Exercise independent thinking and demonstrate critical analytical skills essential from the perspective of big data and AI;
- Investigate existing problems in big data and AI and conduct original big data and AI research independently with in-depth knowledge and practical experience to solve the complex problems in industry; and
- Apply a range of big data and AI knowledge and techniques effectively in practice in the academic field and industry for robust data analytics and applications.