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School of Computing and Data Science
Master of Statistics
MStat | Full-time & Part-time
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Programme Highlights:
Expected Duration
Expected Duration
1 year
Fees
Fees
HK$228,000
Expected Programme Start Date
Expected Programme Start Date
September 2025
Application Deadline
Application Deadline
Round 1 (Main):
12:00 noon (GMT +8), November 18, 2024
Round 2 (Clearing):
12:00 noon (GMT +8), January 13, 2025
Description
Description
Full Time (one year, including daytime, evenings and Saturdays)
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Expected Duration
Expected Duration
2 years
Fees
Fees
HK$228,000
Expected Programme Start Date
Expected Programme Start Date
September 2025
Application Deadline
Application Deadline
Round 1 (Main):
12:00 noon (GMT +8), November 18, 2024
Round 2 (Clearing):
12:00 noon (GMT +8), January 13, 2025
Description
Description
Part Time (two years, including evenings and Saturdays)
Apply Now

Overview

The programme is designed to provide a rigorous training in the principles and the practice of statistics. It emphasizes in applications and aims to prepare candidates for further study, research, consulting work and administration in various fields through computer-aided and hands-on experience. Candidates should have knowledge of matrices and calculus, introductory statistics and linear modelling.

The programme offers great flexibilities for students who wish to take a general approach, or a specialized theme in Risk Management, Data Analytics, or Financial Statistics. For a specialized theme, there are core courses and elective courses for students to choose from.

Students joining the programme are expected to come from a wide range of disciplines. The programme is recommended for those at work to be part-time students and will benefit from interaction with classmates of similar or different backgrounds as well as for fresh graduates to be full-time students to get a postgraduate degree before working.

Expected graduation period for normal course of studies

Full Time: Summer (July 2026)
Part Time: Summer (July 2027)

Entrance Requirements

1. A Bachelor's degree or an equivalent qualification; and

2. Applicants should have knowledge of matrices and calculus, introductory statistics and linear modelling.

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For additional information, we recommend reviewing the following documents:

Additional Information
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Regulations & Syllabuses
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Supporting Documents
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Faculty

School of Computing and Data Science
To meet the growing importance of computing and AI literacy in the 21st century, HKU recognises the need to equip students with the necessary skills to succeed in an increasingly technology-driven world. The newly established School of Computing and Data Science (SCDS) at The University of Hong Kong encompasses the existing Department of Computer Science and the Department of Statistics and Actuarial Science. We have about 60 renowned scientists and 800 postgraduate students and offer 14 academic programmes.

The School, through a cross-faculty integration, will create a powerful synergy between computational technology, mathematical modelling and statistical reasoning that have become the cornerstones of modern data science and artificial intelligence. By combining the complementary strengths of computer science and statistics curricula, students graduating from the school will have strong analytical and computational skills. The School will also serve as a thriving hub for interdisciplinary academic and research collaborations, fostering partnerships with all other faculties that leverage the potential of modern data science and AI.

The taught postgraduate programmes offered by the School will provide students with the latest knowledge and skills in computing & data science technologies and prepare them for the demands of the industry and job market. The programmes will cover various topics, including but not limited to data science, artificial intelligence, cybersecurity, statistics and software engineering301. The School works closely with industry partners to ensure that the programmes are up-to-date and relevant to the current demands of the industry. The school aims to produce well-equipped graduates who can contribute significantly to advancing knowledge and innovation.