The University of Hong Kong

What you'll study

This MPhil is a research degree centred on an independent thesis supervised by faculty in areas such as machine learning, statistical modelling, data mining, natural language processing, computer vision, databases and distributed data systems. The programme typically combines focused coursework in advanced topics with sustained research under supervision.

• Research thesis: The core component is an original research project leading to an MPhil thesis. Candidates develop a research proposal, conduct experiments or theoretical work, and present findings under regular supervisory guidance.
• Advanced coursework: Students normally take selected advanced-level modules to support their research, for example: advanced machine learning, statistical inference for data science, deep learning, probabilistic models, time series and sequential data, scalable data management, and optimisation for large-scale systems.
• Research methods and seminars: Training in research methodology, reproducible research practices, and participation in departmental seminars and reading groups is expected. Students are encouraged to attend workshops and present work at conferences.
• Interdisciplinary options: Where relevant, students may collaborate with colleagues in statistics, computer science, engineering, business analytics, and health informatics to apply data-science methods to domain-specific problems.
• Duration and assessment: The programme is research-focused and is typically completed over a period consistent with research master’s norms. Assessment is primarily by thesis, complemented by progress reports and oral examinations.

Entry requirements

Applicants should hold a recognised bachelor’s degree with good honours in a relevant discipline such as computer science, statistics, mathematics, engineering, physics, or a closely related field. A strong quantitative background and programming experience are expected.

• Academic credentials: A competitive undergraduate degree (or equivalent) with substantial coursework in mathematics, probability and statistics, and programming or algorithms.
• Research potential: Evidence of research ability is important—this can include an honours project, publications, research internships, or a detailed research proposal outlining intended MPhil work.
• Programming and technical skills: Competence in one or more programming languages commonly used in data science (such as Python, R, C++ or Java), and familiarity with data-processing tools and libraries.
• References: Two or more academic referees who can comment on the applicant’s academic and research potential.
• English proficiency: For applicants whose prior education was not in English, proof of English language proficiency is required in line with the University’s regulations.
• Interview: Shortlisted applicants may be asked to attend an interview with prospective supervisors or the admissions panel.

Career prospects

Graduates of the MPhil in Data Science pursue a range of careers that require strong methodological and research skills. Many continue to doctoral study and academic research; others move into technical leadership roles in industry.

• Academic and research paths: Progression to PhD programmes and research positions in universities, research institutes and public-sector labs.
• Industry roles: Data scientist, machine learning engineer, quantitative analyst, research scientist, data engineer, and roles in applied AI across finance, technology, healthcare, telecommunications and government.
• Consulting and specialised roles: Positions in analytics consulting, R&D teams, and product development where advanced modelling and large-scale data systems expertise is required.
• Transferable skills: Graduates gain strengths in problem formulation, experimental design, statistical reasoning, software development for data pipelines, and communicating technical results to stakeholders.

Why study at The University of Hong Kong

The University of Hong Kong offers strong supervision from faculty active in foundational and applied data-science research, with access to well-equipped computing facilities and opportunities for interdisciplinary collaboration across departments. The institution’s location in a leading international financial and technology hub provides proximity to industry partners, public-sector agencies and start-ups, facilitating collaborative projects, internships and knowledge exchange.

HKU emphasises research training, encouraging students to publish and present their work, and to engage with regional research networks including partners in the Greater Bay Area. The university’s long-standing academic reputation and international research links help graduates pursue further study or enter competitive technical roles worldwide.