The University of Hong Kong

What you'll study

The PhD in Data Science is a research-led programme centred on an independent doctoral thesis, supported by advanced coursework and seminars. Students typically undertake a programme of research training, select specialised modules to build methodological depth, and attend research seminars and lab meetings across relevant departments.

• Research thesis: The core of the degree is an original piece of research that makes a substantive contribution to data science. Projects commonly address topics such as machine learning algorithms, statistical theory, causal inference, scalable data systems, natural language processing, computer vision, privacy and fairness, or applications in domains like healthcare, finance and urban analytics.
• Advanced coursework and modules: Typical taught components include advanced machine learning, probabilistic modelling and Bayesian methods, optimisation for large-scale learning, statistical inference, deep learning, data engineering and databases, and topics in ethics, interpretability and privacy. Coursework is tailored with supervisor advice to complement the research project.
• Research training: Structured training covers research methods, scientific communication, reproducible computing, software engineering for research and transferable skills such as grant writing and teaching. Students are encouraged to present at conferences and publish in peer-reviewed venues.
• Supervision and collaboration: Candidates are supervised by faculty members from relevant departments and are often co-supervised across disciplines. Interdisciplinary collaboration with centres and industry partners is common to support applied projects and access to domain-specific data.
• Duration and assessment: The programme is assessed primarily on the quality and originality of the doctoral thesis, together with progress reviews and demonstration of competence through publications, conference presentations or taught assessments as required by the faculty.

Entry requirements

Applicants should hold a relevant postgraduate degree (such as a Master’s) in data science, computer science, statistics, mathematics, engineering, or a closely related discipline; exceptional candidates with an outstanding Bachelor’s degree and strong research experience may also be considered. Successful applicants demonstrate strong quantitative and programming skills, a clear research proposal or research interests, and evidence of prior research potential.

• Academic qualifications: A recognised Master’s degree in a relevant field, or an excellent Bachelor’s degree with research experience.
• Research proposal: A concise proposal outlining the research problem, context, methodology and potential contribution. This helps match applicants with potential supervisors.
• Supervision availability: Admission depends on the availability of a suitable supervisor with relevant expertise; applicants are encouraged to contact potential supervisors before applying.
• Supporting documents: Curriculum vitae, academic transcripts, samples of research work (e.g. publications or theses), and academic references.
• English language: Proficiency in English is required; applicants whose prior education was not in English will normally need to provide an accepted language test result or other evidence of proficiency.

Career prospects

Graduates from the PhD in Data Science pursue a wide range of careers across academia, industry and the public sector. Many continue into postdoctoral positions and faculty roles, while others enter senior research and technical leadership roles in technology companies, finance, healthcare, telecommunications, and consulting.

• Academic careers: postdoctoral research, lecturing and tenure-track positions focused on machine learning, statistics or applied data science.
• Industry R&D: senior research scientist, machine learning engineer, data scientist, research engineer in multinational tech firms and specialised AI labs.
• Domain-specialised roles: quantitative analyst in finance, computational biologist or clinical data scientist in healthcare, and data lead roles in smart city or urban planning projects.
• Public sector and policy: roles in government agencies and international organisations that require expertise in data-driven policy, privacy and governance.
• Entrepreneurship and consulting: founding or joining data-driven startups, and advisory roles in strategy and analytics consultancies.

Why study at The University of Hong Kong

The University of Hong Kong offers a research-intensive environment with strong expertise across computer science, statistics and application domains. The university’s location provides strategic access to a vibrant technology ecosystem and opportunities for collaboration with industry, hospitals and government bodies in the region.

• Research environment: Supervision by internationally active researchers and access to interdisciplinary research groups and seminar series that foster collaboration across fields.
• Facilities and resources: Access to high-performance computing resources, datasets, and research infrastructure required for large-scale data work.
• Industry and regional links: Proximity to technology companies and research partnerships in Hong Kong and the wider Greater Bay Area, facilitating applied projects, internships and knowledge exchange.
• Career development: Structured training, support for conference attendance and publication, and a network that supports transitions into academia, industry and policy roles.