Health Data Science (HDS) is the science and art of generating data-driven solutions through comprehension of complex real-world health problems, employing critical thinking and analytics to derive knowledge from big data. HDS is an emergent discipline, arising at the intersection of biostatistics, computer science and health. The Graduate Diploma in Health Data Science covers the second part of the HDS pipeline concerned with data analytics, machine learning and data mining, data modelling and communication including data visualisation. The program is designed to appeal to those new to the field, as well as those already working in the industry who are keen to develop their knowledge and skills.

Program Code




Total units of credit (UOC)




Indicative Enrolments

New Program

Why choose this degree?

With this program, you will be well positioned to enter an in-demand workforce, in both public and private health sectors.

Who should choose this degree?

This program is suited to industry professionals and health care graduates as well as those with degrees in mathematics, statistics and computer science.

Degree Structure

Degree Handbook

Career opportunities

  • Health data management
  • Health data analytics
  • Health research

Entry requirements

The program is designed for, and should appeal to, a broad local and international target student base. Student backgrounds will include healthcare (clinical, nursing and allied health), mathematics, statistics, and computer science.

The entry criteria are:

*successful completion of Graduate Certificate in Health Data Science 7372 program or

qualifications equivalent to or higher than Graduate Certificate in Health Data Science 7372 program on a case-by-case basis Cognate discipline is defined as a degree in one of the following disciplines:

  • a science allied with medicine, including medicine nursing dentistry physiotherapy optometry biomedical/ biological science pharmacy public health veterinary science biology biochemistry statistics mathematical sciences computer science psychology (health) economics data science other (case-by-case basis)

Recognition of prior learning (RPL) is awarded in accordance with UNSW 'Recognition of Prior Learning (Coursework Programs) Policy' and 'Recognition of Prior Learning Procedure', for both program admission and credit. Criteria for RPL for admission is detailed in the program entry requirements. Credit (advance standing) is available for additional RPL beyond that acknowledged for program entry. Both formal and non-formal learning is considered. Recognition of formal learning is assessed for equivalence to an entire (HDAT) course on a case-by-case basis. Credit granted for formal learning will yield specified credit for the equivalent 6 UoC course. Recognition of non-formal learning will result from micro-credentialing and awarding of Badges. Reduction in the total volume of learning due to advance standing is limited to a maximum of 12 UoC.

How to apply

Applications must be submitted through our Apply Online portal. We encourage you to submit your completed application as early as possible to ensure it will be processed in time for your preferred term. Some high-demand programs and Faculties with limited places may have an earlier application deadline or commencement date. Find out more.

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