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 Master of Science (Health Data Science) covers the entire pipeline from comprehension of complex health issues, through data wrangling and management, machine learning and data mining, data analytics, data modelling and communication including data visualisation. The degree will appeal to you whether you are new to the field or already working in the industry and keen to develop your knowledge and skills. This degree is appropriate for Australian and international students.

Program Code




Total units of credit (UOC)




Indicative Enrolments


Why choose this degree?

Graduates of this degree will be well suited to an identified area of workforce demand, in both public and private health sectors.

Who should choose this degree?

Industry professionals and health care graduates choose this degree so they can explore the possibility of health solutions through analysing data.

Degree Structure

Degree Handbook

Career Opportunities

  • Health data management
  • Health data analytics
  • Health research 

Entry requirements

The entry criteria are:

  • an undergraduate degree in a cognate discipline
  • an undergraduate degree in a non-cognate discipline at honours level
  • successful completion of Graduate Diploma in Health Data Science 5372 programor
  • qualifications equivalent to or higher than Graduate Diploma in Health Data Science 5372 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.

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 with limited places may have an earlier application deadline or commencement date. Find out more.



2021 Indicative First Year Fee


2021 Indicative Fee to Complete Degree


*Fees are subject to annual review by the University and may increase annually, with the new fees effective from the start of each calendar year. The indicative fees listed here are based on an estimated average and are for tuition only other fees and charges are not included. The amount you pay will vary depending on the calendar year to enrol, the courses you select and whether your study load is more or less than 1 Equivalent Full Time Student Load (8 courses per year).

Indicative fees are a guide for comparison only based on current conditions and available data. You should not rely on indicative fees. More information on fees can be found at the UNSW fees website.

Indicative fees to complete the program have been calculated based on a percentage increase for every year of the program. Fee increases are assessed annually and may exceed the indicative figures listed below.

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