Until recently, if you’d looked into where our health data is collected by researchers across Australia, you would have encountered the usual suspects - hospitals, prescription records, aged-care facilities, and local GP clinics.
But the future of big data is set to look very different when it comes to our health, because the rise of increasingly sophisticated personal devices such as iPhones and Fitbits means scientists can access information across all aspects of our daily lives. In addition, there’s a digital revolution occurring right across our health systems moving to make health data collection efficient and accessible for analysis.
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. There is also an option for a Graduate Diploma and Graduate Certificate.
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.
The Master of Science, Graduate Diploma and Graduate Certificate in Health Data Science are Australasia’s first postgraduate programs in Health Data Science.
The program is designed and run by the Centre for Big Data Research in Health.
The Centre of Big Data Research in Health is a leading Australian and international hub for health research using big data. The aim of the centre is to maximise the productive use of all possible sources of health big data to enhance the health and well-being of Australians and the global community.
At the Centre of Big Data Research in Health, its interdisciplinary team of staff have world-leading expertise in managing, manipulating, analysing and visualising health big data.
Why study a Master of Science in Health Data Science?
“The Health Data Science programs are designed for those new to Health Data Science and those already working in the field looking to up-skill.
Whether you are a statistician who wants to build on your current skills with exposure to a field where you can make an impact; a clinician or nurse who wants to expand your capabilities and improve the quality of care received by your patients; or a keen programmer looking to convert your “on-the-job” experience into a formal qualification, our programs welcome students from a wide range of backgrounds.”
- Andrew Blance, Program Director Health Data Science, Centre for Big Data Research in Health
“We sit at a key point in history, where big data are poised to become a dominant driver in what happens in health and healthcare. We have designed our programs in response to a significant and growing gap in the global health workforce: skilled data scientists who understand the context of health and can apply data analytics to drive health improvement.
The Health Data Science programs aim to equip graduates with the essential cognitive, analytical and communication skills needed to make sense of health big data.
How we make use of this data and transform it into action to better support clinical care, inform health policy and improve population health has never been more important than now.”
- Professor Louisa Jorm, Director, Centre for Big Data Research in Health
There is growing demand within the public and private health sector both in Australia and globally for professionals with specialised interdisciplinary skills in Health Data Science.
A Health Data Scientist can function at any stage along the Health Data Science pipeline.
From designing and leading research studies or evaluations, conducting complex analyses or managing teams of data analysts, through to advising health policy makers on the outcomes of studies or analysis findings, a Health Data Scientist has the breadth of skills for a number of different jobs in the arena of health big data.
A Master of Science in Health Data Science can lead you to a career in:
Learn more:
The entry criteria are:
Cognate discipline is defined as a degree in one of the following disciplines (a science allied with medicine) including:
Note: 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.
Admissions for our Term 1, 2020 and Term 3 are now open.
The deadline to apply for international students are 31 January 2020 for Term 1 & 28 August 2020 for Term 3.
The deadline to apply for domestic students are 7 February 2020 for Term 1 & 4 September 2020 for Term 3.
Allow at least 3 weeks processing time for your application. To ensure your place in the program, it is recommended you apply early and well before the enrolment deadline. Please refer to the programs in the UNSW Handbook for entry requirements.
Tuition fees
For information regarding UNSW Medicine tuition and other student fees, please refer to fees.
Financial assistance for Domestic students
FEE-HELP is an Australian Government loan scheme that assists eligible full fee-paying students to pay all or part of their tuition fees. Please refer to fee-help for more information.
About the UNSW Health Data Science program | Degree at a glance
Master of Science in Health Data Science
How is the program delivered?
You can view detailed information about coursework and subjects in the UNSW handbook
For non HDAT courses, please visit the respective Faculty and School pages for course outline.
| Course Code | Course Name | Course Convenor(s) | Term Offering | Course Outline |
|---|---|---|---|---|
| HDAT9100 | Context of Health Data Science | Dr Amy Gibson | T1 | HDAT9100 Course Outline |
| HDAT9200 | Statistical Foundations for Health Data Science | Andrew Blance | T1 & T3 | HDAT9200 Course Outline |
| HDAT9300 | Computing for Health Data Science | Andrew Blance | T1 | |
| HDAT9400 | Management and Curation of Health Data | Sanja Lujic | T2 & T3 | HDAT9400 Course Outline |
| HDAT9500 | Machine Learning and Data Mining | Dr Oscar Perez Concha | T2 & T3 | HDAT9500 Course Outline |
| HDAT9600 | Statistical Modelling 1 | Andrew Blance | T2 | HDAT9600 Course Outline |
| HDAT9700 | Statistical Modelling 2 |
Dr Mark Hanly Dr Maarit Laaksonen |
T3 | HDAT9700 Course Outline |
| HDAT9800 | Visualisation and Communication of Health Data | Dr James Farrow | T2 | HDAT9800 Course Outline |
|
HDAT9900 HDAT9901 HDAT9902 |
Research Dissertation | Dr Amy Gibson | T1, T2, T3 | |
| HDAT9910 | Research Capstone | Andrew Blance | T1, T2, T3 |
2019
2018