Health Data Science 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. Health Data Science is an emergent discipline, arising at the intersection of (bio)statistics, computer science, and health. The Graduate Diploma in Health Data Science (Grad Dip Health Data Science) is an extension of the Graduate Certificate in Health Data Science 7372, and covers the second part of the Health Data Science pipeline concerned with data analytics, machine learning and data mining, data modelling, and communication including data visualisation.

The program can be completed in 12 months full-time or part-time equivalent. The initial offering in Semester 1, 2018 will be open to internal (face-to-face, on-campus) students only.

Grad Dip Health Data Science graduates will be well suited to an identified area of workforce demand, in both public and private health sectors. High-achieving graduates will have potential for consideration into entry to the Master of Science Health Data Science program 9372. The program is designed to appeal to both those new to Health Data Science and those already working in the field looking to up-skill. The Grad Dip Health Data Science is appropriate for both an Australian and international audience. Potential students from any undergraduate background and/or who possess relevant work experience will be considered for admission via the Graduate Certificate.

1. Advanced disciplinary knowledge and practice
Graduates will be able to apply Health Data Science principles to novel contexts.

2. Enquiry-based learning
Graduates will be able to generate data-driven solutions through comprehension of real-world health problems, employing critical thinking and analytics to derive knowledge from (big) data.

3. Cognitive skills and critical thinking
Graduates will be able to apply Statistical Thinking to synthesise and critically evaluate complex Health Data Science concepts.

4. Communication, adaptive and interactional skills
Graduates will be able to communicate knowledge arising from Health Data Science insights to diverse audiences, in a variety of media including data visualisation (Vis), oral and written word.

5. Global outlook
Graduates will be able to demonstrate a global perspective for the potential of Health Data Science to positively impact health at both individual and community levels.

The 48 UoC Grad Dip Health Data Science by coursework program is fully articulated, with options for further study at Master of Science level.

Students must take 48 UoC of the following core courses:

Students enrolled in the Grad Dip Health Data Science program may exit early at the Graduate Certificate 7372 program if they meet the requirements of this degree.

For information regarding fees for UNSW programs, please refer to the following website:  UNSW Fee Website.

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

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.