Health Data Science - 9372

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 Master of Science in Health Data Science (MSc 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 72 UoC program can be completed in 18 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.

MSc 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 of PhD enrolment. 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 MSc 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.

Graduates will be able to perform the functions of a Health Data Scientist across the entire pipeline.

1. Advanced disciplinary knowledge and practice
Graduates will be able to apply the advanced techniques of Health Data Science to novel health contexts.

2. Enquiry-based learning
Graduates will be able to generate novel data-driven solutions through comprehension of complex 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 articulate context appropriate data-driven solutions.

4. Communication, adaptive and interactional skills
Graduates will be able to communicate knowledge arising from complex 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 articulate a global perspective for the potential of Health Data Science to positively impact health at both individual and community levels.

The 72 UoC broadening MSc Health Data Science by coursework program is fully articulated, including options for Graduate Certificate Health Data Science 7372 program (24 UoC) and Graduate Diploma Health Data Science 5372 program (48 UoC).

Students must take 48 UoC of the following core courses:

The MSc Health Data Science offers a choice between a 24 UoC workplace/internship research dissertation (full-time or part-time options) or a 6 UoC capstone project plus 18 UoC electives (from a selection of over 20 courses).

Electives (up to 18 UoC)

Students enrolled in the MSc Health Data Science may exit early at the Graduate Certificate 7372 or Graduate Diploma 5372 programs if they meet the requirements of these degrees.

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

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 program

or

- 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

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.