Graduate Certificate in Health Data Science

{"domain":"Minimum Entry Requirements","requirements":[{"domain":{"label":"Minimum Entry Requirements","value":"minimum_entry_requirements"},"type":{"label":"Entry Requirements","value":"entry_requirements"},"cl_id":"5fa9d2aac38bea90a9607d2a05013162","description":"The entry criteria are:

\n\nan undergraduate degree in a cognate discipline\na degree in a cognate or non-cognate discipline at honours level (or higher) \nan undergraduate degree in a non-cognate discipline and minimum 1 year full-time equivalent of relevant work experience\n\nor

\n\nminimum 3 year full-time equivalent of relevant work experience\n\n 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)

Relevant experience is defined as:

\n

\n\n\nany (professional) position involving data acquisition, management or handling (e.g. database manager)\n\n\n

\nor

\n

\n\n\nany (professional) position involving analytics (e.g. data analyst)\n\n\n

\nand

\n

\n\n\ntertiary-level training, demonstrating capability in a cognate discipline\n\n\n 

\nEvidence requirements will be a CV in relation to professional experience.

\n 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.

\n 

","links":[]}]}

{"domain":"Limitations on Recognition of Prior Learning","requirements":[{"domain":{"label":"Limitations on Recognition of Prior Learning","value":"limitations_recognition_prior_learning"},"type":{"label":"Entry Requirements","value":"entry_requirements"},"cl_id":"dfa9d2aac38bea90a9607d2a05013162","description":"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.

\nIn the case of HDAT9200 and HDAT9300, combinations of both formal and non-formal learning, and RPL accepted for program entry will be considered towards credit.  For all other courses in the program, non-conferred formal learning beyond that acknowledged for program entry will be considered towards credit.  Recognition of formal learning is assessed for equivalence to an entire (HDAT) course on a case-by-case basis.  Recognition of non-formal learning will result from successful completion of an equivalent course from our suite of Continual Professional Development.

\nCredit granted will yield specified credit for the equivalent 6 UoC course.  Reduction in the total volume of learning due to advance standing is limited to a maximum of 50% of the total UoC for the program.

\n 

","links":[]}]}

The role of a health data scientist is dynamic and always evolving as their work spans across any of the multiple stages of the health data pipeline. From designing and leading research studies and analysing data, through to building machine learning processes to understand complex health issues, a health data scientist's work draws on a multiplicity of skills. This program could lead to a career in:

• Government departments of health (national, state or local)
• Hospitals
• Universities and research institutes
• Pharmaceutical companies
• Health insurance companies
• Private data analytics consultancies