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Digital Health and Data Science

Course resolutions

The course resolutions detailed in this page apply to all courses included in the table below under section 1 (course codes). 

These resolutions must be read in conjunction with applicable University By-laws, Rules and policies including (but not limited to) the University of Sydney (Coursework) Rule 2014 (the 'Coursework Rule'), the Coursework Policy 2021 (the 'Coursework Policy'), the Learning and Teaching Policy 2019, the Resolutions of the Faculty, University of Sydney (Student Academic Appeals) Rule 2021, the Academic Honesty in Coursework Policy 2015 and the Academic Honesty Procedures 2016. Current versions of all policies are available from the Policy Register: http://www.sydney.edu.au/policies

1 Course codes

Code Course title
GNHLTCIN-01  Graduate Certificate in Digital Health and Data Science
MAHLTCIN-01  Master of Digital Health and Data Science

2 Attendance pattern

(1) The attendance pattern for this course is full-time or part-time according to candidate choice.

3 Master's type

(1) The master’s degree in these resolutions is a professional master’s course, as defined by the Coursework Rule 2014.

4 Embedded courses in this sequence

(1) The embedded courses in this sequence are:

(a) the Graduate Certificate in Digital Health and Data Science

(b) the Master of Digital Health and Data Science

(2) Providing candidates satisfy the admission requirements for each stage, a candidate may progress to the award of any of the courses in this sequence and receive full credit for work completed in the prior award. Only the highest award completed will be conferred.

5 Admission to candidature

(1) Available places will be offered to qualified applicants based on merit, according to the following admissions criteria.

(2) Admission to the Graduate Certificate in Digital Health and Data Science requires:

(a) A minimum of an AQF level 7 degree in a cognate discipline, or an AQF level 8 degree in a non-cognate discipline.

(3) Admission to the Master of Digital Health and Data Science requires:

(a) A minimum of an AQF level 8 degree in a cognate discipline with at least a credit average; or

(b) An AQF level 7-equivalent qualification in a cognate discipline with at least a credit average and two years relevant work experience; or

(c) Completion of the requirements of the embedded Graduate Certificate in Digital Health and Data Science with a credit average, or qualifications deemed by the faculty to be equivalent.

(4) In exceptional circumstances the Dean or nominee may admit applicants without these qualifications who, in the opinion of the faculty, have qualifications and evidence of experience and achievement sufficient to successfully undertake the award.

(5) A cognate discipline includes Data Science, Computer Science, Mathematics, Statistics, Engineering, Physics, Economics, Finance, Health Sciences, Medical Sciences, or a related Health Profession or other disciplines that are deemed cognate by the Steering Committee.

6 Cross-faculty management

(1) The Faculty of Engineering is the administering faculty for the course. Candidates in this degree program will be under the general supervision of Faculty of Engineering.

(2) The course is overseen by a cross-faculty Steering Committee with core membership from Faculty of Engineering and Faculty of Medicine and Health, chaired by the Deputy Vice Chancellor (Education) or nominee. It makes recommendations to participating faculties and reports to the University Executive Education Committee.

7 Requirements for award

(1) The units of study that may be taken for the courses are set out in Table A for the Graduate Certificate and Master Digital Health and Data Science.

(2) To qualify for the award of the Graduate Certificate in Digital Health and Data Science, a candidate must complete 24 credit points of units of study including:

(a) 6 credit points of Data Science Selective units of study;

(b) 6 credit points of Digital Health Selective units of study;

(c) 6 credit points of Data Science Elective units of study or 6 credit points of Data Science Selective units of study; and

(d) 6 credit points of Digital Health Elective units of study or 6 credit points of Digital Health Selective units of study.

(3) To qualify for the award of the Master of Digital Health and Data Science candidates must complete 48 credit points of units of study including:

(a) 24 credit points of Core units of study;

(b) 6 credit points of Data Science Elective units of study;

(c) 6 credit points of Digital Health Elective units of study;

(d) 12 credit points of Capstone Project units.

8 Progression Rules

(1) A candidate for the Master of Digital Health and Data Science must complete 24 credit points from Core and Elective units of study before taking Capstone Project Units, at least 12 of these credit points must comprise Core units of study.

9 Cross-institutional study

(1) Cross-institutional study is not available in these courses except where the University of Sydney has a formal cooperation agreement with another university.

10 Course transfer

(1) A candidate for the Master of Digital Health and Data Science degree may elect to discontinue study and graduate with the Graduate Certificate in Digital Health and Data Science, with the approval of the Dean, and provided the requirements of the Graduate Certificate have been met.

11 Transitional provisions

(1) These resolutions apply to students who commenced their candidature after 1 January 2022 and students who commenced their candidature prior to 1 January 2022 who formally elect to proceed under these resolutions.

(2) Students who commenced prior to 1 January 2022 may complete the requirements in accordance with the resolutions in force at the time of their commencement.