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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
MADATASC-02 Master of Data Science
GNDATASC-01 Graduate Diploma in Data Science
GCDATASC-02 Graduate Certificate in Data Science
MADTSCOP-01 Master of Data Science (Online)
GNDTSCOP-01 Graduate Diploma in Data Science (Online)
GCDTSCOP-01 Graduate Certificate in Data Science (Online)

2  Attendance pattern

The course will be offered as both full time and part time. A face-to-face offering will be available to domestic and international students, including student visa holders. A parallel online offering will not be available to student visa holders.

3  Master’s type

This master’s degree is a professional master’s course, as defined by the Coursework Policy.

4  Embedded courses in this sequence

(1)  The embedded courses in this sequence are:

(a) the Graduate Certificate in Data Science
(b) the Graduate Diploma in Data Science
(c) the Master of 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 Data Science requires:

(a) A degree in a cognate discipline at a minimum of AQF level 7 with a minimum average of 60.

(3)  Admission to the Graduate Diploma in Data Science requires:

(a) A degree in a cognate discipline at a minimum of an AQF level 7 with a minimum average of 60; or
(b) Completion of 24 credit points of the embedded Graduate Certificate in Data Science with a minimum average of 60.

(4)  Admission to the Master of Data Science requires:

(a) A degree in a cognate discipline at a minimum of an AQF level 7 degree with at least a credit average; or
(b) Completion of the requirements of the embedded Graduate Diploma in Data Science with a credit average, or qualifications deemed by the faculty to be equivalent; or
(c) Completion of 24 credit points of the embedded Graduate Certificate or Graduate Diploma in Data Science including 12 credit points of Data Science Core units of study and 12 credit points of Data Science Specialist or Specialisation Core units of study with a minimum of credit average.

(5)  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.

(6)  A cognate discipline must show significant study in computer programming or statistics/quantitative-analysis, equivalent in volume of learning to a major or minor. This would include a degree in a discipline deemed cognate by the Program Directors, or a component within a degree not clearly in those disciplines.

6  Requirements for award

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

(2)  To qualify for the award of the Graduate Certificate in Data Science candidates must complete 24 credit points of units of study including:

(a) 12 credit points of Data Science Core units of study; and
(b) 12 credit points of Data Science Specialist units of study.

(3)  To qualify for the award of the Graduate Diploma in Data Science, a candidate must complete 48 credit points of units of study including:

(a) A minimum of 12 credit points of Data Science Core units of study; and
(b) A minimum of 6 credit points of Professional Core units of study; and
(c) A minimum of 12 credit points of Data Science Specialist units of study; and
(d) A maximum of 12 credit points of Foundation or Elective units of study.

(4)  To qualify for the award of the Master of Data Science candidates must complete 72 credit points of units of study including:

(a) for the Professional Pathway:

(i) 18 credit points of Data Science Core units of study; and
(ii) 12 credit points of Professional Core units of study; and
(iii) 12 credit points of Capstone project units of study; and
(iv) A minimum of 18 credit points of Specialisation units of study or Data Science Specialist units of study; and
(v) A maximum of 12 credit points of Elective or Foundation units of study.

(b) for the Research Pathway:

(i) 18 credit points of Data Science Core units of study; and
(ii) 12 credit points of Professional Core units of study; and
(iii) 24 credit points of Research pathway units of study; and
(iv) 18 credit points of Specialisation units of study or Data Science Specialist units of study; and
(v) no credit points of Elective or Foundation units of study.

7  Specialisations

(1)  Completion of a specialisation is optional for the Master of Data Science.

(2)  A specialisation requires the completion of

(a) 18 credit points of the selected Specialisation Core units of study defined in the table for Specialisations for the Master of Data Science; and
(b) completion of a minimum of 12 credit points of capstone or research project units of study in the same area as the specialisation for the Master of Data Science.

(3)  The specialisations available are:

(a) Data Engineering
(b) Machine Learning

(4)  Candidates who choose the Research Pathway will be required to undertake a project in an area related to the specialisation.

(5)  Candidates who do not specify a specialisation must complete 18 credit points of Data Science Specialist units of study

8  Recognition of prior learning

(1)  Subject to the specific provisions of this clause, prior learning may be recognised consistently with the requirements of the Coursework Policy.

(2)  The School of Computer Science may award credit or reduced volume of learning to candidates who have completed relevant postgraduate study at the University of Sydney.

(3)  For all other candidates for the Master of Data Science, recognition of prior learning may account for a maximum of 50 percent of the course requirements for the relevant award.

(4)  Candidates for the Graduate Certificate in Data Science will not be eligible for reduced volume of learning or credit.

(5)  Candidates for the Graduate Diploma in Data Science may be granted:

(a) on the basis of an AQF level 8 degree or above in a cognate discipline:

(i) up to 12 credit points of Professional Core units of study.
(ii) Where a waiver is granted for a Data Science Core unit of study, another Data Science Specialist unit of study must be taken.

(6)  Candidates for the Master of Data Science may be granted:

(a) on the basis of an AQF level 8 degree or above in a cognate discipline:

(i) up to 24 credit points of reduced volume of learning which may be applied against units of study as specified by the School of Computer Science.
(ii) Where a waiver is granted for a Data Science Core unit of study, another Data Science Specialist unit of study must be taken.

(7)  Credit will not be granted for foundation level units of study.

9  Progression rules

(1)  A candidate for the Professional Pathway in the Master of Data Science must complete a minimum of 18 credit points from Data Science Core and 18 credit points from Data Science Specialist or Specialisation Core units of study before enrolling in Capstone Project units of study.

(2)  A candidate for the Research Pathway in the Master of Data Science must

(a) complete a minimum of 18 credit points from Data Science Core and 6 credit points from Data Science Specialist or Specialisation Core units of study with a minimum distinction average and
(b) enrol in 12 credit points from Data Science Specialist or Specialisation Core units of study prior to or concurrently with Research Methods.

10  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.

11  Award of the master’s degree, graduate diploma and graduate certificate

(1)  The master’s degree, graduate diploma and graduate certificate will be awarded in the pass grade

(2)  The testamur for the Master of Data Science will specify the specialisation completed.

12  Course transfer

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

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

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

13  Commencement of these resolutions

These resolutions apply to students who commenced their candidature after 1 January 2023 and students who commenced their candidature prior to 1 January 2023 who elect to proceed under these resolutions. Students who commenced their candidature prior to 1 January 2023 may complete the requirements in accordance with the resolutions in force at the time of their commencement.