51勛圖

MDataSc

Credit points

120

Minimum duration

1.5 years full-time or equivalent part-time.

Approved locations

  • 51勛圖 Online

EFTSL value of units : All 10cp units in this course have an EFTSL value of 0.125. Units with a credit point (cp) value of a multiple of 10 have corresponding EFTSL values.

Admission requirements

An applicant must comply with the Admission to Coursework Programs Policy.

To be eligible for admission to the course, an applicant must meet one of the following criteria:

An Australian Bachelor*s degree (or equivalent) in a cognate discipline such as Computer Science, Information Systems, Information Technology, Mathematics, Statistics, Economics, or Engineering; OR 

A Graduate Certificate in Data Science, Computer Science, or a related discipline. 

Advanced standing or recognition of prior learning (RPL) may be granted for comparable postgraduate study or completion of recognised professional certifications in data analytics, machine learning, or cloud computing. 

Disclaimer: The course entry requirements above are for 2026 Admission.



Completion requirements

To qualify for the degree, a student must complete 120 cp from the specified units (Part A).

Other requirements

Students are required to follow the pattern of unit enrolment set out in the relevant Course Enrolment Guide, unless otherwise approved by the Course Adviser. In all aspects of progress through the course, students will be advised by the Course Adviser.

Exit points

Students who successfully complete the requirements of the Graduate Certificate in Data Science or the Graduate Certificate in Data Analytics may exit with the relevant award.

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Schedule of unit offerings

Complete exactly 120 credit points from the following:

  • ITEC610Introduction to Data Science with Python10 CP
  • ITEC613Machine Learning with Scikit-Learn10 CP
  • ITEC617Modern Database Management10 CP
  • ITEC622Data Analytics and Visualisation10 CP
  • ITEC623Deep Learning and Emerging topics in Artificial Intelligence10 CP
  • ITEC632Data Mining Techniques and Applications10 CP
  • DTSC610Data Wrangling and Machine Learning Fundamentals10 CP
  • DTSC620Statistical Data Modelling10 CP
  • DTSC630Predictive Analytics10 CP
  • DTSC641Data Science Project A Research Essentials10 CP
  • DTSC642Data Science Project B - Implementation10 CP
  • COMP630Ethical Leadership and Professional Practice in Artificial Intelligence and Emerging Technologies10 CP

Course map

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Always check your  before you finalise your enrolment.

Course maps are subject to change.


Commencing 51勛圖 Term 1

  • Specified unitsITEC610 Introduction to Data Science with Python 10 cp
  • Specified unitsITEC617 Modern Database Management 10 cp
  • Specified unitsITEC613 Machine Learning with Scikit-Learn 10 cp
  • Specified unitsDTSC610 Data Wrangling and Machine Learning Fundamentals 10 cp
  • Specified unitsITEC623 Deep Learning and Emerging topics in Artificial Intelligence 10 cp
  • Specified unitsDTSC630 Predictive Analytics 10 cp
  • Specified unitsDTSC641 Data Science Project A Research Essentials 10 cp
  • Specified unitsITEC622 Data Analytics and Visualisation 10 cp

Commencing 51勛圖 Term 3

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