Master of Data Science

Handbook year


Course code


Course type

Masters Degree (Coursework) (AQF Level 9)


Division of Tropical Environments and Societies


Science and Engineering

Award Requirements

Admission Requirements

Course pre-requisites

Completion of an AQF level 7 bachelor degree; or

Five (5) years or more relevant industry experience in IT or Data Science/Data Analytics; or

Other qualifications or practical experience recognised by the Dean, College of Science and Engineering as equivalent to the above.

Entry requirements for this course are consistent with the Pathways to Qualifications in the Australian Qualifications Framework (AQF level 9) Guidelines for Masters degrees.

Minimum English language proficiency requirements

Applicants of non-English speaking backgrounds must meet the English language proficiency requirements of Band 2 Schedule II of the JCU Admissions Policy.

Additional admission requirements

Mathematics B (or equivalent that includes algebra and elementary differential calculus) together with some background in computing, data analysis or programming is assumed.

Admission based on relevant industry experience must be supported by a detailed CV and proof of work experience (e.g. a letter from an employer detailing the position and job description).

Special admission requirements

Candidates will need to ensure that they have reliable access to internet services and computing resources.

Post-admission requirements

Computer and internet access are required.

Academic Requirements for Course Completion

Credit points

36 credit points as per course structure

Course learning outcomes

On successful completion of the Master of Data Science, graduates will be able to:

  • Integrate and apply an advanced body of practical, technical, and theoretical knowledge, including understanding of recent developments and modern challenges, in Data Science and its application.
  • Retrieve, analyse, synthesise and evaluate complex information, concepts, methods, or theories from a range of sources.
  • Plan and conduct appropriate investigations of data sets by selecting and applying qualitative and quantitative methods, techniques and tools, as appropriate to the data and the application.
  • Analyse requirements, and demonstrate effective applications of appropriate computing languages and computational tools for data acquisition, queries, management, analysis and visualisation.
  • Identify, analyse and generate solutions for complex problems, especially related to tropical, regional, or Indigenous contexts, by applying knowledge and skills of data science with initiative and expert judgement.
  • Communicate data concepts and methodologies of data science as well as the arguments and conclusions of the application of data science, clearly and coherently to specialist and non-specialist audiences through advanced written and oral English language skills and a variety of media.
  • Respond appropriately to issues of data security, privacy and, where appropriate, regulatory requirements and cultural frameworks to work effectively, responsibly and safely in diverse contexts.
  • Reflect on current skills, knowledge and attitudes to manage their professional learning needs and performance, autonomously and/or in collaboration with others.
  • Apply knowledge of research principles, methods, techniques and tools to plan and execute a substantial research-based project.

Course Structure



MA5800:03 Foundations for Data Science

MA5820:03 Statistical Methods for Data Scientists

CP5804:03 Database Systems

MA5830:03 Data Visualisation or CC5902:03 IoT Communication Systems


CP5805:03 Programming and Data Analytics Using Python or CC5903:03 IoT Edge Devices*

*Students who have taken CC5902:03 IoT Communication Systems must select CC5903:03 IoT Edge Devices

MA5801:03 Essential Mathematics for Data Scientists or CC5904:03 IoT Security and Cloud Computing*

*Students who have taken CC5902:03 IoT Communication Systems must select CC5904:03 IoT Security and Cloud Computing

MA5810:03 Introduction to Data Mining

MA5821:03 Visual Analytics for Data Scientists using SAS or MA5830:03 Data Visualisation*

*Students who have taken CC5902:03 IoT Communication Systems must select MA5830:03 Data Visualisation


MA5851:03 Data Science Master Class 1

MA5831:03 Advanced Data Management and Analysis using SAS or CP5805:03 Programming and Data Analytics Using Python*

*Students who have taken CC5902:03 IoT Communication Systems must select CP5805:03 Programming and Data Analytics Using Python

MA5832:03 Data Mining and Machine Learning

MA5840:03 Data Science and Strategic Decision Making for Business




JCU Online

This course is 100% online through a carousel delivery model


Expected time to complete

24 months of continuous study for JCU Online students or equivalent part-time

Maximum time to complete

4.5 years

Maximum leave of absence

2 years


Course progression requisites

Must successfully complete sequence 1 and 2 sequentially before attempting any sequence 3 subjects.

To ensure satisfactory progression a minimum of three subjects must be taken in any 12-month period.

Course includes mandatory professional placement(s)


Course includes mandatory fieldwork


Special assessment requirements


Professional accreditation requirements


Maximum allowed Pass Conceded (PC) grade


Supplementary exam for
final subject

Not applicable



Students may apply for a credit transfer for previous tertiary study or informal and non-formal learning in accordance with the Credit Transfer Procedure

Credit may be granted for the following:

  • An AQF Level 7 qualification in a cognate* discipline – up to 12 credit points from sequence 1 and 2.
  • Five (5) years or more relevant industry experience in IT or Data Science/Data Analytics – up to 12 credit points from sequence 1 and 2

Note: If relevant industry experience without qualifications in a quantitative discipline is used to meet entry requirements, that experience will not also be used to give credit.

* Cognate disciplines include data science, computer science, IT, mathematics, statistics, engineering, physics, economics or finance.

Maximum allowed

12 credit points, except where a student transfers from one JCU award to another, then credit may be granted for any subjects where there is subject equivalence between the awards.


Credit will be granted only for studies completed in the 10 years prior to the commencement of this course.


Credit gained for any subject shall be cancelled 14.5 years after the date of the examination upon which the credit is based if, by then, the student has not completed this course.

Other restrictions

Credit will not be granted for undergraduate studies or work experience used to gain admission to the course when assessed separately for admission requirements.

Award Details

Award title


Approved abbreviation


Inclusion of majors on testamur

Not applicable – this course does not have majors

Exit with lesser award

Students who exit the course prior to completion, and have successfully completed 12 credit points of appropriate subjects, may be eligible for the award of Graduate Certificate of Data Science.

Students who exit the course prior to completion, and have successfully completed 24 credit points of appropriate subjects, may be eligible for the award of Graduate Diploma of Data Science.

Course articulation

Students who complete this course are eligible for entry to the Master of Data Science (Professional), and may be granted credit for all subjects completed under this course.

Special awardsWhere coursework is completed at a grade point average of 6 or above, the Deputy Vice Chancellor, on the recommendation of the College Dean of Science and Engineering may recommend the award of Master of Data Science with Distinction.