Graduate Diploma of Data Science

Graduate Diploma of Data Science

Handbook year


Course code


Course type

GDN – Graduate Diploma (AQF Level 8)


Tropical Environments and Societies

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.

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 the employer detailing your position and job description).

Special admission requirements

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

Academic Requirements for Course Completion

Credit points

24 credit points as per course structure

Post-admission requirements

Computer and internet access is required.

Course learning outcomes

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

  • Integrate and apply specialised theoretical and technical knowledge in data science and/or contemporary applications of data science
  • Retrieve, analyse, synthesise and evaluate knowledge from a range of sources.
  • Plan and conduct reliable, efficient analysis of a variety of data by selecting and applying appropriate methods, techniques and tools.
  • Demonstrate effective applications of appropriately chosen computing languages and computational tools for data acquisition, queries, management, analysis and visualisation
  • Identify, analyse and generate solutions to unpredictable or complex problems, especially related to tropical, rural, remote or Indigenous contexts, by applying knowledge and skills of data science and/or its applications with initiative and high-level judgement.
  • Communicate data concepts and methodologies of data science and/or data science applications clearly and coherently to a variety of audiences through advanced written and oral English language skills and a variety of media.
  • Critically review general regulatory requirements, ethical principles and, where appropriate, 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 in collaboration with others.

Course Structure


Select either No Major or a Major

OPTION 1 - No Major



CP5804:03 Database Systems

MA5800:03 Foundations for Data Science

MA5820:03 Statistical Methods for Data Scientists

MA5830:03 Data Visualisation


CP5805:03 Programming and Data Analytics Using Python

MA5801:03 Essential Mathematics for Data Scientists

MA5810:03 Introduction to Data Mining

MA5821:03 Visual Analytics for Data Scientists using SAS

OPTION 2 - Major


CP5804:03 Database Systems

MA5800:03 Foundations for Data Science

MA5820:03 Statistical Methods for Data Scientists

MA5830:03 Data Visualisation

MA5810:03 Introduction to Data Mining


Select a Major from Table A


Type of major

Optional, Single

Credit points in major

9 credit points




Internet of Things

JCU Online




JCU Online

This course is 100% online through a continuous delivery model.


Exit only


Expected time to complete

16 months of continuous study (equivalent to one year full-time study normally); or equivalent part-time

Explanation – Eight study periods yields 64 weeks, but spanning one mid-year recess and two end-of-year breaks will add 7 or 8 weeks for a total of 72 weeks, compared to 1.5 years at 78 weeks.

Maximum time to complete

3 years

Explanation – "part-time" modality for sequential offerings is expected to be at 2/3 of the full-time rate rather than 1/2 (study 16 weeks, 8 weeks off) as this results in 6CP per Teaching Period. Because of the currency of knowledge in Data Science it is important to know if a candidate is going to complete their studies over a longer time frame, especially if they intend to continue into the Master's program.  72 weeks x 1.5 + 1 year LoA = 160 weeks, marginally over three years, without allowing for recesses.

Maximum leave of absence

1 year


Course progression requisites

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

Course includes mandatory professional placement(s)


Special assessment requirements


Professional accreditation requirements


Maximum allowed Pass Conceded (PC) grade




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

* 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 13 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 for 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

Majors will appear on the testamur

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.

Course articulation

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

Special Awards

Not applicable