Master of Data Science

Master of Data Science

Course code

300104

Course type

MCW – Masters by Coursework (AQF Level 9)

Division

Division of 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.

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 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

48 credit points as per course structure

Additional course rules

Not Applicable

Post-admission requirements

Computer and internet access is required.

Additional completion
requirements

Not Applicable

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.
  • Critically review ethical principles, issues of data security and 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, capstone experience and/or piece of scholarship.

Course Structure

CORE SUBJECTS

CAROUSEL 1

MA5800:03 Foundations for Data Science

MA5820:03 Statistical Methods for Data Science

MA5830:03 Data Visualisation

CP5804:03 Database Systems

CAROUSEL 2

CP5805:03 Programming and Data Analytics Using Python

MA5801:03 Essential Mathematics for Data Scientists

MA5810:03 Introduction to Data Mining

MA5821:03 Advanced Statistical Methods for Data Scientists

CAROUSEL 3

CP5806:03 Data and Information: Management, Security, Privacy and Ethics

MA5831:03 Big Data: Management and Processing

MA5832:03 Data Mining and Machine Learning

MA5840:03 Data Science and Strategic Decision Making for Business

CAROUSEL 4

MA5851:03 Data Science Master Class 1

MA5852:03 Data Science Master Class 2

MA5853:03 Data Science Project 1

MA5854:03 Data Science Project 2

Campus

COURSE AVAILABLE AT

NOTES

JCU Online

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

Candidature

Expected time to complete

32 months in continuous carousel mode (48CP) or equivalent part time

Maximum time to complete

5.5 years

Maximum leave of absence

2 years

Progression

Course progression
requisites

Must successfully complete carousels 1, 2 and 3 sequentially before attempting any carousel 4 subjects.

A minimum of three subjects is required to be taken in any 12 month period to ensure satisfactory progression.

Course includes mandatory professional placement(s)

No

Special assessment
requirements

Nil

Professional accreditation
requirements

Nil

Maximum allowed Pass
Conceded (PC) grade

Nil

Advanced Standing

Eligibility

Students may apply for advanced standing for previous tertiary study in accordance with the Advanced Standing and Articulation policy and associated procedures

Advanced standing may be granted for the following:

  • An AQF Level 7 qualification in a cognate* discipline – up to 12 credit points from Carousel 1 and 2.
  • An AQF Level 8 Graduate Certificate in Data Science – up to 12 credit points from Carousel 1 and 2.
  • An AQF Level 8 Graduate Diploma in Data Science – up to 24 credit points.

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

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

Currency

Advanced standing will be granted only for subjects completed in the 10 years prior to the commencement of this course.

Expiry

Advanced standing gained for any subject shall be cancelled 17 years after the date of the examination upon which the advanced standing is based if, by then, the student has not completed this course.

Other restrictions

Advanced standing 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

MASTER OF DATA SCIENCE

Approved abbreviation

MDataSc

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

Not applicable

Special awards

Where 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