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Handbook year

Course code

300104

Course type

MCW – Masters by Coursework (AQF Level 9)

Division

Tropical Environments and Societies

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 ʯÁñÊÓƵ 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.

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 AVAILABLE AT

NOTES

ʯÁñÊÓƵ Online

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

Cairns

A full-time student will study up to 25% of this course online.

Expected time to complete

32 months of continuous study for ʯÁñÊÓƵ Online students,

24 months full-time for on-campus students;or equivalent part-time

Maximum time to complete

5.5 years

Maximum leave of absence

2 years

Course progression
requisites

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

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

Course includes mandatory professional placement(s)

There are no mandatory placements for students admitted to the ʯÁñÊÓƵ Online campus.

This course includes prescribed professional placements for students admitted to the Cairns Campus only. Students may be required to undertake such placements away from the campus at which they are enrolled, at their own expense.

Special assessment
requirements

Nil

Professional accreditation
requirements

Nil

Maximum allowed Pass
Conceded (PC) grade

Nil

Eligibility

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 Carousel 1 and 2.
• Five (5) years or more relevant industry experience in IT or Data   Science/Data Analytics – up to 12 credit points from Carousel 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

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

Currency

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

Expiry

Credit gained for any subject shall be cancelled 15.5 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 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