ʯÁñÊÓƵ

Course code

300106

Course type

GDN – Graduate Diploma (AQF Level 8)

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.

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 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.

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.
• 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 with initiative and high-level judgement.
• Communicate data concepts and methodologies of data science 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 AVAILABLE AT

NOTES

ʯÁñÊÓƵ Online

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

Expected time to complete

1.5 years in continuous carousel model (24CP) 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

3.5 years

Explanation – "part-time" modality for carousel 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 = 210 weeks, marginally over two years, without allowing for recesses.  

Maximum leave of absence

1 year

Course progression
requisites

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

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.

Note: Where relevant industry experience without qualifications, as provided for in the Admission Requirements, 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 ʯÁñÊÓƵ award to another, then advanced standing may be granted for more than two-thirds of the new award, 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 13.5 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 title

GRADUATE DIPLOMA OF DATA SCIENCE

Approved abbreviation

GDipDataSc

Inclusion of majors on
testamur

Not applicable – this course does not have majors

Exit with lesser award

Not applicable

Course articulation

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

Special Awards

Not applicable