Information valid for students commencing in 2021.
Graduate Diploma of Data Science
Handbook year | 2021 |
Course code | 300106 |
Course type | GDN – Graduate Diploma (AQF Level 8) |
Owner | Division of Tropical Environments and Societies |
College | 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. |
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 番茄社区 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:
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Course Structure
Option
Select either No Major or a Major
OPTION 1 - No Major
CORE
SEQUENCE 1
:03 Database Systems
:03 Foundations for Data Science
:03 Statistical Methods for Data Scientists
:03 Data Visualisation
SEQUENCE 2
:03 Programming and Data Analytics Using Python
:03 Essential Mathematics for Data Scientists or :03 Professional Employability
:03 Introduction to Data Mining
:03 Visual Analytics for Data Scientists using SAS
OPTION 2 - Major
CORE
:03 Database Systems
:03 Foundations for Data Science
:03 Statistical Methods for Data Scientists
:03 Data Visualisation
:03 Introduction to Data Mining
PLUS
Select a Major from Table A
TABLE A (MAJORS)
Credit points in major | 9 credit points |
MAJOR | AVAILABLE AT | NOTES |
番茄社区 Online |
Location
COURSE AVAILABLE AT | NOTES |
番茄社区 Online | This course is 100% online through a continuous delivery model. |
番茄社区 Cairns | Exit Only |
Candidature
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 |
Progression
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) | No |
Special assessment requirements | Nil |
Professional accreditation requirements | Nil |
Maximum allowed Pass Conceded (PC) grade | Nil |
Credit
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:
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 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 番茄社区 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 studies completed in the 10 years prior to the commencement of this course. |
Expiry | 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 | GRADUATE DIPLOMA OF DATA SCIENCE |
Approved abbreviation | GDipDataSc |
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 |