Information valid for students commencing in 2021.
Master of Data Science (Professional)
Handbook year | 2021 |
Course code | 300604 |
Course type | MCW – Masters by Coursework (AQF Level 9) |
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. 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 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 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. |
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 | Not Applicable |
Course learning outcomes | On successful completion of the Master of Data Science (Professional), graduates will be able to:
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Course Structure (番茄社区 Online)
CORE SUBJECTS
SEQUENCE 1
:03 Foundations for Data Science
:03 Statistical Methods for Data Scientists
:03 Data Visualisation
:03 Database Systems
SEQUENCE 2
:03 Programming and Data Analytics Using Python
:03 Essential Mathematics for Data Scientists
:03 Introduction to Data Mining
:03 Visual Analytics for Data Scientists using SAS
SEQUENCE 3
:03 Data Science Master Class 1
:03 Advanced Data Management and Analysis using SAS
:03 Data Mining and Machine Learning
:03 Data Science and Strategic Decision Making for Business
SEQUENCE 4
:03 Data Information: Management, Security, Privacy and Ethics
:03 Data Science Master Class 2
:03 Data Science Project 1
:03 Data Science Project 2
Course Structure (Cairns)
CORE SUBJECTS
:03 Foundations for Data Science
:03 Statistical Methods for Data Scientists
:03 Data Visualisation
:03 Database Systems
:03 Programming and Data Analytics Using Python
:03 Professional Employability
:03 Introduction to Data Mining
:03 Visual Analytics for Data Scientists using SAS
:03 Data Science Master Class 1
:03 Advanced Data Management and Analysis using SAS
:03 Professional Placement/Internship 1
:03 Data Science and Strategic Decision Making for Business
:03 Data and Information: Management, Security, Privacy and Ethics
:03 Data Science Master Class 2
:03 Data Mining and Machine Learning
:03 Professional Placement/Internship 2
Campus
COURSE AVAILABLE AT | NOTES |
番茄社区 Online | This course is 100% online through a continuous delivery model. |
番茄社区 Cairns | A full-time student will study up to 25% of this course online. |
Candidature
Expected time to complete | 32 months of continuous study for 番茄社区 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 |
Progression
Course progression | 番茄社区 Online students must successfully complete sequence 1, 2 and 3 sequentially before attempting any sequence 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 番茄社区 Online campus. This course includes prescribed professional placements for students admitted to the 番茄社区 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 | Nil |
Professional accreditation | Nil |
Maximum allowed Pass | 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: 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 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 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 Details
Award title | MASTER OF DATA SCIENCE (PROFESSIONAL) |
Approved abbreviation | MDataSc(Prof) |
Inclusion of majors on | 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. Students who exit the course prior to completion, and have successfully completed 36 credit points of appropriate subjects, may be eligible for the award of Master 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 (Professional) with Distinction. |