Fast Facts
Commencing
- Cairns: March, September
- Brisbane: January, March, May, July, September, November
Fees
$34,960.00+
+estimated annual tuition fee for a full-time study load
Plus Student Services and Amenities fee
Scholarships and financial aid available if eligible
Fees currently displayed are indicative only. 2024 fees will be uploaded by October, 2023.
Duration
2 years full-time
Entry Requirements
AQF level 7 bachelor degree; or minimum five years relevant industry experience in IT or Data Science/Data Analytics; or equivalent
English language requirements
Band 2
If your native language is not English, you must meet the minimum English language requirements for this course.
CRICOS Code
102256E
Course detail
JCU students have the advantage of learning how to apply data science skills to tropical, regional, and Aboriginal and Torres Strait Islander contexts. Benefit from industry expertise with courses taught by data scientists, SAS partnership and certification, and access to the SAS data Science Academy.
Learn to exercise your professional judgment to suit specific circumstances and have the opportunity to complete a substantial research-based project. Your capstone project will allow you to build a portfolio to showcase your expertise.
Upon graduating, you will be across recent developments and modern challenges in data science and equipped with skills in key areas including machine learning, data mining, algorithm development, and advanced modelling.
You will have senior industry contacts, experience with real-world projects, and a foundational understanding of technology that allows you to adapt to every innovation. You will know when and how to apply computing languages and computational tools for data acquisition, queries, management, analysis, and visualisation.
You'll experience an innovative delivery method - this degree is broken up into twelve study periods. Students must complete an additional subject externally (online) in specified study periods throughout the course duration, in order to complete sixteen subjects in two, full-time years of study.
Consult the handbook for more information.
JCU Data Science (Professional) graduates are highly skilled specialists with invaluable professional expertise.
Graduates benefit from rapidly increasing job openings across the data industry and a data-driven future. You can apply data science to industrial, environmental, cultural, societal, and agricultural projects.
You could find work as a data scientist, data engineer, data analyst, data architect, visualisation specialist or statistician.
Handbook year | Information valid for students commencing in 2024. |
Course code | 300604 |
Course type | MCW – Masters by Coursework (AQF Level 9) |
Owner | Academy |
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 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 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:
|
Inherent Requirements
Inherent Requirements | Inherent requirements are the identified abilities, attributes, skills, and behaviours that must be demonstrated, during the learning experience, to successfully complete a course. These abilities, attributes, skills, and behaviours preserve the academic integrity of the University's learning, assessment, and accreditation processes, and where applicable, meet the standards of a profession. For more information please visit: Master of Data Science (Professional). |
Reasonable adjustments | All JCU students have the opportunity to demonstrate, with reasonable adjustments where applicable, the inherent requirements for their course. For more information please visit: Student Disability Policy and Procedure. |
Course Structure
CORE SUBJECTS
MA5800:03 Foundations for Data Science
MA5820:03 Statistical Methods for Data Scientists
MA5830:03 Data Visualisation
CP5804:03 Database Systems
CP5805:03 Programming and Data Analytics Using Python
MA5890:03 Career Planning
MA5810:03 Introduction to Data Mining
MA5821:03 Visual Analytics for Data Scientists using SAS
MA5851:03 Data Science Master Class 1
MA5831:03 Advanced Data Management and Analysis using SAS
MA5891:03 Professional Placement/Internship 1
MA5840:03 Data Science and Strategic Decision Making for Business
CP5806:03 Data and Information: Management, Security, Privacy and Ethics
MA5852:03 Data Science Master Class 2
MA5832:03 Data Mining and Machine Learning
MA5892:03 Professional Placement/Internship 2
Location
COURSE AVAILABLE AT | NOTES |
JCU Cairns | A full-time student will study up to 25% of this course online |
JCU Brisbane |
Candidature
Expected time to complete | 2 years full-time for on-campus students; or equivalent part-time |
Maximum time to complete | 4 years |
Maximum leave of absence | 2 years |
Progression
Course progression | To ensure satisfactory progression a minimum of three subjects must be taken in any 12 month period. |
Course includes mandatory professional placement(s) | This course includes prescribed professional placements for students admitted to the JCU Cairns and JCU Brisbane 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 |
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 JCU 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 | Students may receive an Award of Recognition in accordance with the Recognition of Academic Excellence Procedure |
Estimated annual tuition fee: $AUD34,960.00;$AUD34,960.00
Course fees are charged per year of full-time study. International course fees are reviewed annually and subject to change.
A Student Services and Amenities Fee is payable per subject up to a maximum amount per year.
Student Visa holders must have Overseas Student Health Cover (OSHC) for the duration of their Student Visa (except Norwegian, Swedish and Belgian citizens). Costs depend on the length of study and the number of dependents accompanying the student. For more information see Overseas Student Health Cover (OSHC).
Scholarships and financial aid are also available for international students.
Consult the admission requirements section in the Handbook for prerequisites and pathways into this course.
Application dates vary between courses, whether they're delivered in semester, trimester or carousel study modes. View our Application due dates page for more information.
Gather the documents you need to apply and submit an application via JCU's online application portal.
To study with JCU in Brisbane please see the application instructions to apply.
Real stories
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Lecturer
Dr Kelly Trinh
Associate Lecturer, Statistics and Data Science
JCU Master of Data Science students develop the knowledge and skills to work with machine learning algorithms and statistical techniques widely applied in practice. Students have hands-on experience working with a range of programming languages such as Python, R, Cloud Computation (Amazon Web Services) and SAS.