Master of Data Science (Professional)

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 requirements	Not Applicable
Course learning outcomes	On successful completion of the Master of Data Science (Professional), 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.

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 requisites	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 requirements	Nil
Professional accreditation requirements	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: An AQF Level 7 qualification in a cognate* discipline – up to 12 credit points from sequence 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 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 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. 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

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