Course and Subject Handbook 2024

Course and Subject Handbook 2024 Courses Postgraduate Courses Graduate Diploma of Data Science

Graduate Diploma of Data Science

Handbook year	Information valid for students commencing in 2024
Course code	300106
Course type	GDN – Graduate Diploma (AQF Level 8)
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.
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 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: Integrate and apply specialised theoretical and technical knowledge in data science and/or contemporary applications of 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 and/or its applications with initiative and high-level judgement. Communicate data concepts and methodologies of data science and/or data science applications 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.

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: Graduate Diploma of Data Science.
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

Option

Select either No Major or a Major

OPTION 1 - No Major

CORE

SEQUENCE 1

CP5804:03 Database Systems

MA5800:03 Foundations for Data Science

MA5820:03 Statistical Methods for Data Scientists

MA5830:03 Data Visualisation

SEQUENCE 2

CP5805:03 Programming and Data Analytics Using Python

MA5801:03 Essential Mathematics for Data Scientists or MA5890:03 Career Planning

MA5810:03 Introduction to Data Mining

MA5821:03 Visual Analytics for Data Scientists using SAS

OPTION 2 - Major

CORE

CP5804:03 Database Systems

MA5800:03 Foundations for Data Science

MA5820:03 Statistical Methods for Data Scientists

MA5830:03 Data Visualisation

MA5810:03 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
Internet of Things	JCU Online

Location

COURSE AVAILABLE AT	NOTES
JCU Cairns	Exit Only
JCU Online	This course is 100% online through a continuous delivery model.

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

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 sequence1 and 2. Five (5) years or more relevant industry experience in IT or Data Science/Data Analytics – up to 12 credit points from sequence 1 and 2 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 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 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 testamur	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	Students may receive an Award of Recognition in accordance with the Recognition of Academic Excellence Procedure

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