Graduate Certificate of Data Science

Handbook year	Information valid for students commencing in 2024
Course code	300111
Course type	GRC – Graduate Certificate (AQF Level 8)
Owner	Academy
College	Science and Engineering

Award Requirements

Course pre-requisites	Completion of an AQF level 7 bachelor degree; or five (5) years or more of 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	Mathematical Methods (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.

Credit points	12 credit points as per course structure
Post-admission requirements	Computer and internet access is required.
Course learning outcomes	On successful completion of the Graduate Certificate of Data Science, graduates will be able to: Critically describe specialised theoretical and technical knowledge in data science Retrieve, analyse, synthesise and evaluate knowledge from a range of sources Plan and conduct reliable, efficient analysis of data by applying relevant methods, techniques and tools with some guidance Demonstrate effective applications of nominated computing languages and computational tools for data queries, management, analysis and visualisation Identify and generate solutions to unpredictable problems, especially related to tropical, rural, remote or Indigenous contexts, by applying knowledge and skills of data science with high-level judgement Communicate fundamental data concepts and methodologies of data science 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 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 Certificate 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.

MA5800:03 Foundations for Data Science

MA5820:03 Statistical Methods for Data Scientists

MA5830:03 Data Visualisation

CP5804:03 Database Systems

COURSE AVAILABLE AT	NOTES
JCU Online	This course is 100% online through a carousel delivery model.
JCU Cairns	A full-time student will study up to 25% of this course online.

Expected time to complete	36 weeks in continuous carousel model (12cp) for JCU Online students; 6 months full time for on-campus students; or equivalent part-time Explanation for JCU Online students – four subjects takes 32 weeks, but spanning the mid-year and end-of-year breaks will add four weeks in most years.
Maximum time to complete	2 years Explanation – "part-time" modality for carousel offerings is not expected to be at one half of the full time rate. 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. 36 weeks x 1.5 + 1 year LoA = 106 weeks. In Carousel model a leave of absence cannot be a full year if the student wishes to come back into the next subject.
Maximum leave of absence	1 year

Course progression requisites	Nil
Course includes mandatory professional placement(s)	No
Special assessment requirements	Nil
Professional accreditation requirements	Nil – this course is not accredited.
Supplementary exam for final subject	Not applicable

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 6 credit points. 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	6 credit points except where a student transfers from one JCU award to another, then credit may be granted for more than two-thirds of the new award, 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 12 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 title	GRADUATE CERTIFICATE OF DATA SCIENCE
Approved abbreviation	GCertDataSc
Inclusion of majors on testamur	Not applicable – this course does not have majors
Exit with lesser award	Not applicable
Course articulation	Students who complete this course are eligible for entry to the Graduate Diploma of Data Science and 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