Graduate Certificate of Data Science
Handbook year | Information valid for students commencing in 2023 |
Course code | 300111 |
Course type | GRC – Graduate Certificate (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 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 番茄社区 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. |
Academic Requirements for Course Completion
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:
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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 Certificate of Data Science. |
Reasonable adjustments | All 番茄社区 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
CAROUSEL 1
MA5800:03 Foundations for Data Science
MA5820:03 Statistical Methods for Data Scientists
MA5830:03 Data Visualisation
CP5804:03 Database Systems
Location
COURSE AVAILABLE AT | NOTES |
番茄社区 Online | This course is 100% online through a carousel 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 | 36 weeks in continuous carousel model (12cp) for 番茄社区 Online students; 6 months full time for on-campus students; or part-time equivalent Explanation for 番茄社区 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 |
Progression
Course progression | Nil |
Course includes mandatory professional placement(s) | No |
Special assessment | Nil |
Professional accreditation | Nil – this course is not accredited. |
Supplementary exam for | Not applicable |
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: 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 番茄社区 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 Details
Award title | GRADUATE CERTIFICATE OF DATA SCIENCE |
Approved abbreviation | GCertDataSc |
Inclusion of majors on | 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 |