Graduate Certificate of Data Science

Graduate Certificate of Data Science

Course code

300111

Course type

GRC – Graduate Certificate (AQF Level 8)

Division

Tropical Environments and Societies

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

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.

Course Structure

CORE SUBJECTS

CAROUSEL 1

MA5800:03 Foundations for Data Science

MA5820:03 Statistical Methods for Data Science

MA5830:03 Data Visualisation

CP5804:03 Database Systems

Campus

COURSE AVAILABLE AT

NOTES

JCU Online

This course is 100% online through a carousel delivery model.

Candidature

Expected time to complete

36 weeks in continuous carousel model (12CP) or equivalent part-time

Explanation – 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
requisites

Nil

Course includes mandatory professional placement(s)

No

Special assessment
requirements

Nil

Professional accreditation
requirements

Nil – this course is not accredited.

Maximum allowed Pass
Conceded (PC) grade

Nil

Supplementary exam for
final subject

Not applicable

Advanced Standing

Eligibility

Students may apply for advanced standing for previous tertiary study in accordance with the Advanced Standing and Articulation policy and associated procedures

Advanced standing 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 advanced standing.

* 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 advanced standing may be granted for more than two-thirds of the new award, where there is subject equivalence between the awards

Currency

Advanced standing will be granted only for subjects completed in the 10 years prior to the commencement of this course

Expiry

Advanced standing gained for any subject shall be cancelled 12 years after the date of the examination upon which the advanced standing is based if, by then, the student has not completed this course.

Other restrictions

Advanced standing 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
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 advanced standing for all subjects completed under this course

Special Awards

Not applicable