UWA's admission requirements for some postgraduate courses have changed for Semester 2, 2020 to facilitate student access to study during the COVID-19 situation. In many cases, these changes may not be extended beyond 2020. Contact Future Students for more information.

Graduate Certificate in Data Science

In a data-driven world, there is growing demand in industry for people who can develop and interpret models for data analysis. The Graduate Certificate in Data Science equips students with skills in predictive modelling and computational data analysis. Successful completion of the certificate will allow students to articulate into the Master of Data Science.

Course overview

Course title
Graduate Certificate in Data Science
Award abbreviation
GradCertDatSc
Course code
62230
Course type
postgraduate certificate
Status
current / 2020
Administered by
Faculty of Engineering and Mathematical Sciences

Course details

Intake periods
Mid-year only
Attendance type
full-time only
Credit points required
24
A standard full-time load is 24 points per semester.
Standard course duration
0.5 Years
Time limit
2 years
Delivery mode
internal
Locations offered
UWA (Perth)
Domestic fee type
Postgraduate fee-paying/FEE-HELP
Course Coordinator(s)
Associate Professor Rachel Cardell-Oliver
Fees
Visit the fees calculator.

Prospective students should see the Future Students website for details on admission requirements, intake periods, fees, availability to international students, careers information etc.

Course structure

Key to availability of units:
S2 = Semester 2

All units have a value of six points unless otherwise stated.

Take all units (24 points):

AvailabilityUnitcodeUnitnameUnit requirementsContact hours
S2CITS4009Computational Data Analysis
Prerequisites:
enrolment in the Master of Data Science or Master of Information Technology or Master of Professional Engineering (Chemical Engineering specialsiation or Mining Engineering specialisation or Software Engineering specialisation) or Master of Renewable and Future Energy
lectures: 2 hours per week; labs: 2 hours per week
S2CITS4404Artificial Intelligence and Adaptive Systems
Prerequisites:
Enrolment in 62530 Master of Data Science or 62510 Master of Information Technology or 62550 Master of Professional Engineering (Software Engineering specialisation) or (Electrical and Electronic Engineering specialisation) or HON-CMSSE Computer Science and Software Engineering [Honours] and completion of 12 points of programming-based units
Incompatibility:
CITS7212 Computational Intelligence
S2CITS5503Cloud Computing
Prerequisites:
Enrolment in 62530 Master of Data Science or 62510 Master of Information Technology or 62550 Master of Professional Engineering (Software Engineering specialisation) or HON-CMSSE Computer Science and Software Engineering [Honours] or 42630 Master of Business Analytics and completion of 12 points of programming-based units
S2STAT4064Applied Predictive Modelling
Prerequisites:
STAT2401 Analysis of Experiments or STAT2402 Analysis of Observations or STAT2062 Fundamentals of Probability with Applications
lectures: 2 hours per week; computer laboratories: 2 hours per week

See also the rules for the course and the Student Rules.

Rules

Applicability of the Student Rules, policies and procedures

1.(1) The Student Rules apply to students in this course.

(2) The policy, policy statements and guidance documents and student procedures apply, except as otherwise indicated in the rules for this course.

Academic Conduct Essentials module

2.(1) Except as stated in (2), a student who enrols in this course for the first time irrespective of whether they have previously been enrolled in another course of the University, must undertake the Academic Conduct Essentials module (the ACE module).

(2) A student who has previously achieved a result of Ungraded Pass (UP) for the ACE module is not required to repeat the module.

English Language competency requirements

3. To be considered eligible for consideration for admission to this course an applicant must satisfy the University's English language competence requirement as set out in the University Policy on Admission: Coursework.

Admission requirements

4. To be considered for admission to this course an applicant must have—

(a) a bachelor's degree with a major in Data Science, or an equivalent qualification, as recognised by UWA; or

(b)

(i) a bachelor's degree, or an equivalent qualification, as recognised by UWA; and

(ii) at least two years of full-time documented relevant industry experience considered by UWA to be sufficient to permit satisfactory completion of the course; and

(iii) a demonstrated data science and coding ability, as assessed by UWA.

Admission ranking and selection

5. Where relevant, admission will be awarded to the highest ranked applicants or applicants selected based on the relevant requirements.

Articulations and exit awards

6. This course does not form part of an articulated sequence.

Course structure

7.(1) The course consists of units to a total value of 24 points.

(2) Units must be selected in accordance with the course structure, as set out in these rules.

Satisfactory progress

8. To make satisfactory progress in a calendar year a student must pass units to a value of at least half the total value of units in which they remain enrolled after the final date for withdrawal without academic penalty.

9. A student who has not achieved a result of Ungraded Pass (UP) for the ACE module when their progress status is assessed will not have made satisfactory progress even if they have met the other requirements for satisfactory progress in Rule 8.

Progress status

10.(1) A student who makes satisfactory progress is assigned the status of 'Good Standing'.

(2) Unless the relevant board determines otherwise because of exceptional circumstances—

(a) a student who does not make satisfactory progress for the first time under Rule 8 is assigned a progress status of 'On Probation';

(b) a student who does not make satisfactory progress for the second time under Rule 8 is assigned a progress status of 'Suspended';

(c) a student who does not make satisfactory progress for the third time under Rule 8 is assigned a progress status of 'Excluded'.

11. A student who does not make satisfactory progress in terms of Rule 9 is assigned the progress status of 'On Probation', unless they have been assigned a progress status of 'Suspended' or 'Excluded' for failure to meet other satisfactory progress requirements in Rule 8.

Award with distinction

12. This rule is not applicable to this course.