Course overview

Description

The Master of Data Science will prepare its graduates for a career in this rapidly expanding field of work. It will equip them with the necessary knowledge and skills to understand and apply appropriate analytical methodologies to transform the way an organisation achieves its goals and objectives, to deal effectively with large data management tasks, to master the statistical and machine learning foundations on which data analytics is built, and to evaluate and communicate the effectiveness of new technologies.

Course title
Master of Data Science (coursework or coursework and dissertation)
Award abbreviation
MDSc
Course code
62530
Course type
Master's degree by coursework or by coursework and dissertation
Status
Current / 2023
Administered by
Physics, Mathematics and Computing
CRICOS code
093310E

Course details

Intake periods
Beginning of year and mid-year
Attendance type
Full- or part-time (Student visa holders should read Education Services for Overseas Students Act 2000 for more information.)
Articulation
The Master of Data Science has the following exit awards: 62230 Graduate Certificate in Data Science (24 points) (24 points), 62330 Graduate Diploma in Data Science (48 points) (48 points)
Credit points required
96
A standard full-time load is 24 points per semester.
Standard course duration
1.5 years full-time (or equivalent part-time) comprising 72 points of taught units and 24 points of admission credit, as recognised and granted by the School
Maximum course duration
2 years full-time (or equivalent part-time) comprising up to 96 points of taught study (see Rule 5 for further information)
Time limit
5 years
Delivery mode
Internal
Locations offered
UWA (Perth)
Domestic fee type
Postgraduate fee-paying/FEE-HELP
Available to international students
Yes. For information on international student fees see 'Student Procedures: Fees'. (Enquiries: https://www.uwa.edu.au/askuwa)
Course Coordinator(s)
Associate Professor Wei Liu
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:
S1
Semester 1
S2
Semester 2
N/A
not available in 2023 – may be available in 2024 or 2025
NS
non-standard teaching period

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

Students who have completed degree studies in a non-cognate area, or equivalent as recognised by the School, must complete relevant conversion units up to the value of 24 points from this group, as advised by the School.

AvailabilityUnit codeUnitnameUnit requirementsContact hours
S1, S2CITS1401Computational Thinking with Python
Prerequisites
Mathematics Applications ATAR
or MATH1720 Mathematics Fundamentals or equivalent
Incompatibility
CITS2401 Computer Analysis and Visualisation
Lectures: 2-hours per week; Labs: 2-hours per week; WorkshopS: 1-hour per week
S2CITS1402Relational Database Management Systems
Prerequisites
Mathematics Applications ATAR
or MATH1720 Mathematics Fundamentals or equivalent
Incompatibility
CITS2232 Databases
lectures: 2 hours per week; labs: 2 hours per week
S1STAT2401Analysis of Experiments
Prerequisites
Mathematics Applications ATAR
or MATH1720 Mathematics Fundamentals or equivalent
lectures: 2 hours per week; labs: 2 hours per week
S2STAT2402Analysis of Observations
Prerequisites
Mathematics Applications ATAR
or MATH1720 Mathematics Fundamentals or equivalent
Lectures: 3-hours per week; Practical Classes: 2-hours per week; Laboratories: 1-hour per week

Take all units (48 points):

AvailabilityUnit codeUnitnameUnit requirementsContact hours
S2CITS4009Computational Data Analysis
Prerequisites
Enrolment in
62510 Master of Information Technology
or 62530 Master of Data Science
or 62560 Master of Renewable and Future Energy
or 62550 Master of Professional Engineering (SP-ECHEM Chemical Engineering specialisation
or SP-EMINI Mining Engineering specialisation
or the SP-ESOFT Software Engineering specialisation)
or 72530 Master of Environmental Science (SP-SSDSC Sensing and Spatial Data Science specialisation)

or (
Enrolment in
Bachelor of Engineering (Honours) or an associated Combined Degree
and Successful completion of
96 points
lectures: 2 hours per week; labs: 2 hours per week
S1CITS4012Natural Language Processing
Prerequisites
Enrolment in in the
HON-CMSSE Computer Science and Software Engineering [Honours]
or the 62510 Master of Information Technology
or the 62530 Master of Data Science
or
the 62550 Master of Professional Engineering and the SP-ESOFT Software Engineering specialisation
or
the BH008 Bachelor of Advanced Computer Science [Honours] and the MJD-ARIDM Artificial Intelligence major
and completion of 12 points of programming-based units
Lectures: 2-hours per week; Laboratories: 2-hours per week.
S1CITS4407Open Source Tools and Scripting
Prerequisites
Enrolment in
in the the 62510 Master of Information Technology
or the 62530 Master of Data Science
or the 72530 Master of Environmental Science
or the 42630 Master of Business Analytics
S1CITS5504Data Warehousing
Prerequisites
CITS1402 Relational Database Management Systems and Completion of 6 points of programming-based units
and Enrolment in
62510 Master of Information Technology
or 62530 Master of Data Science
or 42630 Master of Business Analytics
Incompatibility
CITS3401 Data Warehousing
lectures: 2 hours per week; labs: 2 hours per week
S1CITS5508Machine Learning
Prerequisites
Enrolment in
Computer Science and Software Engineering [Honours]
or 62510 Master of Information Technology
or 62530 Master of Data Science
or 63550 Master of Engineering
or 42630 Master of Business Analytics
or 62550 Master of Professional Engineering
or 53560 Master of Physics (specialised in Medical Physics)
or BH008 Bachelor of Advanced Computer Science [Honours] and the MJD-CDSDM Computing and Data Science major
or the MJD-ARIDM Artificial Intelligence major
) and completion of 12 points of programming-based units
lectures: 2 hours per week; labs: 2 hours per week for 11 weeks from week 2
S2CITS5553Data Science Capstone Project
Prerequisites
Enrolment in
in the 62530 Master of Data Science and completion of 24 points of Level 4/Level 5 units
Lectures: 10-hours; Project Mentor Sessions: 4-hours; Project: 60-hours
S1STAT4064Applied Predictive Modelling
Prerequisites

Course Enrolment in
the HON-MTHST Mathematics and Statistics [Honours]
or the 62530 Master of Data Science
or the 70550 Master of Bioinformatics
and STAT2401 Analysis of Experiments
and STAT2402 Analysis of Observations
or STAT2062 Fundamentals of Probability with Applications
Incompatibility
STAT3406 Applied Statistics and Data Visualisation
Lectures: 2-hours per week; Computer Labs: 2-hours per week
S2STAT4066Bayesian Computing and Statistics
Prerequisites

Course Enrolment in
the MJD-CDSDM Computing and Data Science major
or the HON-MTHST Mathematics and Statistics [Honours]
or the 62530 Master of Data Science
and STAT2401 Analysis of Experiments
and STAT2402 Analysis of Observations
or STAT2062 Fundamentals of Probability with Applications
Incompatibility
STAT3405 Introduction to Bayesian Computing and Statistics
Lectures: 2-hours per week; Computer Labs: 3-hours per fortnight; Practical Classes: 1-hour per fortnight

Take unit(s) to the value of 24 points, including a minimum of 12 points at Level 5.

Note: Enrolment in the Data Science Research Project is by invitation only.

Group A
AvailabilityUnit codeUnitnameUnit requirementsContact hours
S1, S2BUSN5003Data Storytelling
Prerequisites
Enrolment in
42630 Master of Business Analytics
or 41690 Master of Marketing
or 62530 Master of Data Science
or 42270 Graduate Certificate in Business Analytics
Up to three hours per week.
S1CITS4402Computer Vision
Prerequisites
Enrolment in
HON-CMSSE Computer Science and Software Engineering
or 62530 Master of Data Science
or
62550 Master of Professional Engineering and SP-EBIOM Biomedical Engineering specialisation
or SP-EELEC Electrical and Electronic Engineering specialisation
or SP-ESOFT Software Engineering specialisation
or
53560 Master of Physics and SP-MEDPH Medical Physics
or
BH008 Bachelor of Advanced Computer Science [Honours] and MJD-ARIDM Artificial Intelligence
or Bachelor of Engineering (Honours) or an associated Combined Degree
and Successful completion of
96 points and ( CITS2401 Computer Analysis and Visualisation
or CITS1401 Computational Thinking with Python

and MATH1012 Mathematical Theory and Methods )
S2CITS4403Computational Modelling
Prerequisites
Enrolment in in the
HON-CMSSE Computer Science and Software Engineering [Honours]
or the 62510 Master of Information Technology
or the 62530 Master of Data Science
or
the 62550 Master of Professional Engineering and the SP-ESOFT Software Engineering specialisation
or
the BH008 Bachelor of Advanced Computer Science [Honours] and MJD-ICYDM International Cybersecurity major
or the MJD-ARIDM Artificial Intelligence major
S1CITS4404Artificial Intelligence and Adaptive Systems
Prerequisites
Enrolment in in the
HON-CMSSE Computer Science and Software Engineering [Honours]
or the 62510 Master of Information Technology
or the 62530 Master of Data Science
or
the 62550 Master of Professional Engineering and the SP-EELEC Electrical and Electronic Engineering specialisation
or the SP-ESOFT Software Engineering specialisation
or
the BH008 Bachelor of Advanced Computer Science [Honours] and the MJD-ARIDM Artificial Intelligence major
and completion of 12 points of programming-based units
S1CITS4419Mobile and Wireless Computing
Prerequisites
Enrolment in
HON-CMSSE Computer Science and Software Engineering
or 62530 Master of Data Science
or 62550 Master of Professional Engineering ( Software Engineering specialisation
or Electrical and Electronic Engineering specialisation
or
Enrolment in
BH008 Bachelor of Advanced Computer Science [Honours] and MJD-ICYDM International Cybersecurity

or
Enrolment in
Bachelor of Engineering (Honours) or an associated Combined Degree
and 96 points
and CITS3002 Computer Networks
N/ACITS5011Data Science Research Project Part 1
Prerequisites
enrolment in
the Master of Data Science (62530)
and 24 points of Level 4/Level 5 units completed within the course with the equivalent of a UWA weighted average mark (WAM) of at least 70 percent.
Incompatibility
CITS5014 Data Science Research Project Part 1
and CITS7201/CITS7202 Computer Science and Software Engineering Research Project Part 1/Part 2
N/ACITS5012Data Science Research Project Part 2
Prerequisites
enrolment in
the Master of Data Science (62530) and completed CITS5011 Data Science Research Project Part 1
Incompatibility
CITS5015 Data Science Research Project Part 2
S1, S2CITS5014Data Science Research Project Part 1
Prerequisites
Enrolment in
in the 62530 Master of Data Science and completion of 18 points of Level 4/Level 5 units completed within the course with the equivalent of a UWA weighted average mark (WAM) of at least 70 percent
Incompatibility
CITS5011 Data Science Research Project Part 1
S1, S2CITS5015Data Science Research Project Part 2
Prerequisites
Enrolment in
in the 62530 Master of Data Science
and CITS5014 Data Science Research Project Part 1
Incompatibility
CITS5012 Data Science Research Project Part 2
S2CITS5503Cloud Computing
Prerequisites

Enrolment in
( HON-CMSSE Computer Science and Software Engineering
or 62510 Master of Information Technology
or 62530 Master of Data Science
or 42630 Master of Business Analytics
or BH008 Bachelor of Advanced Computer Science [Honours]
or MJD-ICYDM International Cybersecurity
or MJD-CDSDM Computing and Data Science

and 12 points of programming-based units )
or Enrolment in 62550 Master of Professional Engineering Software Engineering specialisation
or
Enrolment in
Bachelor of Engineering (Honours) or an associated Combined Degree
and 120 Points
and 12 points of programming-based units
S1CITS5505Agile Web Development
Prerequisites
Enrolment in
in the 62510 Master of Information Technology
or the 62530 Master of Data Science and completion of 6 points of programming-based units
Incompatibility
CITS3403 Agile Web Development
Lectures: 2-hours per week; Laboratories: 2-hours per week
S2CITS5506The Internet of Things
Prerequisites
Enrolment in in the
62510 Master of Information Technology
or the 62530 Master of Data Science
or
the 62550 Master of Professional Engineering and the SP-ESOFT Software Engineering specialisation
and completion of 6 points of programming-based units
Lectures: 2-hours per week; Labs: 3-hours per week
S2CITS5507High Performance Computing
Prerequisites

Enrolment in
( 62510 Master of Information Technology
or 62530 Master of Data Science

and 12 points of programming-based units )
or Enrolment in 62550 Master of Professional Engineering Software Engineering specialisation
or
Enrolment in
Bachelor of Engineering (Honours) or an associated Combined Degree
and 120 Points including 12 points of programming-based units
Incompatibility
CITS3402 High Performance Computing
or SHPC4002 Advanced Computational Physics
S1, S2GENG5505Project Management and Engineering Practice
Prerequisites
Enrolment in
62550 Master of Professional Engineering
or Enrolment in
62510 Master of Information Technology
or Enrolment in
62530 Master of Data Science
or Enrolment in
62540 Master of Ocean Leadership
or Enrolment in
62560 Master of Renewable and Future Energy
or Enrolment in 62570 Master of Offshore and Coastal Engineering
or
Enrolment in
( Bachelor of Engineering (Honours) or an associated Combined Degree

and Successful completion of
120 Points )
lectures: 26 hours; practical classes: 13 hours
S2INMT5526Business Intelligence
Prerequisites
INMT5518 Models for Logistics, Operations and Services (ID 3338)
or BUSN5002 Fundamentals of Business Analytics (ID 7181)
or Unit(s) BUSN5101 Programming for Business (ID 7189) or equivalent
lectures/seminars/workshops: up to 3 hours per week
S1, S2MGMT5504Data Analysis and Decision Making
Incompatibility
MGMT5513 Data Driven Decision Making
lectures/seminars/workshops: up to 3 hours per week
S1PHYS4021Quantum Information and Computing
Prerequisites
Enrolment in
CM015 Bachelor of Science Frontier Physics and Master of Physics
or 53560 Master of Physics
or 65550 Master of Quantum Technology and Computing
or HON-PHYSC Physics
or HON-MTHST Mathematics and Statistics
or
HON-CMSSE Computer Science and Software Engineering
or MJD-ICYDM International Cybersecurity
or 62530 Master of Data Science
and MATH1012 Mathematical Theory and Methods or equivalent
Incompatibility
PHYS3005 Quantum Computation
Lectures/Workshop: 3 x 45 minutes per week
S1, S2PUBH4401Biostatistics I
Prerequisites
enrolment in
honours
or postgraduate courses
lectures: 2 hours per week; tutorials: 1.5 hours per week
S2PUBH5769Biostatistics II
Prerequisites
PUBH4401 Biostatistics I or equivalent training/experience
lectures: 2 hours per week; tutorials: 1.5 hours per week
NSPUBH5785Analysis of Linked Health DataNoneoffered intensively (1 week full-time)
N/APUBH5802Advanced Analysis of Linked Health Data
Prerequisites
PUBH5785 Introductory Analysis of Linked Health Data (ID 3940) (formerly PUBH8785 Introductory Analysis of Linked Health Data) or equivalent skills and experience.
offered intensively (1 week full-time)
S2STAT4063Computationally Intensive Methods in Statistics
Prerequisites
Course Enrolment in
the HON-MTHST Mathematics and Statistics [Honours]
or the 62530 Master of Data Science

and STAT3062 Statistical Science
or STAT4064 Applied Predictive Modelling
3-hours per week
S1STAT4065Multilevel and Mixed-Effects Modelling
Prerequisites
Course Enrolment 62530 Master of Data Science
and
STAT2401 Analysis of Experiments
and STAT2402 Analysis of Observations
or STAT2062 Fundamentals of Probability with Applications
Incompatibility
STAT3401 Advanced Data Analysis
Lectures: 2-hours per week; labs: 2-hours per week
S2STAT5061Statistical Data Science
Prerequisites

Course Enrolment in
the 62530 Master of Data Science
and STAT2401 Analysis of Experiments
and STAT2402 Analysis of Observations
or STAT2062 Fundamentals of Probability with Applications
Incompatibility
STAT3064 Statistical Learning
and STAT4067 Applied Statistics and Data Visualisation
Lectures: 2-hours per week; Laboratory: 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 and Communication and Research Skills modules

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) and the Communication and Research Skills module (the CARS module).

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

(3) A student must successfully complete the ACE module within the first teaching period of their enrolment. Failure to complete the module within this timeframe will result in the student's unit results from this teaching period being withheld. These results will continue to be withheld until students avail themselves of a subsequent opportunity to achieve a passing grade in the ACE module. In the event that students complete units in subsequent teaching periods without completing the ACE module, these results will similarly be withheld. Students will not be permitted to submit late review or appeal applications regarding results which have been withheld for this reason and which they were unable to access in the normally permitted review period.

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, or an equivalent qualification, as recognised by UWA;

and

(b) the equivalent of a UWA weighted average mark of at least 65 per cent;

and

(c) completed ATAR Mathematics Applications, or equivalent, as recognised 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.(1) This course has the following exit awards:

  • 62230 Graduate Certificate in Data Science (24 points)
  • 62330 Graduate Diploma in Data Science (48 points)

(2) A student who withdraws from the Master of Data Science course before completing it, but after completing Level 4 and Level 5 units to the value of 24 points, may apply to the School to be awarded the Graduate Certificate in Data Science.

(3) A student who withdraws from the Master of Data Science course before completing it, but after completing Level 4 and Level 5 units to the value of 48 points, may apply to the School to be awarded the Graduate Diploma in Data Science.

Course structure

7.(1) The course consists of units to a total value of 96 points (maximum value) which include conversion units to a value of 24 points.

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

(3) Students who have completed a bachelor's degree with a major in a cognate area, or equivalent as recognised by the Faculty are granted credit for conversion units up to a value of 24 points.

Satisfactory progress

8. To make satisfactory progress a student must pass units to a point value greater than 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 Communication and Research Skills module (the CARS 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 in terms of Rule 8 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. To be awarded the degree with distinction a student must achieve a course weighted average mark (WAM) of at least 80 per cent which is calculated based on—

(a) all units above Level 3 attempted as part of the course that are awarded a final percentage mark;

(b) all relevant units above Level 3 undertaken in articulating courses of this University that are awarded a final percentage mark;

and

(c) all units above Level 3 completed at this University that are credited to the master's degree course.

Deferrals

13. Applicants awarded admission to the course are entitled to a deferral of up to 12 months, as per the University Policy on: Admissions (Coursework).

Additional rules
Collaborative international articulation agreements

14. A student from a cognate background granted admission into the course via a collaborative international articulation agreement is required to complete 96 credit points of the course including units to the value of up to 60 points from Group A of which at least 30 points must be undertaken at Level 5.