Master of Data Science (coursework or coursework and dissertation)

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 overview

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 / 2021
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 Faculty
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)
Professional accreditation
Master of Data Science (coursework or coursework and dissertation) is accredited by: Australian Computer Society (ACS).
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 'Schedule 2: 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 2021; NS = non-standard teaching period

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

Note: Units that are indicated as N/A may be available in 2022 or 2023.

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.

AvailabilityUnitcodeUnitnameUnit requirementsContact hours
S1, S2CITS1401Computational Thinking with Python
Prerequisites:
Mathematics Applications ATAR or MATH1720 Mathematics Fundamentals or equivalent or higher
Co-requisites:
Nil.
Incompatibility:
Nil.
lectures: 2 hours per week; labs: 2 hours per week; workshop: 1 hour per week
S2CITS1402Relational Database Management Systems
Prerequisites:
Mathematics Applications ATAR or MATH1720 Mathematics Fundamentals or equivalent or higher
Co-requisites:
Nil.
Incompatibility:
CITS2232 Databases
lectures: 2 hours per week; labs: 2 hours per week
S1STAT2401Analysis of Experiments
Prerequisites:
Nil
Co-requisites:
Nil.
Incompatibility:
Nil
lectures: 2 hours per week; labs: 2 hours per week
S2STAT2402Analysis of Observations
Prerequisites:
Nil
Co-requisites:
Nil.
Incompatibility:
Nil
Lectures: 3-hours per week; Practical Classes: 2-hours per week; Laboratories: 1-hour per week

Take all units (48 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
Co-requisites:
Nil.
Incompatibility:
Nil.
lectures: 2 hours per week; labs: 2 hours per week
S1, S2CITS4012Natural Language Processing
Prerequisites:
enrolment in Bachelor of Advanced Computer Science [Honours] or Master of Data Science and 12 points of programming based-units.
Co-requisites:
Nil
Incompatibility:
Nil
lectures: 2 hours per week; laboratories: 2 hours per week.
S1CITS4407Open Source Tools and Scripting
Prerequisites:
enrolment in Master of Information Technology or Master of Data Science or Master of Business Analytics
Co-requisites:
Nil
Incompatibility:
Nil
S1CITS5504Data Warehousing
Prerequisites:
enrolment in: (Master of Information Technology or Master of Data Science or Master of Business Analytics) and (CITS1402 Relational Database Management Systems or BUSN5101 Programming for Busines or BUSN5002 Fundamentals of Business Analytics).
Co-requisites:
Nil.
Incompatibility:
CITS3401 Data Warehousing
lectures: 2 hours per week; labs: 2 hours per week
S1CITS5508Machine Learning
Prerequisites:
enrolment in the BH008 Bachelor of Advanced Computer Science [Honours] (Artificial Intelligence major or Computing and Data Science major) or HON-CMSSE Computer Science and Software Engineering or 62510 Master of Information Technology or 62530 Master of Data Science or 62550 Master of Professional Engineering (Software Engineering) or 42630 Master of Business Analytics and completion of 12 points of programming-based units
Co-requisites:
Nil
Incompatibility:
Nil
lectures: 2 hours per week; labs: 2 hours per week for 11 weeks from week 2
S2CITS5553Data Science Capstone Project
Prerequisites:
Course Enrolment in the Master of Data Science And 24 Points of Level 4/Level 5 units
Co-requisites:
Nil
Incompatibility:
Nil
Lectures: 10-hours; Project Mentor Sessions: 4-hours; Project: 60-hours
S2STAT4064Applied Predictive Modelling
Prerequisites:
STAT2401 Analysis of Experiments (ID 390) or STAT2402 Analysis of Observations (ID 389) or STAT2062 Fundamentals of Probability with Applications (ID 5019)
Co-requisites:
Nil
Incompatibility:
Nil
Lectures: 2-hours per week; Computer Labs: 2-hours per week
S1STAT4066Bayesian Computing and Statistics
Prerequisites:
STAT1400 Statistics for Science or STAT1520 Economic and Business Statistics or MATH1012 Mathematical Theory and Methods or STAT2401 Analysis of Experiments (ID 390) or STAT2402 Analysis of Observations (ID 389)
Co-requisites:
Nil
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 18 points at Level 5.

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

AvailabilityUnitcodeUnitnameUnit requirementsContact hours
S1, S2BUSN5003Data Storytelling
Prerequisites:
Enrolment in Master of Business Analytics, and BUSN5002 Fundamentals of Business Analytics or Master of Data Science
Co-requisites:
Nil.
Incompatibility:
Nil.
Up to three hours per week.
S1CITS4402Computer Vision
Prerequisites:
enrolment in the MJD-ARTIF Artificial Intelligence or HON-CMSSE Computer Science and Software Engineering or 62530 Master of Data Science or 62550 Master of Professional Engineering (Biomedical Engineering or Electrical and Electronic Engineering or Software Engineering)
Co-requisites:
Nil.
Incompatibility:
CITS4240 Computer Vision
N/ACITS4403Computational Modelling
Prerequisites:
enrolment in the MJD-ARTIF Artificial Intelligence or MJD-INTCY International Cybersecurity or HON-CMSSE Computer Science and Software Engineering or 62510 Master of Information Technology or 62530 Master of Data Science or 62550 Master of Professional Engineering (Software Engineering)
Co-requisites:
Nil.
Incompatibility:
CITS7211 Modelling Complex Systems
N/ACITS4404Artificial Intelligence and Adaptive Systems
Prerequisites:
enrolment in the MJD-ARTIF Artificial Intelligence or HON-CMSSE Computer Science and Software Engineering or 62510 Master of Information Technology or 62530 Master of Data Science or 62550 Master of Professional Engineering (Electrical and Electronic Engineering or Software Engineering) and completion of 12 points of programming-based units
Co-requisites:
Nil.
Incompatibility:
CITS7212 Computational Intelligence
N/ACITS4419Mobile and Wireless Computing
Prerequisites:
enrolment in the MJD-INTCY International Cybersecurity or HON-CMSSE Computer Science and Software Engineering or 62530 Master of Data Science or 62550 Master of Professional Engineering (Electrical and Electronic Engineering or Software Engineering)
Co-requisites:
Nil.
Incompatibility:
CITS7219 Mobile and Wireless Computing
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.
Co-requisites:
nil
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
Co-requisites:
Nil
Incompatibility:
CITS5015 Data Science Research Project Part 2
S1, S2CITS5014Data Science Research Project Part 1
Prerequisites:
enrolment in the Master of Data Science And 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.
Co-requisites:
Nil
Incompatibility:
CITS5011 Data Science Research Project Part 1
S1, S2CITS5015Data Science Research Project Part 2
Prerequisites:
enrolment in the Master of Data Science And CITS5014 Data Science Research Project Part 1 Or CITS5011 Data Science Research Project Part 1
Co-requisites:
Nil
Incompatibility:
CITS5012 Data Science Research Project Part 2
S2CITS5503Cloud Computing
Prerequisites:
enrolment in the BH008 Bachelor of Advanced Computer Science [Honours] (International Cybersecurity major or Computing and Data Science major) or HON-CMSSE Computer Science and Software Engineering or 62510 Master of Information Technology or 62530 Master of Data Science or 62550 Master of Professional Engineering (Software Engineering) or 42630 Master of Business Analytics and completion of 12 points of programming-based units
Co-requisites:
Nil.
Incompatibility:
Nil.
S1CITS5505Agile Web Development
Prerequisites:
enrolment in (Master of Data Science or Master of Information Technology) and (completion of 6 points of programming-based units)
Co-requisites:
Nil.
Incompatibility:
CITS3403 Agile Web Development
Lectures: 2-hours per week; Laboratories: 2-hours per week
S2CITS5506The Internet of Things
Prerequisites:
completion of 6 points of programming-based units
Co-requisites:
Nil
Incompatibility:
Nil
Lectures: 2-hours per week; Labs: 3-hours per week
S2CITS5507High Performance Computing
Prerequisites:
enrolment in: (Master of Data Science or Master of Information Technology or Master of Professional Engineering [Software Engineering]) and (completion of 12 points of programming-based units)
Co-requisites:
Nil.
Incompatibility:
CITS3402 High Performance Computing and SHPC4002 High Performance Computing
S1, S2GENG5505Project Management and Engineering Practice
Prerequisites:
enrolment in the Master of Professional Engineering or the Master of Information Technology or the Master of Engineering in Oil and Gas or the Master of Data Science or the Master of Ocean Leadership or the Master of Renewable and Future Energy
Co-requisites:
Nil.
Incompatibility:
Nil.
lectures: 26 hours; practical classes: 13 hours
S2INMT5526Business Intelligence
Prerequisites:
Nil
Co-requisites:
Nil.
Incompatibility:
Nil.
lectures/seminars/workshops: up to 3 hours per week
S1, S2MGMT5504Data Analysis and Decision Making
Prerequisites:
Nil.
Co-requisites:
Nil.
Incompatibility:
MGMT5513 Data Driven Decision Making
lectures/seminars/workshops: up to 3 hours per week
S1PHYS4021Frontiers in Quantum Computation
Prerequisites:
MATH1012 Mathematical Theory and Methods or equivalent or higher.
Co-requisites:
Nil
Incompatibility:
PHYS3005 Quantum Computation (ID 7770)
lectures/workshop: 3 x 45 minutes per week
S1, S2PUBH4401Biostatistics I
Prerequisites:
enrolment in honours or postgraduate courses
Co-requisites:
Nil
Incompatibility:
Nil
lectures: 2 hours per week; tutorials: 1.5 hours per week (for face-to-face mode only)
S2PUBH5769Biostatistics II
Prerequisites:
PUBH4401 Biostatistics I or equivalent training/experience
Co-requisites:
Nil.
Incompatibility:
Nil.
lectures: 2 hours per week; tutorials: 1.5 hours per week (for face-to-face mode only)
NSPUBH5785Introductory Analysis of Linked Health Data
Prerequisites:
Nil
Co-requisites:
Nil
Incompatibility:
Nil
offered intensively (1 week full-time)
N/APUBH5802Advanced Analysis of Linked Health Data
Prerequisites:
PUBH5785 Introductory Analysis of Linked Health Data (formerly PUBH8785 Introductory Analysis of Linked Health Data) or equivalent skills and experience. The computing component of the unit assumes a facile competence in the preparation of computing syntax for programs such as SPSS, SAS or STATA and familiarity with the statistical analysis of linked data files at an introductory to intermediate level.
Co-requisites:
Nil.
Incompatibility:
Nil.
offered intensively (1 week full-time)
S2STAT4063Computationally Intensive Methods in Statistics
Prerequisites:
STAT3062 Statistical Science or STAT2401 Analysis of Experiments
Co-requisites:
Nil
Incompatibility:
Nil
3 hours per week
S1STAT4065Multilevel and Mixed-Effects Modelling
Prerequisites:
STAT2401 Analysis of Experiments (ID 390) or STAT2402 Analysis of Observations (ID 389) or STAT3405 Introduction to Bayesian Computing and Statistics (ID 5923) or STAT4066 Bayesian Computing and Statistics (ID 6204)
Co-requisites:
Nil
Incompatibility:
STAT3401 Advanced Data Analysis
lectures: 2 hours per week; labs: 2 hours per week
S2STAT4067Applied Statistics and Data Visualisation
Prerequisites:
STAT1400 Statistics for Science (ID 388) or STAT1520 Economic and Business Statistics (ID 397) or STAT2401 Analysis of Experiments (ID 390) or MATH1012 Mathematical Theory and Methods (ID 6013)
Co-requisites:
Nil
Incompatibility:
STAT3406 Applied Statistics and Data Visualisation
lectures: 3 hours per week; labs 1 hour 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 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 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 under Rule 8 is assigned the status of 'Good Standing'.

(2) Unless the Faculty 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.