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 title
- Master of Data Science (coursework or coursework and dissertation)
- Award abbreviation
- Course code
- Course type
- master's degree by coursework or by coursework and dissertation
- current / 2019
- Administered by
- Faculty of Engineering and Mathematical Sciences
- CRICOS code
- 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.)
- 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
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
- Locations offered
- UWA (Perth)
- Domestic fee type
- Postgraduate fee-paying/FEE-HELP
- Course Coordinator(s)
- Dr Jianxin Li
- 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.
|Key to availability of units:|
|S1 = Semester 1; S2 = Semester 2; SS = summer teaching period; N/A = not available in 2019|
All units have a value of six points unless otherwise stated.
Note: Units that are indicated as N/A may be available in 2020 or 2021.
Note: Students are advised to refer to the recommended study guides available on the EMS website.
Students who have completed degree studies in a non-cognate area, or equivalent as recognised by the Faculty, must complete relevant conversion units up to the value of 24 points from this group, as advised by the Faculty.
|S1, S2||CITS1401||Computational Thinking with Python|
|S2, SS||CITS1402||Relational Database Management Systems|
|S1, S2||STAT1400||Statistics for Science|
|S1||STAT2401||Analysis of Experiments|
|S2||STAT2402||Analysis of Observations|
Take all units (36 points):
|S2||CITS4009||Introduction to Data Science|
|S2||STAT4064||Applied Predictive Modelling|
|S1||STAT4066||Bayesian Computing and Statistics|
Take unit(s) to the value of 36 points, including a minimum of 18 points at Level 5.
Note: Enrolment in the Data Science Research Project is by invitation only.
|S2||CITS4404||Artificial Intelligence and Adaptive Systems|
|S1||CITS4407||Open Source Tools and Scripting|
|S2||CITS4419||Mobile and Wireless Computing|
|S1, S2||CITS5011||Data Science Research Project Part 1|
|S1, S2||CITS5012||Data Science Research Project Part 2|
|S1, S2||CITS5013||Data Science Research Project Part 3 (12 points)|
|S1||CITS5505||Agile Web Development|
|S2||CITS5506||The Internet of Things|
|S2||CITS5507||High Performance Computing|
|S1, S2||GENG5505||Project Management and Engineering Practice|
|S1, S2||MGMT5504||Data Analysis and Decision Making|
|N/A||PUBH5802||Advanced Analysis of Linked Health Data|
|S2||STAT4063||Computationally Intensive Methods in Statistics|
|S1||STAT4065||Multilevel and Mixed-Effects Modelling|
|S2||STAT4067||Applied Statistics and Data Visualisation|
Applicability of the Student Rules, policies and procedures
1.(1) The http://matrix-prod.its.uwa.edu.au/handbooks2019/postgraduate/student-procedures'>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).
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 ranking and selection
Articulations and 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 Faculty 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 Faculty to be awarded the Graduate Diploma in Data Science.
(2) Units must be selected in accordance with the course structure, as set out in these rules.
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.
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
Collaborative international articulation agreements
13. 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.