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 / 2020
- Administered by
- Faculty of Engineering and Mathematical Sciences
- 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
- Course Coordinator(s)
- Dr 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 2020; 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 2021 or 2022.
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.
| Availability | Unitcode | Unitname | Unit requirements | Contact hours |
|---|---|---|---|---|
| S1, S2 | CITS1401 | Computational Thinking with Python |
| lectures: 2 hours per week; labs: 2 hours per week; workshop: 1 hour per week |
| S2 | CITS1402 | Relational Database Management Systems |
| lectures: 2 hours per week; labs: 2 hours per week |
| S1, S2 | STAT1400 | Statistics for Science |
| lectures: 3 hours; labs: 2 hours |
| S1 | STAT2401 | Analysis of Experiments |
| lectures: 2 hours per week; labs: 2 hours per week |
| S2 | STAT2402 | Analysis of Observations |
| lectures: 3 hours per week; practical classes: 2 hours per week; labs: 1 hour per week |
Take all units (48 points):
| Availability | Unitcode | Unitname | Unit requirements | Contact hours |
|---|---|---|---|---|
| S2 | CITS4009 | Computational Data Analysis |
| lectures: 2 hours per week; labs: 2 hours per week |
| S1 | CITS4407 | Open Source Tools and Scripting |
| |
| S2 | CITS5503 | Cloud Computing |
| |
| S1 | CITS5504 | Data Warehousing |
| lectures: 2 hours per week; labs: 2 hours per week |
| S1 | CITS5508 | Machine Learning |
| lectures: 2 hours per week; labs: 2 hours per week for 11 weeks from week 2 |
| S2 | CITS5553 | Data Science Capstone Project |
| lectures: 10 hours; project mentor sessions: 4 hours; project: 60 hours |
| S2 | STAT4064 | Applied Predictive Modelling | lectures: 2 hours per week; computer laboratories: 2 hours per week | |
| S1 | STAT4066 | 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
| Availability | Unitcode | Unitname | Unit requirements | Contact hours |
|---|---|---|---|---|
| S1 | CITS4402 | Computer Vision |
| |
| S1 | CITS4403 | Computational Modelling |
| |
| S2 | CITS4404 | Artificial Intelligence and Adaptive Systems |
| |
| S2 | CITS4419 | Mobile and Wireless Computing |
| |
| N/A | CITS5011 | Data Science Research Project Part 1 |
| |
| S1, S2 | CITS5012 | Data Science Research Project Part 2 |
| |
| S1 | CITS5014 | Data Science Research Project Part 1 |
| |
| S1, S2 | CITS5015 | Data Science Research Project Part 2 | ||
| S1 | CITS5505 | Agile Web Development | lectures: 2 hours per week; labs: 2 hours per week | |
| S2 | CITS5506 | The Internet of Things |
| lectures: 2 hours per week; labs: 3 hours per week |
| S2 | CITS5507 | High Performance Computing |
| |
| S1, S2 | GENG5505 | Project Management and Engineering Practice |
| lectures: 26 hours; practical classes: 13 hours |
| S2 | INMT5526 | Business Intelligence |
| lectures/seminars/workshops: up to 3 hours per week |
| S1, S2 | MGMT5504 | Data Analysis and Decision Making |
| lectures/seminars/workshops: up to 3 hours per week |
| N/A | PHYS4021 | Frontiers in Quantum Computation |
| lectures/workshop: 3 x 45 minutes per week |
| S1, S2 | PUBH4401 | Biostatistics I |
| lectures: 2 hours per week; tutorials: 1.5 hours per week (for face-to-face mode only) |
| S2 | PUBH5769 | Biostatistics II |
| lectures: 2 hours per week; tutorials: 1.5 hours per week (for face-to-face mode only) |
| NS | PUBH5785 | Introductory Analysis of Linked Health Data | offered intensively (1 week full-time) | |
| NS | PUBH5802 | Advanced Analysis of Linked Health Data |
| offered intensively (1 week full-time) |
| S2 | STAT4063 | Computationally Intensive Methods in Statistics | 3 hours per week | |
| S1 | STAT4065 | Multilevel and Mixed-Effects Modelling | lectures: 2 hours per week; labs: 2 hours per week | |
| S2 | STAT4067 | Applied Statistics and Data Visualisation | lectures: 3 hours per week; labs 1 hour per week |
- Example 1
- Course Study Plan: PSP-62530-1
- Example 2
- Course Study Plan: PSP-62530-2
- Example 3
- Course Study Plan: PSP-62530-1.5
See also the rules for the course and the Student Rules.
Rules
Applicability of the Student Rules, policies and procedures
1.(1) The http://handbooks.uwa.edu.au/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).
(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, 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 Mathematics Applications ATAR, 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 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.
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 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 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.
Additional rules
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.