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 / 2018
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)
Professor Amitava Datta
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; SS = summer teaching period; NS = non-standard teaching period

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

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.

AvailabilityUnitcodeUnitnameUnit requirements
S1, S2CITS1401Problem Solving and Programming
Prerequisites:
Mathematics Applications ATAR or WACE Mathematics 2C/2D or MATH1720 Mathematics Fundamentals or equivalent or higher
S2, SSCITS1402Relational Database Management Systems
Prerequisites:
Mathematics Applications ATAR or WACE Mathematics 2C/2D or MATH1720 Mathematics Fundamentals or equivalent or higher
Incompatibility:
CITS2232 Databases
S1, S2, SSCITS2401Computer Analysis and Visualisation
Prerequisites:
Mathematics Methods ATAR or WACE Mathematics 3A/3B or MATH1720 Mathematics Fundamentals or equivalent or higher
S1, S2STAT1400Statistics for Science
Prerequisites:
Mathematics Applications ATAR or Mathematics Methods ATAR or WACE Mathematics 2C/2D or MATH1720 Mathematics Fundamentals or equivalent or higher
Incompatibility:
STAT1510 Statistics A, STAT1520 Economic and Business Statistics
S1, S2, SSSTAT1520Economic and Business Statistics
Prerequisites:
Mathematics Methods ATAR or WACE Mathematics 3A/3B or MATH1720 Mathematics Fundamentals or ECON1111 Quantitative Methods for Business and Economics or MATH0038 Mathematical Analysis or equivalent or higher
Incompatibility:
STAT1400 Statistics for Science; STAT1510 Statistics A; STAT1530 Statistics B; STAT2210 Biometrics 1; MATH1020 Calculus, Statistics and Probability; SCIE1104 Science, Society and Data Analysis
S1STAT2401Analysis of Experiments
Prerequisites:
STAT1400 Statistics for Science or STAT1520 Economic and Business Statistics or MATH1002 Mathematical Methods 2 or MATH1012 Mathematical Theory and Methods or MATH1020 Calculus, Probability and Statistics (students enrolled in the Master of Data Science may take one of these units as a co-requisite)
Incompatibility:
STAT2227 Applied Linear Modelling
S2STAT2402Analysis of Observations
Prerequisites:
STAT1400 Statistics for Science or STAT1520 Economic and Business Statistics or MATH1002 Mathematical Methods 2 or MATH1012 Mathematical Theory and Methods or MATH1020 Calculus, Probability and Statistics (students enrolled in the Master of Data Science may take one of these units as a co-requisite)
Incompatibility:
STAT2226 Statistical Models for Data

Take all units (36 points):

AvailabilityUnitcodeUnitnameUnit requirements
S2CITS4009Introduction to Data Science
Prerequisites:
enrolment in the Master of Data Science or the Master of Information Technology
S2CITS5503Cloud Computing
Prerequisites:
enrolment in one of the following: Master of Professional Engineering; Honours in Computer Science and Software Engineering; Master of Information Technology; Master of Data Science
S1CITS5504Data Warehousing
Prerequisites:
enrolment in the Master of Information Technology (62510) or the Master of Data Science (62530)
Incompatibility:
CITS4243 Advanced Databases, CITS3401 Data Warehousing and Data Mining (formerly CITS3401 Data Exploration and Mining)
S1CITS5508Machine Learning
Prerequisites:
enrolment in the Master of Data Science or the Master of Information Technology
S2STAT4064Applied Predictive Modelling
Prerequisites:
STAT2401 Analysis of Experiments or STAT2402 Analysis of Observations or STAT2062 Fundamentals of Probability with Applications
S1STAT4066Bayesian Computing and Statistics
Prerequisites:
STAT1400 Statistics for Science; or STAT1520 Economic and Business Statistics; or MATH1002 Mathematical Methods 2; or MATH1020 Calculus, Probability and Statistics; or MATH1020 Calculus, Probability and Statistics
Incompatibility:
STAT3405 Introduction to 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.
Group A
AvailabilityUnitcodeUnitnameUnit requirements
S1CITS4008Scientific Communication
Incompatibility:
CITS7200 Scientific Communication
S1CITS4402Computer Vision
Prerequisites:
enrolment in one of the following: Master of Professional Engineering; Honours in Computer Science and Software Engineering; Master of Physics; Master of Data Science; for pre-2012 courses: enrolment in honours or a higher degree by coursework in Computer Science and Software Engineering
Incompatibility:
CITS4240 Computer Vision
S1CITS4403Computational Modelling
Prerequisites:
enrolment in one of the following: Master of Professional Engineering; Master of Data Science; Honours in Computer Science and Software Engineering; for pre-2012 courses: enrolment in honours or a higher degree by coursework in Computer Science and Software Engineering
Incompatibility:
CITS7211 Modelling Complex Systems
S2CITS4404Artificial Intelligence and Adaptive Systems
Prerequisites:
enrolment in the Master of Professional Engineering or Master of Data Science or Honours in Computer Science and Software Engineering; for pre-2012 courses: enrolment in honours or a higher degree by coursework in Computer Science and Software Engineering
Incompatibility:
CITS7212 Computational Intelligence
S1CITS4407Open Source Tools and Scripting
Prerequisites:
enrolment in the Master of Information Technology (62510) or the Master of Data Science (62530)
S2CITS4419Mobile and Wireless Computing
Prerequisites:
enrolment in the Master of Professional Engineering or Honours in Computer Science and Software Engineering or Master of Data Science; for pre-2012 courses: enrolment in honours or a higher degree by coursework in Computer Science and Software Engineering
Incompatibility:
CITS7219 Mobile and Wireless Computing
S1, S2CITS5011Data Science Research Project Part 1
Prerequisites:
enrolment in the Master of Data Science
Incompatibility:
CITS7201/CITS7202 Computer Science and Software Engineering Research Project Part 1/Part 2
S1, S2CITS5012Data Science Research Project Part 2
Prerequisites:
enrolment in Master of Data Science
S1, S2CITS5013Data Science Research Project Part 3 (12 points)
Prerequisites:
enrolment in the Master of Data Science
Co-requisites:
CITS5012 Data Science Research Project Part 2
S1CITS5505Agile Web Development
Prerequisites:
enrolment in the Master of Information Technology (62510) or the Master of Data Science (62530)
Incompatibility:
CITS4230 Internet Technologies, CITS3403 Agile Web Development (formerly CITS3403 Web and Internet technologies)
S2CITS5506The Internet of Things
Prerequisites:
enrolment in the Master of Information Technology (62510) or the Master of Data Science (62530)
S2CITS5507High Performance Computing
Prerequisites:
enrolment in one of the following: Master of Information Technology (62510); Graduate Certificate in Scientific and High Performance Computing (70260); Master of Data Science (62530)
Incompatibility:
CITS3402 High Performance Computing, 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; for pre-2012 courses: (GENG1003 Introduction to Professional Engineering or ENSC1001 Global Challenges in Engineering) and completion of 96 points towards an Engineering degree
Incompatibility:
CIVL4150 Engineering Practice, ELEC4332 Project Engineering Practice, MECH4400 Engineering for Sustainable Development
S2INMT5526Business Intelligence
S1, S2MGMT5504Data Analysis and Decision Making
Incompatibility:
MGMT5513 Data Driven Decision Making
S2PUBH5769Biostatistics II
Prerequisites:
PUBH4401 Biostatistics I or equivalent training/experience
NSPUBH5802Advanced 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.
S2STAT4063Computationally Intensive Methods in Statistics
Prerequisites:
STAT3062 Statistical Science or STAT2401 Analysis of Experiments or STAT2402 Analysis of Observations
S1STAT4065Multilevel and Mixed-Effects Modelling
Prerequisites:
(STAT2401 Analysis of Experiments and STAT2402 Analysis of Observations) or STAT3405 Introduction to Bayesian Computing and Statistics
Incompatibility:
STAT3401 Advanced Data Analysis
S2STAT4067Applied Statistics and Data Visualisation
Prerequisites:
STAT1400 Statistics for Science or STAT1520 Economic and Business Statistics or MATH1002 Mathematical Methods 2 or MATH1020 Calculus, Probability and Statistics
Incompatibility:
STAT3406 Applied Statistics and Data Visualisation

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

Rules

Applicability of the Student Rules, policies and procedures

1.(1) The 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 in—

(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.