Master of Data Science (coursework or coursework and dissertation) (62530)

The University of Western Australia

About

UWA's admission requirements for some postgraduate courses have changed for Semester 2, 2020 to facilitate student access to study during the COVID-19 situation.

In many cases, these changes may not be extended beyond 2020.

Contact Future Students for more information.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.

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	Prerequisites: Mathematics Applications ATAR or WACE Mathematics 2C/2D or MATH1720 Mathematics Fundamentals or equivalent or higher	lectures: 2 hours per week; labs: 2 hours per week; workshop: 1 hour per week
S2	CITS1402	Relational Database Management Systems	Prerequisites: ATAR Mathematics Applications or MATH1720 Mathematics Fundamentals or equivalent or higher Incompatibility: CITS2232 Databases	lectures: 2 hours per week; labs: 2 hours per week
S1, S2	STAT1400	Statistics for Science	Prerequisites: Mathematics Applications ATAR or Mathematics Methods ATAR or WACE Mathematics 2C/2D or MATH1720 Mathematics Fundamentals or equivalent or higher Incompatibility: STAT1520 Economic and Business Statistics	lectures: 3 hours; labs: 2 hours
S1	STAT2401	Analysis 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)	lectures: 2 hours per week; labs: 2 hours per week
S2	STAT2402	Analysis 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)	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	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	lectures: 2 hours per week; labs: 2 hours per week
S1	CITS4407	Open Source Tools and Scripting	Prerequisites: Enrolment in 62530 Master of Data Science or 62510 Master of Information Technology or 42630 Master of Business Analytics.
S2	CITS5503	Cloud Computing	Prerequisites: Enrolment in 62530 Master of Data Science or 62510 Master of Information Technology or 62550 Master of Professional Engineering (Software Engineering specialisation) or HON-CMSSE Computer Science and Software Engineering [Honours] or 42630 Master of Business Analytics and completion of 12 points of programming-based units
S1	CITS5504	Data Warehousing	Prerequisites: Enrolment in (62530 Master of Data Science or 62510 Master of Information Technology or 42630 Master of Business Analytics) and (CITS1402 Relational Database Management Systems or BUSN5101 Programming for Business or BUSN5002 Fundamentals of Business Analytics). Incompatibility: CITS4243 Advanced Databases, CITS3401 Data Warehousing and Data Mining (formerly CITS3401 Data Exploration and Mining)	lectures: 2 hours per week; labs: 2 hours per week
S1	CITS5508	Machine Learning	Prerequisites: Enrolment in 62530 Master of Data Science or 62510 Master of Information Technology or 62550 Master of Professional Engineering (Software Engineering specialisation) or the HON-CMSSE Computer Science and Software Engineering [Honours] or 42630 Master of Business Analytics 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
S2	CITS5553	Data Science Capstone Project	Prerequisites: enrolment in the 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
S2	STAT4064	Applied Predictive Modelling	Prerequisites: STAT2401 Analysis of Experiments or STAT2402 Analysis of Observations or STAT2062 Fundamentals of Probability with Applications	lectures: 2 hours per week; computer laboratories: 2 hours per week
S1	STAT4066	Bayesian Computing and Statistics	Prerequisites: STAT1400 Statistics for Science or STAT1520 Economic and Business Statistics or MATH1002 Mathematical Methods 2 or MATH1012 Mathematical Theory and Methods 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

AVAILABILITY	UNITCODE	UNITNAME	UNIT REQUIREMENTS	CONTACT HOURS
S1	CITS4402	Computer Vision	Prerequisites: enrolment in one of the following: Master of Professional Engineering; Honours in Computer Science and Software Engineering; 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
S1	CITS4403	Computational Modelling	Prerequisites: Enrolment in 62530 Master of Data Science or 62550 Master of Professional Engineering (Software Engineering specialisation) or HON-CMSSE Computer Science and Software Engineering [Honours] and completion of 6 points of programming-based units Incompatibility: CITS7211 Modelling Complex Systems
S2	CITS4404	Artificial Intelligence and Adaptive Systems	Prerequisites: Enrolment in 62530 Master of Data Science or 62510 Master of Information Technology or 62550 Master of Professional Engineering (Software Engineering specialisation) or (Electrical and Electronic Engineering specialisation) or HON-CMSSE Computer Science and Software Engineering [Honours] and completion of 12 points of programming-based units Incompatibility: CITS7212 Computational Intelligence
S2	CITS4419	Mobile 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
N/A	CITS5011	Data 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
S1, S2	CITS5012	Data 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, S2	CITS5014	Data Science Research Project Part 1	Prerequisites: enrolment in the Master of Data Science (62530) 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. Incompatibility: CITS5011 Data Science Research Project Part 1
S1, S2	CITS5015	Data Science Research Project Part 2	Prerequisites: enrolment in the Master of Data Science (62530) and CITS5014 Data Science Research Project Part 1 or CITS5011 Data Science Research Project Part 1 Incompatibility: CITS5012 Data Science Research Project Part 2
S1	CITS5505	Agile Web Development	Prerequisites: Enrolment in the 62530 Master of Data Science or 62510 Master of Information Technology and completion of 6 points of programming-based units Incompatibility: CITS4230 Internet Technologies, CITS3403 Agile Web Development (formerly CITS3403 Web and Internet Technologies)	lectures: 2 hours per week; labs: 2 hours per week
S2	CITS5506	The Internet of Things	Prerequisites: Enrolment in 62530 Master of Data Science or 62510 Master of Information Technology or 62550 Master of Professional Engineering (Software Engineering specialisation) and completion of 6 points of programming-based units	lectures: 2 hours per week; labs: 3 hours per week
S2	CITS5507	High Performance Computing	Prerequisites: Enrolment in 62530 Master of Data Science or 62510 Master of Information Technology or 62550 Master of Professional Engineering (Software Engineering specialisation) and completion of 12 points of programming-based units Incompatibility: CITS3402 High Performance Computing, SHPC4002 High Performance Computing
S1, S2	GENG5505	Project 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; 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	lectures: 26 hours; practical classes: 13 hours
S2	INMT5526	Business Intelligence	Prerequisites: For Master of Business Analytics students: BUSN5101 Programming for Business	lectures/seminars/workshops: up to 3 hours per week
S1, S2	MGMT5504	Data Analysis and Decision Making	Incompatibility: MGMT5513 Data Driven Decision Making	lectures/seminars/workshops: up to 3 hours per week
N/A	PHYS4021	Frontiers in Quantum Computation	Prerequisites: enrolment in the Master of Physics or Master of Professional Engineering or Master of Data Science or Honours in Computer Science and Software Engineering or the Honours in Mathematics and Statistics or Honours in Physics	lectures/workshop: 3 x 45 minutes per week
S1, S2	PUBH4401	Biostatistics I	Prerequisites: enrolment in honours or postgraduate courses	lectures: 2 hours per week; tutorials: 1.5 hours per week (for face-to-face mode only)
S2	PUBH5769	Biostatistics II	Prerequisites: PUBH4401 Biostatistics I or equivalent training/experience	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	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.	offered intensively (1 week full-time)
S2	STAT4063	Computationally Intensive Methods in Statistics	Prerequisites: STAT3062 Statistical Science or STAT2401 Analysis of Experiments or STAT2402 Analysis of Observations	3 hours per week
N/A	STAT4065	Multilevel and Mixed-Effects Modelling	Prerequisites: STAT2401 Analysis of Experiments or STAT2402 Analysis of Observations or STAT3405 Introduction to Bayesian Computing and Statistics or STAT4066 Bayesian Computing and Statistics. Incompatibility: STAT3401 Advanced Data Analysis	lectures: 2 hours per week; labs: 2 hours per week
S2	STAT4067	Applied Statistics and Data Visualisation	Prerequisites: STAT1400 Statistics for Science or STAT1520 Economic and Business Statistics or MATH1002 Mathematical Methods 2 or MATH1012 Mathematical Theory and Methods Incompatibility: STAT3406 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.

Entry 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

Institution