The Master of Data Science provides students with the knowledge and skills to understand and apply appropriate analytical methodologies to transform the way an organisation achieves its 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.
Students will gain a detailed knowledge of contemporary data management and analysis technologies, including those for data collection and storage, visualisation, internet-based applications, and software project management.
Students will also acquire essential skills in high performance computing.
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.
- CITS1401 Computational Thinking with Python (6)
- CITS1402 Relational Database Management Systems (6)
- STAT1400 Statistics for Science (6)
- STAT2401 Analysis of Experiments (6)
- STAT2402 Analysis of Observations (6)
Take all units (48 points):
- CITS4009 Computational Data Analysis (6)
- CITS4407 Open Source Tools and Scripting (6)
- CITS5503 Cloud Computing (6)
- CITS5504 Data Warehousing (6)
- CITS5508 Machine Learning (6)
- CITS5553 Data Science Capstone Project (6)
- STAT4064 Applied Predictive Modelling (6)
- STAT4066 Bayesian Computing and Statistics (6)
Option – Group A
Take unit(s) to the value of 24 points, including a minimum of 12 points at Level 5.
- CITS4402 Computer Vision (6)
- CITS4403 Computational Modelling (6)
- CITS4404 Artificial Intelligence and Adaptive Systems (6
- CITS4419 Mobile and Wireless Computing (6)
- CITS5011 Data Science Research Project Part 1 (6)
- CITS5012 Data Science Research Project Part 2 (6)
- CITS5014 Data Science Research Project Part 1 (6)
- CITS5015 Data Science Research Project Part 2 (6)
- CITS5505 Agile Web Development (6)
- CITS5506 The Internet of Things (6)
- CITS5507 High Performance Computing (6)
- GENG5505 Project Management and Engineering Practice (6)
- INMT5526 Business Intelligence (6)
- MGMT5504 Data Analysis and Decision Making (6)
- PHYS4021 Frontiers in Quantum Computation (6)
- PUBH4401 Biostatistics I (6)
- PUBH5769 Biostatistics II (6)
- PUBH5785 Introductory Analysis of Linked Health Data (6)
- PUBH5802 Advanced Analysis of Linked Health Data (6)
- STAT4063 Computationally Intensive Methods in Statistics (6)
- STAT4065 Multilevel and Mixed-Effects Modelling (6)
- STAT4067 Applied Statistics and Data Visualisation (6)
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;
(b) the equivalent of a UWA weighted average mark of at least 65 per cent;
(c) completed Mathematics Applications ATAR, or equivalent, as recognised by UWA.