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Course Overview
Eligibility
Career & Study Pathways
Quality & Recognition
Course Content
Database Systems
Data modelling, principles of database design, data manipulation languages and database application techniques. Includes an introduction to DBMS internals and advanced databases. Design and implementation of a database application using SQL and stored procedures.
Data Visualisation
An introduction to statistical and visualisation tools for the exploratory analysis of data. Covers what makes an effective data visualisation and how to create interactive data visualisations, with a strong focus on storytelling skills.
Fundamentals of Data Science
A broad overview of Data Science in the modern world, covering databases, data analytics, data mining, Bayesian statistics, statistical software, econometrics, machine learning and business forecasting.
Business Economics
An introduction to economic analysis and policy, in particular decision-making in business. Covers tools to use economic principles in decision-making and understanding of the broader economic environment.
Economics of Strategy
Covers the fundamentals of Game Theory and its applications, including market competition among large firms, designing of incentive contracts, bidding at auctions, and bargaining.
Multivariate Analysis
Methodological background in Multivariate Analysis including principal component analysis, canonical correlation analysis, cluster analysis, factor analysis and discriminant analysis.
Statistical Inference
General inference theory based on maximum likelihood and Bayes methods. Covers estimation, confidence set construction and hypothesis testing.
Foundations of Computer Science
Core computer science foundations course (or Principle of Programming as alternative).
Principle of Programming
Alternative to Foundations of Computer Science.
Machine Learning and Data Mining
Machine learning and data mining methods (or Data Mining and its Business Applications as alternative).
Data Mining and its Business Applications
Alternative to Machine Learning and Data Mining.
Data Science and Decisions Project A
Research and communication skills in data science through project work (6 UOC).
Data Science and Decisions Project B
Research and communication skills in data science through project work (6 UOC).
Data Science and Decisions Project
Full 12 UOC research project option as an alternative to Project A and Project B.
Common Questions
Students must complete one of four specialisations, each comprising 30 UOC: Computational Data Science and Decisions (specialising in computational methods to manipulate, understand and predict data), Behavioural Data Science and Decisions (interpreting, understanding and predicting data for use in business), Business Data Science and Decisions (methods of interpreting, understanding and predicting data for use in business), and Quantitative Data Science and Decisions (mathematical and statistical methods to interpret, understand and predict data).
For domestic students, Commonwealth Supported Places (CSP) are available with an indicative first year fee of $10,000 and a fee to complete the degree of $20,000 (2026 figures). For domestic full-fee-paying students, the indicative first year fee is $43,500 and the fee to complete the degree is $88,000. For international students, the indicative first year fee is $62,000 and the fee to complete the degree is $128,000. All fees are subject to annual review and are a guide only.
Yes. The graduate certificate and graduate diploma provide an articulation pathway into the Master of Data Science and Decisions. Graduates who have completed the Graduate Certificate (7959) with a WAM of 65 may apply to transfer and will receive up to 24 UOC of Advanced Standing. Students who have completed the full 48 UOC of the Graduate Diploma (5959) with a WAM of 65 may apply to transfer and will receive up to 48 UOC of Advanced Standing. Students who have completed 24 UOC of the Graduate Diploma (5959) with a WAM of 65 may apply to transfer and will receive up to 24 UOC of Advanced Standing.
Applicants must have a recognised bachelor degree (or equivalent qualification) with a substantial quantitative component in a cognate discipline (such as Business, Commerce, Engineering, Mathematics, Medicine, Science, Social Science, or Technology) and an overall average of 70 or higher as determined by the UNSW Postgraduate Coursework Entry Calculator. Assumed knowledge includes a mathematical background of at least one course in several variable calculus and one course in linear algebra at the university undergraduate level.
The program is taught by leading academics across three different schools: the School of Mathematics and Statistics (Faculty of Science), the School of Computer Science and Engineering (Faculty of Engineering), and the School of Economics (Faculty of Business). UNSW is ranked #2 in Australia and #48 globally for Mathematics, #1 in Australia and #25 globally for Engineering & Technology, and #2 in Australia and #40 globally for Economics and Econometrics (QS World University Rankings by Subject, 2025).
Application
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Master of Nursing · Griffith University
