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Unit of study_
OSTA5003: Computational Statistical Methods
2026 unit information
The objectives of this unit of study are to develop an understanding of modern computationally intensive methods for statistical learning, inference, exploratory data analysis and data mining. Advanced computational methods for statistical learning will be introduced, including clustering, density estimation, smoothing, predictive models, model selection, combinatorial optimisation methods, sampling methods, the Bootstrap and Monte Carlo approach. In addition, the unit will demonstrate how to apply the above techniques effectively for use on large data sets in practice.
Unit details and rules
Managing faculty or University school:
Science
| Study level | Postgraduate |
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| Academic unit | Mathematics and Statistics Academic Operations |
| Credit points | 6 |
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Prerequisites:
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None |
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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A good understanding of statistics, including hypothesis testing and regression modelling, and substantial statistical computing experience. For example, both ODAT5011 and ODAT5021 or a unit like STAT5002. |
At the completion of this unit, you should be able to:
- LO1. Formulate domain/context specific questions and identify appropriate statistical analysis.
- LO2. Formulate, evaluate and interpret appropriate statistical models to describe the relationships between multiple factors.
- LO3. Perform statistical machine learning using a given classifier and create a cross-validation scheme to calculate the prediction accuracy.
- LO4. Understand, perform and interpret various unsupervised machine learning methods
- LO5. Construct and implement resampling techniques to understand the behaviour of statistical models.
- LO6. Create a reproducible report to communicate outcomes using a programming language.
Unit availability
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
| Session | MoA ? | Location | Outline ? |
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PG Online Session 1B 2025
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Online | Online Program | |
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PG Online Session 2A 2025
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Online | Online Program |
| Session | MoA ? | Location | Outline ? |
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PG Online Session 1B 2026
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Online | Online Program |
Outline unavailable
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PG Online Session 2A 2026
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Online | Online Program |
Outline unavailable
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Modes of attendance (MoA)
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