Statistics

Statistics is a major offered by the School of Mathematics and Statistics. Units of study in this major are available at standard and advanced level.

About the major

Statistics is pervasive in all areas of the sciences, the social sciences, finance and business, and is the key paradigm used to assess the strength of evidence from all kinds of data. In a statistics major, students learn about theoretical, computational, and applied statistics, and probability theory. As part of the major students will apply the techniques that they learn to a variety of applications. Students learn about quantifying uncertainty, experimental design, probabilistic modelling and the latest techniques in statistical and machine learning. This major is essential training if you wish to become a professional statistician.

Advanced level units are available at all levels. Advanced units have more stringent prerequisites than standard level units and are significantly more demanding.

Requirements for completion

The Statistics major and minor requirements are listed in the Statistics unit of study table.

Contact and further information

sydney.edu.au/science/schools/school-of-mathematics-and-statistics

First year enquiries:


Other undergraduate enquiries:


All enquiries: +61 2 9351 5787

Major coordinator
Dr Michael Stewart
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Learning Outcomes

Students who graduate from Statistics will be able to demonstrate:

1. Exhibit a broad and coherent body of knowledge in fundamental principles of probability theory and statistics, including the principles of decision-making under uncertainty and statistical hypothesis testing.
2. Exhibit a deep and comprehensive knowledge of statistical reasoning and inference methods, the framework of statistical hypothesis testing and common statistical procedures.
3. Formulate statistical questions in a disciplinary context and identify and apply appropriate techniques and statistical reasoning to prepare and analyse data.
4. Analyse data in descriptive, interpretive and exploratory ways using graphical methods and visualisation tools.
5. Identify and address gaps in their statistical knowledge and skills by independently sourcing, collating and synthesising appropriate resources that extend their understanding of statistical concepts.
6. Communicate statistical concepts, methodology and results to diverse audiences using a variety of models including to facilitate data-driven decision-making.
7. Use computer resources and statistical programming languages to address a broad range of statistical questions.
8. Construct robust experimental designs using statistical principles.
9. Address practical and abstract statistical problems using a range of concepts, techniques and technologies, working professionally, ethically and responsibly and with consideration of cross-cultural perspectives, within collaborative, interdisciplinary teams.