Start with the question
Begin with the question, observational unit and structure of the data. A method is easier to choose—and harder to misuse—once these are clear.
Teaching
Teaching quantitative thinking
I teach undergraduate data science and statistics as a Casual Academic in the University of Sydney’s School of Mathematics and Statistics. I want students to see quantitative methods as a way of reasoning about evidence—not a sequence of commands to reproduce.
01 Current role
Introductory courses are where students form habits that shape every later analysis.
I work with students on moving from a question to a defensible analysis: identifying the observational unit, understanding the structure of the data, choosing methods that fit the question, interpreting output in context and writing code another person can follow. Technical fluency matters, but it should serve an intelligible argument.
An approach to quantitative teaching
Interpret in context
Translate output into context, units and uncertainty. A correct calculation is incomplete if its meaning cannot be stated precisely.
Leave an auditable path
Organise data and code so that the path from raw material to conclusion remains visible, reviewable and possible to repeat.
02 Teaching & research
Teaching sharpens the same habits that strong clinical research requires: clear questions, proportionate claims and transparent reasoning.
It also informs my interest in how students and prevocational doctors enter academic work. Early research experience should involve genuine contribution, careful supervision and the gradual development of independence—not simply proximity to a project.
03 Student resources
The Resources library is designed for concise, original notes and worked examples in statistics, data science and reproducible analysis.
It will remain selective: fewer notes, written around real points of confusion, and revised as the explanations improve.
Browse teaching notes