Topics

Applied Statistics

How statistics gets used in practice: data analysis workflows, experiment design, forecasting, and the statistical thinking behind machine learning.

• Data Analysis

  Practical workflows for exploring, cleaning, and interpreting real datasets, from the first look at the data through to a conclusion you can defend.

• Experiments & Causality

  How to design A/B tests and experiments that answer the question you asked, and how to separate genuine cause from coincidence in observational data.

• Forecasting & Time Series

  Building and validating forecasts from time-ordered data: trend, seasonality, backtesting, and recognising when a model has quietly stopped working.

• Machine Learning Statistics

  The statistical thinking behind machine learning: validation, overfitting, class imbalance, and reading model metrics without fooling yourself.