Statistics, ML fundamentals, experiment design, and communicating results. Focused on the judgement calls that separate junior data scientists from senior ones.
A product team asks you to predict which users will churn next month. You have labelled historical data with 95% non-churners and 5% churners. What is the most important thing to address before training a model?
