
Let's practice data science thinking through a probability problem

Let's practice data science thinking through a probability problem

Part 1: The basics — discretization of time, censoring and the life table

Randomization usually balances confounders in experiments, but what happens when it doesn't?

Part 2: Avoiding burnout, learning strategies and the superpower of solitude

Part 1: Why continuous learning matters for data scientists & what to study

How experimentation is more powerful than knowing counterfactuals

An explanation of the causal assumption implicit in prescriptive modeling and how to satisfy it

Mastering the fundamentals of bagging and boosting with simple examples

Part 6: Balancing multiple objectives using the weights and preemptive goal programming approaches

Where the gambler's fallacy shows up in data science and what to do about it