Covers three basic concepts of data science together with the corresponding techniques: Sampling to estimate properties of a population (Bootstrap), random assignment and experiments to make causal inferences (randomization test), and model selection to enable good predictions (cross-validation). Emphasizes practical data handling and programming skills in Python.
Covers three basic concepts of data science together with the corresponding techniques: Sampling to estimate properties of a population (Bootstrap), random assignment and experiments to make causal inferences (randomization test), and model selection to enable good predictions (cross-validation). Emphasizes practical data handling and programming skills in Python.
