Learn five sampling techniques that outperform simple random sampling, with Python code for each method.
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Test all five linear regression assumptions in Python before trusting your coefficients or p-values.
Explore how Pareto, log-normal, and Cauchy distributions differ from normal and why confusing them is costly.
Learn how DoWhy automatically identifies causal estimands using the Lalonde dataset in three steps.
Estimate the Average Treatment Effect in Python using DoWhy with regression and propensity score matching on real data.
Learn how to stress-test causal effect estimates in DoWhy using refutation methods on the Lalonde dataset.
Learn how to build and test causal inference models in Python using the DoWhy library.
Learn five methods to move beyond correlation and establish causality in your data analysis work.
Discover five essential visualizations that separate statistical signal from noise in your data analysis workflow.
Learn five statistical validation checks that ensure machine learning models perform reliably in production environments.