The statistical foundation every data scientist needs. Probability, distributions, CLT, hypothesis testing, A/B experiments, Bayesian statistics, regression analysis with regularization, time series forecasting with ARIMA and Prophet, causal inference with DiD and RD, and Monte Carlo simulation.
1-month free Pro trial included
Probability fundamentals, common distributions (Normal, Binomial, Poisson), and the Central Limit Theorem that makes statistical inference possible
p-values, statistical power, t-tests, chi-square tests, running rigorous A/B tests, linear regression, and Bayesian thinking
Bayesian vs frequentist inference, prior and posterior distributions, Bayesian updating, credible intervals vs confidence intervals, and PyMC for probabilistic programming
Multiple linear regression assumptions and diagnostics, logistic regression for classification, regularization (Ridge, Lasso, Elastic Net), and interpreting coefficients correctly
Decomposition, stationarity and differencing, ARIMA and SARIMA models, Facebook Prophet, autocorrelation, and evaluating forecasts with MAE/MAPE/RMSE
Randomized controlled trials, confounding and Simpson's paradox, difference-in-differences, regression discontinuity, instrumental variables, and multi-armed bandits
SciPy statistical tests, bootstrapping and permutation tests, Monte Carlo simulation, multiple testing corrections, and building a reproducible statistical analysis workflow
