Statistical Modeling and Inference for Data Science

The course coding-oriented course covers the concepts underpinning and the applications of statistical modeling/inference. Students build models with real-world data and modern data science toolkits in Python and R like Scikit-learn and TidyModels. Concepts covered in this course include: Foundations in probability including basic rules, bayes formula, basic distributions; Sampling and the central limit theorem; Bootstrapping, confidence intervals, hypothesis testing, multiple testing; Linear models, basic and multiple regression, inference for regression, regularization; Classification, logistic regression and tree-based methods; Prediction, model interpretation, model evaluation.