Programma

The course aims to introduce the application of statistical methods to challenging real-world settings, touching on both fundamental concepts of statistics and modern ideas of the statistical learning paradigm. The nature will be essentially illustrative, featuring noteworthy applications in health, business, industry, and sports analytics, keeping the mathematical level up to that of a second-year undergraduate course in economics or engineering. A (very) indicative list of two-hour seminars is as follows: Introduction to some core ideas of statistical inference. Statistical models: making sense of data. Statistical learning: a set of tools. Case study I: health (frequentist regularization via lasso and boosting). Case study II: business (text mining, empirical Bayes, the EM algorithm). Case study III: industry (time series forecasting & control). Case study IV: sports analytics (full Bayes modelling).