STATISTICAL FOUNDATIONS OF DATA SCIENCE

The course provides an overview of some advanced statistical methods for data science. The focus is on understanding advantages and limitations of each approach, interpretation, and main applications in various disciplines, particularly in economics, business, and management. Students will learn how to solve several supervised and unsupervised learning tasks, including regression, classification, clustering, and Bayesian inference.

Solid knowledge of basic probability, descriptive statistics and statistical inference, including hypothesis testing and confidence intervals; see, for example, Chapters 1–8 of Ross (2017). Working knowledge of R is welcome but not mandatory. Luiss Preliminary Courses for Master's degree - Recommended: Statistics, Probability Suggested: R, Mathematics

Knowledge and understanding: The course will offer key statistical tools to investigate interrelationships between predictors and a continuous or categorical outcome. It will also discuss clustering and principles of Bayesian inference. Strengths, weaknesses, use cases, and interpretation of the results of each method will be discussed in depth. Applying knowledge and understanding: On successful completion of this course students will be able to: - Appreciate the different statistical methods for prediction of continuous and categorical outcomes. - Select, implement, and interpret the most appropriate statistical predictive tools in a range of real-world applications. - Cluster observations according to similar patterns and summarize multivariate datasets for information retrieval. Making judgements: Students are expected to be able to choose the appropriate statistical method to pursue their aims with data analysis, taking into consideration data limitations and comparative performance. Students will demonstrate fluency with the software and interpretation of the results. Throughout the entire course, students will be stimulated to consider strengths and weaknesses of the different methods discussed in class.

• Introduction • Probability and Statistics recap • Principles of regression • Simple and multivariate linear regression • Cross-validation • Principles of classification • Logistic regression • Clustering • Principles of Bayesian inference • Bayesian linear regression

• James, G., Witten D., Hastie T. & Tibshirani R. (2021). An Introduction to Statistical Learning: With Applications in R. 2nd Ed. Springer. [main] • Bishop C. (2006). Pattern Recognition and Machine Learning. Springer. • Gelman A., Carlin J., Stern H., Dunson D., Vehtari A., Rubin D. (2013). Bayesian Data Analysis. 3rd Ed. Chapman & Hall. • Hastie T., Tibshirani R., Friedman J. (2009). The Elements of Statistical Learning. 2nd Ed. Springer. • Hoff P. (2009). A First Course in Bayesian Statistical Methods. Springer. • Ross S. M. (2017). Introductory statistics. 4th Ed. Elsevier.

Lectures and Lab sessions.

Assignment (1/3) Written final exam (2/3).

An interview to verify understanding and motivation.