Fundamentals of Data Analysis

Fondamenti di Analisi dei Dati — A.Y. 2026/27

How the course works

The course is organised in two modules.

Module 1 — Statistical data analysis. We build the vocabulary of data (description, probability, association, distributions), then use inference and regression models to explain a phenomenon, and close with causal reasoning — why adjusting for a variable in a regression is a causal act, and when it is not enough — and with how to tell the story honestly.

Module 2 — Predictive analysis and data representation. The goal changes from explaining to predicting: empirical risk minimisation, baselines, splits and cross-validation; the same models re-read as predictors, plus a few new ones; and ways to represent data — features, clusters, densities, principal components — in the service of the analysis rather than as an end in themselves.

Each module ends with a written test. The practical work is a project on an assigned dataset, carried out in two parts and presented at the exam. The schedule says what happens when.

Materials

Schedule — what is covered in each session, and when.
Lecture notes — the chapters, with runnable code.
Slides — the decks used in class, as PDF.
Self-assessment — multiple-choice questions to practise, in Italian.
Project — the template repository, the brief and the deadlines.

The official syllabus — programme, textbooks, assessment rules as filed with the university — is on the degree course pages, which are the version that counts.