Statistics & Big Data

Business Statistics and Artificial Intelligence

HEMA specialistic degree · Università Cattolica del Sacro Cuore, Rome · A.Y. 2026–2027
Prof. Giuseppe Arbia · Prof. Niccolò Salvini

Module 1 · Arbia14 Sep – 23 Oct · room 204

Module 2 · Salvini2 Nov – 11 Dec · rooms 202 / 204

Office hoursBook a slot

SoftwareSetup guide

Everything you need for this course is on this page. Each lecture gets a row: slides, code and readings appear there as we go.

Module 1 — Statistical foundations, advanced regression, forecasting Prof. Giuseppe Arbia · 14 September – 23 October · room 204

#	Date	Time · Room	Topic	Materials
1	Mon 14 Sep	17:00–19:00 · 204	Course introduction · statistical foundations: hypothesis testing, confidence intervals	—
2	Fri 18 Sep	09:00–13:00 · 204	OLS and notions of prediction · machine learning principles: bias–variance tradeoff, cross-validation	—
3	Fri 25 Sep	09:00–13:00 · 204	Multiple regression	—
4	Tue 29 Sep	10:00–12:00 · 204	Nonlinear regression	—
5	Fri 2 Oct	09:00–13:00 · 204	GLM extensions: logit and Poisson models	—
6	Mon 5 Oct	17:00–19:00 · 204	Survival models · lecture by Prof. Salvini — schedule change	—
7	Fri 9 Oct	09:00–13:00 · 204	Regularisation and high-dimensional methods: Ridge, LASSO, Elastic Net	—
8	Fri 16 Oct	09:00–13:00 · 204	Time series: components, visualization, smoothing, trend and seasonality	—
9	Fri 23 Oct	09:00–13:00 · 204	AR and ARIMA models · space–time modelling · business applications	—

Change to the published schedule — lecture 6, Monday 5 October. Prof. Salvini lectures in place of Prof. Arbia, who has a supervening commitment. Date, time, room and topic are unchanged: 17:00–19:00, room 204, survival models.

First intermediate exam — Module 1 content, expected at the end of October, once Module 1 closes on 23 October. Last year it fell on 27 October. The date is not set yet. It will be announced on Blackboard and added here as soon as it is.

Module 2 — Unsupervised learning, machine learning, communication Prof. Niccolò Salvini · 2 November – 11 December · rooms 202 / 204

#	Date	Time · Room	Topic	Materials
10	Mon 2 Nov	17:00–19:00 · 202	Module 2 opening · software setup check · the unsupervised learning problem	slides · setup
11	Tue 3 Nov	09:00–12:00 · 204	k-means clustering · customer segmentation	slides · practice
12	Mon 9 Nov	17:00–19:00 · 202	Hierarchical clustering · hospital and patient grouping	slides · practice
13	Fri 13 Nov	09:00–12:00 · 204	Lab — clustering end to end in R	slides · data
14	Mon 16 Nov	17:00–19:00 · 202	Principal components · latent drivers of customer behaviour	slides · practice
15	Tue 17 Nov	10:00–13:00 · 204	Correspondence analysis · dimensions of brand perception · lab	slides · data · practice
16	Mon 23 Nov	17:00–19:00 · 202	Decision trees	slides · practice
17	Fri 27 Nov	10:00–13:00 · 204	Random forests and gradient boosting · lab	slides · data · practice
18	Mon 30 Nov	17:00–19:00 · 202	Neural networks	slides · practice
19	Fri 4 Dec	10:00–13:00 · 204	Lab — neural networks, and when not to reach for them	slides
20	Wed 9 Dec	17:00–19:00 · 202	Model interpretability · predictive accuracy vs interpretability in managerial decision-making	slides · practice
21	Fri 11 Dec	10:00–13:00 · 108	Executive dashboards · communicating uncertainty · storytelling with data · human–AI collaboration	slides · practice

Second intermediate exam — Module 2 content, in the winter session. The date is not set yet. It will be announced on Blackboard and added here as soon as it is.

Instructors

Giuseppe Arbia
Module 1

Niccolò Salvini
Module 2

Both lecturers receive students in person after each lecture. Online appointments are with Prof. Salvini — see Office hours. Working students are covered by Prof. Salvini for both modules; write to arrange a slot.