Studying virtually at the University of Bologna

Modern Statistics and Big Data Analytics (10 ECTS)

Term offered: Autumn Term 2026

Teaching: in English, Synchronous – students must attend live sessions at scheduled times via Teams

This module introduces modern statistical methods for analysing large amounts of data. Students work with cluster analysis and robust statistics, combining theoretical foundations with practical data analysis and R-coding. By the end of the course, students understand key methods of statistical learning and are able to apply them to real-world datasets and interpret results.

Detailed module description on UNIBO homepage

Machine Learning and Data Mining (8 ECTS)

Term offered: Autumn Term 2026

Teaching: in English, Synchronous – students must attend live sessions at scheduled times via Teams

This module covers the full data mining process, from data warehouses and data lakes to machine learning methods. Students learn how to choose suitable algorithms, evaluate results and apply machine learning techniques to extract useful information from large datasets. By the end of the course, they are able to design and assess data-driven solutions.

Detailed module description on UNIBO homepage

Matrix Tensor Techniques for Data Science (6 ECTS)

Term offered: Autumn Term 2026

Teaching: in English, Synchronous – students must attend live sessions at scheduled times via Teams

This module focuses on mathematical and computational techniques for analysing large-scale data. Students gain knowledge of matrix and tensor methods and their applications in data science. By the end of the course, they are able to analyse complex datasets and extract interpretable information in various application domains.

Detailed module description on UNIBO homepage

Data Science for Lawyers (6 ECTS)

Term offered: Autumn Term 2026

Teaching: in English, Synchronous – students must attend live sessions at scheduled times via Teams

This module explores how data science, AI and data analytics are used in legal contexts. Students learn to analyse legal datasets, understand AI-based methods and reflect on ethical and regulatory implications. By the end of the course, they are able to critically evaluate data-driven approaches in the legal domain.

Detailed module description on UNIBO homepage

Supervised Statistical Learning (6 ECTS)

Term offered: Spring Term 2027

Teaching: in English, Synchronous – students must attend live sessions at scheduled times via Teams

This module introduces key methods for building supervised statistical models, including classification, regression and resampling techniques. By the end of the course, the student knows the fundamentals of the most important multivariate techniques to build supervised statistical models for predicting or estimating an output based on one or more inputs. The student is able to represent and organize knowledge about large-scale data collections and to turn data into actionable knowledge.

Detailed module description on UNIBO homepage

Big Data and Analytics (10 ECTS)

Term offered: Spring Term 2027

Teaching: in English, Synchronous – students must attend live sessions at scheduled times via Teams

This module introduces statistical and machine learning techniques for analysing large datasets. Students explore methods such as classification, dimension reduction and tree-based models. By the end of the course, they are able to choose appropriate analytical methods and interpret results to support decision-making.

Detailed module description on UNIBO homepage

Detailed calendar for Second cycle degree programme (LM) in Statistical Sciences (cod. 6810)

Step-by-Step Guide

1. Apply for UNIBO by registering via the following form.
2. Once you have been accepted, UNIBO will send you an email containg the necessary information for registering at UNIBO and navigating the online learning environment.
3. Register to get access to the online study plan and choose the modules you are interested in.
4. Once you have enroled, a tutor will reach out to you and provide you with support for modules, exams, deadlines, the system, and so on.

Requirements

You must be enroled in one of the eligible Master's programmes and deliver proof thereof.

Participation Limitation

In order to provide online participants with the same learning quality, as students who participate on-site, there are limited spots reserved for DSAI Module Exchange participants. A maximum of 10 students will be accepted for this module. Once the selection has been made, you will be contacted.

Please be aware that the personal information you provide in the application form will be forwarded amongst the four partnering universities for administrative purposes.