Studying virtually at Alma Mater Studiorum - Università di Bologna

ATTENTION: Please note that the number of participants is limited to 10 students per module.

Please also note that the module descriptions are from the previous academic year and will be updated as soon as possible.

Big Data and Analytics, 10 ECTS credits

Students will learn the fundamentals of the most important multivariate techniques that help to make intelligent use of large data base by recognising patterns for predicting or estimating an output based on one or more inputs. At the end of the module the student is able to...

...represent and organise knowledge about big data collections

...turn data into actionable knowledge

...choose the best suited methodology for the problem at hand, and to critically interpret the results. 

Detailed module description on UNIBO homepage

Modern Statistics and Big Data Analytics, 10 ECTS credits

Students gain an understanding of theory and computing of modern statistical methods for analysing large amounts of data (big data). They will acquire knowledge about the most important methods of statistical learning and prediction, by means of cluster analysis and robust statistics. This includes: K-means, hierarchical clustering, mixture models, R-coding, influence function, application and in-depth discussion, to name a few topics. 

Detailed module description on UNIBO homepage

Machine Learning and Data Mining M, 8 ECTS credits

In this module students will get an in-depth understanding of the most relevant principles and use cases of a wide set of Machine Learning algorithms, the main steps of the Data Mining process, choosing the best suitable method for the process and evaluating the quality of results, and the main concepts related to managing big amounts of enterprise data (including Data Warehouse and Data Lake). 

Detailed module description on UNIBO homepage

Matrix Tensor Techniques for Data Science, 6 ECTS credits

Module contents include: Vector and matrix norms, mathematical foundations and algorithms for: Linear regression and least squares, Eigenvalues, SVD, pseudoinverse, principal component analysis and factor analysis, matrix completion, tensors, to name a few. In this module students will acquire this theoretical and computational knowledge on matrix and tensor techniques for analysing large amounts of data and apply it to real-world data.

Detailed module description on UNIBO homepage

Data Science for Lawyers, 6 ECTS credits

This module looks at Artificial Intelligence and Data Science from a legal and normative angle, while also teaching students about the technical instruments for data analytics, such as R, Python, and KNIME. They will acquire the competence and knowledge to read and understand a quantitative and qualitative analysis of data manipulations with NLP and AI from legal activieties, eg. judgments, justice statistics, legislation, and administrative acts. A learning goal is to also make an ethical evaluation on how AI models and Data Analytics methods can be biased, discriminatory, or affect human rights.

Detailed module description on UNIBO homepage

Important Dates - Fall Term 2025

1. Application deadline: 27 June.
2. First day of lectures: 15 September, last day of lectures 17 December.
3. Exam periods: The exact dates of midterms and final exams will be shared by lecturers in due time. If you have questions about specific dates, please consult our academic calendar or reach out to your tutor.
4. Your Transcript of Records will be issued by 15 February.

Detailed academic calendar of UNIBO

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 per 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.