Program Overview

Program Overview

The Master in Data Science is designed to prepare professionals (Data Scientists) capable of creating knowledge to improve the administration of the territory and the performance of public and private companies.

Program Objectives

The program aims to form professionals who can guarantee the technical and organizational management of data, taking charge of data collection, analysis, and dissemination, through a training path suitable for market demand and confirmation of the professional figures targeted by the training offer.

Program Structure

The Master's program is conducted through introductory lessons, seminars, and exercises, guided research, and individual and group training activities aimed at verifying the ability to apply tools in real situations, conducting theoretical-disciplinary foundations on an operational plane, and directly involving participants in project activities.

Curriculum

The program includes:

• 12 courses and seminars for in-depth study
• 9 programming languages and software, including R, Python, Matlab, Iramuteq, MapReduce and Hadoop, Tensor flow, open-source GIS tools, Condor, and Tableau
• 2 didactic channels: blended and in-person

Didactic Channels

The Master offers two types of didactics:

• Blended: recommended for those already working in Public Administrations or in Public and Private Entities, with part of the content (316 hours) delivered through distance learning (F.A.D.)
• In-person: recommended for new graduates, unemployed, or inactive, with all content delivered in the classroom (406 hours)

Admission

The call for admission to the XII edition of the Master in Data Science for the academic year 2025/2026 has been published. The application for admission (pre-enrollment) must be submitted by January 15, 2026, according to the modalities indicated in the admission call. Candidates who are admitted must enroll by February 19, 2026.

Calendar

The academic calendar for the 2025/2026 academic year will be published shortly, indicating the days on which the lessons of the next edition of the Master in Data Science will take place.