course of study

The course is recognized by the Ministry of University and Research as a 2nd level Academic Master and allows for the acquisition of 64 ECTS (CFU).

The qualification will be issued upon verification of attendance and after a final exam, which will consist in the presentation of a project on the application of one of the methodologies introduced during the master to real case studies, hopefully resulting from the internship experience.

There will be exams at the end of each module.


The master MD2SL is articulated in three distinct teaching blocks and a dedicated data science lab.

  1. The first block, "Data Science Bootcamp", provides students with a solid knowledge of the disciplines at the base of data science (mathematics, statistics, computer programming), to ensure a homogeneous preparation of students from potentially very different backgrounds.

  2. The second block consists of "Core Courses" that introduce students to the main concepts and tools of data science and data analytics.

  3. The third teaching block consists of the "Elective Courses" and is designed for the acquisition of specific skills in two distinct application areas in which the Data Scientist can play a role of central importance: economics and business, or health and medical science.

The specific objectives of each block are achieved through a well-balanced mix of theoretical lectures, case study analysis, workshops, and individual and group practical activities. The latter encourages exchanges and interactions among students for a greater and deeper understanding of the topics covered.


At the end of the program, students will have the opportunity to apply the knowledge acquired to a 225-hour internship at one of the public or private companies, research centers and units, or local authorities that are partners of the master course. The placement in a professional setting will allow students to personally follow the phases of software design and development and the implementation of complex data analysis.

The internship activity aims to equip students with specific skills, such as:

  • the ability to apply technical skills to real-world cases;

  • an orientation to problem solving in the design, implementation and monitoring of specific projects;

  • the ability to communicate the results of projects developed in corporate or institutional contexts;

  • managerial skills useful in all phases of the development of data science and big-data analytics projects.


Scientific Director: Fabrizia Mealli, UniversitĂ  degli Studi di Firenze

Organizing Committee:


The master course will also involve lecturers and speakers from partner organizations.