LABORATORY MOD. A: CASE STUDIES AND GOOD PRACTICES

This course is part of the minor in Data, Information and Society. As such, it allows students to continue their education in the area of Data Science and its applications in social domains, building on notions and skills learned in the previous modules of the minor.
The overall objectives of the course are:
- present case studies that enable students to manage and analyze data leading to first-hand learning of socially relevant phenomena:
- to present practical case studies that enable students to reflect on the implications of using algorithms and automated decision-making systems in public life;

Students will have achieved an understanding of the application of statistical and computer science concepts to issues related to public life and society. They will know how to critically evaluate the application of machine learning methods to areas of public and social life.

Previous modules from the Data Information and Society minor

Reading and implementation of case studies selected ad-hoc and presented by the lecturer in the classroom. Case studies will cover topics such as: systematic bias; algorithmic fairness; cognitive paradoxes.

Allen Downey, Probably Overthinking it, The University of Chicago Press
Cathy O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Penguin Books
Various reading material and notebooks shared trough the Moodle platform

Active participation in class (20% of grade)
Presentation of a project illustrating a case study of interest (80%).
Maximum points are achieved for particularly clear and well-structured presentations of the case study.

oral

Lezioni frontali teoriche convenzionali integrate da esercitazioni, discussione di casi studio e laboratori informatici. Il materiale didattico preparato dal docente sarà distribuito durante il corso attraverso la piattaforma Moodle. Il software statistico utilizzato nel corso è R (www.r-project.org)