Information and big data

Academic Year 2025/2026 - Teacher: GIOVANNI GIUFFRIDA

Expected Learning Outcomes

In the first part, the course will introduce the main database management systems, illustrating in simple language how data is organized and stored, and how the Relational Model helps to use information effectively, with practical examples applied to the social sciences.

The second part is dedicated to Big Data and Artificial Intelligence, with particular focus on generative models and Large Language Models (LLMs). The functioning of these technologies will be explained clearly and accessibly: students will understand how these systems “learn” from data and are able to generate content, analyze texts, and interact with people.

Additionally, practical examples of LLM usage in different social contexts will be presented: the impact of these technologies on everyday life, social dynamics, and the ethical and cultural issues that arise will be discussed. The aim is to provide both a basic understanding of their functioning and awareness of the consequences of adopting these technologies in society.

Course Structure

Classroom lecture and group discussion.

Required Prerequisites

No particular prerequisites are required. However, some basic knowledge of computer science might help.

Attendance of Lessons

Attendance is strongly recommended; discussions and material not in the lecture materials will be discussed in class.

Detailed Course Content

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· Brief historical overview of information systems and data management

· Difference between information systems and computer systems

· Concept of data and information

· How data abstraction is achieved

· Organization of data according to the Relational Model

· Introduction to data querying (how information can be searched for and retrieved)

· Simple overview of relational algebra (only basic concepts)

· What Database Management Systems (DBMS) are and what they are used for

· Introduction to data mining and text mining: their meaning and purpose

· Basic notions of data/text mining algorithms, explained with concrete examples

· Principles of Big Data, Artificial Intelligence, and Machine Learning explained in accessible language

· Examples of how Big Data are used in the social sciences

· Focus on generative models and Large Language Models (LLMs):

o Clear and straightforward explanation of how they work

o Application examples

· Reflection on social, cultural, and ethical impacts

Textbook Information

Slides provided by the teacher.

Articles suggested by the teacher.

Atzeni,Ceri,Paraboschi,Torlone, Basi di Dati,Modelli e linguaggi di interrogazione, terza edizione, McGraw-Hill 2002.

Albano-Ghelli-Orsini, Basi di Dati Relazionali e a Oggetti, Zanichelli, 1997

Ullman, Basi di Dati e Basi di Conoscenza, 1991

Machine Learning: The Art and Science of Algorithms That Make Sense of Data, Peter Flach, Cambridge University Press, 2015

Big data. Una rivoluzione che trasformerà il nostro modo di vivere e già minaccia la nostra libertà. Viktor Mayer-Schönberger, Kenneth N. Cukier, 2013

Intelligenza Artificiale e fenomeni sociali, Sergio Bedessi, Maggioli Editore, 2019

Sebastian Raschka, Sviluppare Large Language Model, Apogeo, 2025

Learning Assessment

Learning Assessment Procedures

Written exam with multiple-choice questions, designed to assess the knowledge and understanding of fundamental concepts. Inclusion of True/False questions, intended to verify the ability to apply acquired knowledge, to distinguish between correct and incorrect statements, and to foster critical self-assessment of learning.

Examples of frequently asked questions and / or exercises

SELECTION can never return an empty set. T/F?

The relational model was invented by J. Watson in 1980. T/F?

A properly terminated transaction could leave the DB in an inconsistent state. T/F?

Degree Course in

Sociology of networks, information and innovation