INFORMATION VISUALIZATION, DATA SCIENCE AND SOCIAL MEDIA ANALYTICS MOD.2

This course provides an introduction to the principles and practices of information visualization, with a focus on understanding how data can be visually represented to support exploration, interpretation, and communication. Through theoretical foundations, design guidelines, and practical exercises, students will learn how to critically analyze, design, and implement effective visualizations for digital humanities and public engagement contexts.

- Understanding what information visualization is and why use it
- How visualization works
- Knowledge of fundamental graphs and data transformation
- Mapping data into a visualisation
- Learn to make trustworthy and accessible visualizations
- Acquire knowledge beyond the usage of some tools for creating interactive visualisations

Basic computing knowledge
Basic knowledge of Python

Foundations of data visualization
Data abstraction
Task abstraction
Fundamental charts and data transformation
Designing a visualization
Evaluating a visualization
Tools for Interactive Visualization (e.g. Tableau, Flourish, Panda)
Hands-on labs and guided tutorials

- Slides and additional material given during the classes
- Wilke, Claus O. Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media, 2019. Accessible online at https://clauswilke.com/dataviz/
- Andy Kirk. Data Visualization: A Handbook for Data Driven Design. SAGE Publications, London, 2016.

The course includes regular assignments to support the learning process. The final exam will consist of an oral examination, during which the student will present and discuss a project developed on a topic previously agreed upon with the instructor.
The oral exam will focus both on the course content and on the presentation and critical discussion of the project.

The final grade will be based on the following criteria:
- the quality of the project (alignment with the topic, technical/methodological soundness, creativity, and completeness);
- the clarity and awareness demonstrated during the project presentation and oral discussion;
- the knowledge and understanding of the course content;
- the level of participation in class and home activities.

oral

Grading criteria:
- Scores Below 18/30: Exam not passed. Insufficient knowledge and understanding of the course topics; inadequate or missing project; weak or incoherent oral discussion.
- Scores between 18–22/30: Sufficient knowledge and understanding of the course topics, as outlined in the syllabus; essential but coherent project; simple and not in-depth discussion; basic level of participation in activities.
- Scores between 23–25/30: Satisfactory knowledge and understanding; well-structured project with room for improvement; clear discussion; good participation in activities.
- Scores between 26–28/30: Good knowledge and understanding of the topics; complete and relevant project; well-structured and thoughtful discussion; consistent and active participation.
- Scores between 29–30/30: In-depth and autonomous knowledge; original, well-developed, and complete project; critical and well-argued discussion; excellent participation in activities.

Il corso combina lezioni frontali interattive e lavoro di gruppo. Gli studenti utilizzeranno strumenti collaborativi per la condivisione di dati e frammenti di codice, come Google Colaboratory, oltre a software per la visualizzazione dei dati, come Tableau e altre piattaforme per la creazione di visualizzazioni statiche e interattive.

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development