Subject of the course

Nowadays we deal with an increasing amounts of data that constantly challenge our cognitive capabilities. Indeed, data becomes useful only when we are able to derive insight and take decisions from it. Luckily, humans are extremely good at processing visual data: few people could detect patterns among rows of numbers, but even kids can understand bar charts. Accordingly, visualization is a vital tool to understand and share insights around data. An effective visualization can help present a core idea or start an open discussion.

Mission and Goals

Data visualization is an extensive topic at the intersection of several areas, such as statistics, data mining, cognitive science, and communication  design. Our mission is to provide a compact introduction to the field to anyone who needs to communicate something to someone using data. The goal of the course is to provide the students with the knowledge, skills and resources required to make sense of data, design effective visualizations, and tell stories from data. 

At the end of this course, students should be able to:

• understand basic principles of data visualization and evaluate existing visualizations;
• select the visualization methods and design styles that best apply to different kinds of data;
• create effective visualizations from data.

Detailed program

• Fundamentals: definitions and introduction to different types of visualizations (exploratory vs explanatory, static vs interactive); basics about human perception; evaluation and design principles of visualization.
• Visualization of numerical data: basic charts and glyphs; dimensionality of visualization; standard approaches to the visualization of time series, multi-variate and high-dimensional data. 
• Visualization of non-numerical data: standard approaches to the visualization of graphs, networks, hierarchies, and text.
• Advanced topics: interactive visualization; dimensionality reduction.

Teaching organization

The course will involve frontal lectures as well as hands-on sessions on practical examples will be presented; students will be encouraged to bring their own laptops.

The evaluation will consist of a project where students will be asked to design an effective visualization of a dataset of their own choice (students will be encouraged to use data or results from their own research work). 

Do consider the following as a tentative schedule. Changes and variations might be announced in the weeks before the beginning of the course.

19 MAY 2020, TUE, 14:30 – 18:30
21 MAY 2020, THU, 09:30 – 13:30
26 MAY 2020, TUE, 14:30 – 18:30
28 MAY 2020, THU, 09:30 – 13:30
03 JUN 2020, WED, 14:30 – 18:30
04 JUN 2020, THU, 09:30 – 13:30