The visual representation of data takes full advantage of the human visual system in terms of perception and cognition. Elaborate patterns, interesting data points and outliers can easily be identified, individual data points and sets can efficiently be compared and contrasted, provided that the data is properly represented. Visualization enables users to explore their data in an interactive manner, to get overviews and drill down to detailed views, following processes that yield insights that would be difficult to obtain using fully automated data analysis techniques from the fields such as, e.g., data mining or machine learning. The two serve different purposes, but can complement one another very effectively. Visualization can for instance help formulate hypotheses, that can then be tested using statistical tests or other elaborate data analysis techniques. Beyond these exploratory aspects, data visualization can also support decision making, and plays a central role in the communication of findings to a wide range of audiences. This course first gives an overview of the field of data visualization. It then discusses fundamental principles of human visual perception, focusing on how they help inform the design of visualisations. The following sessions focus on visualization techniques for specific data structures and discuss them in depth from both design and implementation perspectives, including: multi-variate data, hierarchical structures, networks, time-series, and geographical data. All exercises are based on the D3 software library (Data-Driven Documents), that enables developers to create a wide range of interactive, Web-based visualizations that run on a wide variety of platforms, ranging from desktop workstations to mobile devices.Language: The course material is in English. Lectures can be taught either in French or in English, at the students' convenience.
Credits ECTS : 4