Data Storytelling

Objective

Objective: 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 fields such as, e.g., data mining or machine learning. While these approaches serve different purposes, they 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. In this context, visualization does not only present results, but also helps structure, sequence, and frame insights into coherent narratives, allowing data to be interpreted as part of a meaningful story rather than as isolated facts. Such data-driven narratives can guide attention, highlight causality or uncertainty, and support sensemaking across different levels of expertise. 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 visualizations. The following sessions focus on visualization techniques for specific data structures, and discuss them in depth from both design and implementation perspectives, including: multivariate data, hierarchical structures, networks, time series, statistical data, and geographical data. Throughout the course, particular attention is given to how these techniques can be combined and orchestrated to support exploration, explanation, and storytelling with data.

Planning

The evaluation will be conducted through a group project, in which students, working in teams of two or three, will be asked to design and present a data-driven story.

Plan: By the end of the course, students will be able to:

- situate data within political, social, or scientific contexts, and leverage this context to craft more precise and meaningful data stories

- identify and avoid common pitfalls in data representation, both in terms of visual encoding and schematic design

- design and implement simple data visualizations based on publicly available datasets

- use programming to develop interactive data visualizations

- select appropriate visual representations and animations that best support the intended message or narrative

- critically navigate and assess contemporary international work and practices in data visualization and data storytelling