How do you incorporate data art and data aesthetics into your data visualization portfolio?

1 What is data art?

Data art is the use of data as a medium or a source of inspiration for artistic creation. Data art can be visual, auditory, interactive, or immersive, and it can explore various themes, emotions, or narratives. Data art can also challenge the conventional ways of representing data, and invite the audience to engage with data in new and unexpected ways.

2 What is data aesthetics?

Data aesthetics is the study and practice of how data can be presented in a visually pleasing, engaging, and meaningful way. Data aesthetics considers the elements of design, such as color, shape, size, layout, and typography, as well as the principles of composition, balance, contrast, harmony, and hierarchy. Data aesthetics also takes into account the context, purpose, and audience of the data visualization, and how to convey the message effectively.

3 Why are data art and data aesthetics important?

Data art and data aesthetics are important because they can help you stand out from the crowd and showcase your unique style and vision. Data art and data aesthetics can also help you attract attention, spark curiosity, and generate interest in your data visualization portfolio. Moreover, data art and data aesthetics can help you tell a compelling story with data, and evoke emotions, insights, or actions from your audience.

5 Examples of data art and data aesthetics

Data art and data aesthetics can be used to enhance a data visualization portfolio. For example, Giorgia Lupi and Stefanie Posavec created a year-long project called Dear Data, where they exchanged postcards with hand-drawn data visualizations about their personal lives. Google News Lab and Truth & Beauty collaborated on The Rhythm of Food, which used Google Trends data to visualize seasonal and cultural patterns of food-related searches. Valentina D'Efilippo and James Ball developed The Shape of Slavery, a project that uses geometric shapes to represent historical and contemporary data on slavery. Lastly, Matt Daniels and Brian Foo created Sonification of Income Inequality, where sound was used to represent the income gap between different countries and regions.