Good data visualization doesn’t just mean pretty pictures, it’s an essential step in turning your research into a compelling message. Just like the text content of a poster or paper, visualizations should be tailored based on the audience and what you want to communicate. In this session we will have a crash course on the history of data visualization, the science behind it, and some practical examples of how to make your visualizations better.
This will be a hands-on session with time throughout to apply what you’re learning to an actual dataset. Please bring your computer. You’re welcome to bring your own data to use, but we’ll also have a dataset that we’re demonstrating on to share with you if you’d like to follow along.
Here are a few topics we’ll cover:
- Understanding sensory processing: what tasks are we good and bad at, and what type of visualization (position, size, shape, color hue, color intensity) pairs well with each type of data (categorical nominal, categorical ordinal, quantitative discrete, quantitative continuous)
- Choosing the right type of visualization and the right tool to build it.
- Look at some common visualization blunders and talk about why they are “wrong.”
Worshop will be led by Aaron Williams, senior data research scientist in the UVA Engineering Office of Diversity and Engagement.