Principles of Data Visualization from Perception to Statistical Graphics

Description: Data visualization is a powerful tool for exploring and communicating our research findings. A good plot helps us uncover and share the patterns in our data, but creating good plots takes skill and practice. This workshop introduces concepts of design principles, visual perception, and storytelling with data. We'll discuss the graphical elements of a plot, when to use different statistical plot types, and how to make your plots more understandable and accessible. We'll explore some historic and more recent data visualizations as we deconstruct the principles of plotting. Learners are encouraged to bring a draft plot that they are working on.

Learning Objectives

By the end of this workshop learners should be able to:

• Explain when, and why, to use a data visualization
• Describe common features of “good” data visualizations
• Identify principles of visual perception and aesthetics that can be used to make effective and expressive plots
• Compare the features and utility of various plot types
• Critically review a plot to meet responsible data science standards
• Know where to go for more resources on making accessible and equitable data visualizations.

Prerequisites: This workshop is designed for researchers who are actively working with data and looking to create and/or improve their data visualizations. This workshop is foundational for data visualization, and thus does not require knowledge of any specific tool or software for making plots. You can apply the tips and theory from this workshop to improve your data visualizations regardless of the software you’re using. This workshop is a prerequisite for the Intermediate R and Python workshops on "Next level data visualization" and a requirement of the GradPathways Data Visualization microcredential pathway.

Software: None

Instructors: Pamela Reynolds, Tyler Shoemaker

Instructor Biographies

Pamela Reynolds is the Associate Director of DataLab and an experimental ecologist with a background in project management and team science. Reynolds received her PhD in Biology from the University of North Carolina at Chapel Hill where she studied the biodiversity of the world’s oceans. Her research interests include applications of data science to promote interdisciplinary research, and developing pedagogy for technical skills and computational thinking. Reynolds is also co-lead of the Data Feminism Research and Learning Cluster at the UC Davis DataLab.

Tyler Shoemaker is a Postdoctoral Scholar at the DataLab, where he develops and implements methods for text analysis and natural language processing across a variety of research projects, ranging from the digital humanities to environmental and health sciences.