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Activity 1

Please complete this short survey through Slido. The purpose of the survey is to understand how participants' college majors relate to their current work. Additionally, results will be used to create our own data story as a group during the in-person workshop. Let’s make this relevant and fun together! 

Pre-Course Survey

Activity 2

Learning Objective: Distinguish “data visualization” from “information design” in the context of a visual communication of data such as infographics, charts, maps. 

  • Watch me talk about the essentials of Information Design and Data Visualization. (16:13). If you’d rather not watch this video, feel free to review the PDF that includes the text-based script and/or read the About Visual Communication document. I am all about giving people choices!
  • Read the Piktochart article titled A Comprehensive Guide to Information Design (With Examples). It covers the fundamentals of “good information design” as you are guiding others in discovering differences between information, data, and how to communicate both. This is also a good source for locating examples. (11:00).

Activity 3

Learning Objective: Identify common sources of bias in the data visualization process.

  • Read Evaluating Data Visualizations to get an idea of some of the ways that data visualizations can misinterpret, hide, or distort information. (2:00).

Activity 4

Learning Objective: Review common visualization techniques, pre-attentive attributes, types of data, and visual encoding for each type.

  • Read the article titled Preattentive Visual Properties and How to Use Them in Information VisualizationThis article examines preattentive processing that takes place in sensory memory. Knowing the basics of these attributes can be useful in information design because it allows us the designer to grab the attention of the user with minimal effort on the part of the user. The information in this article can help you understand how you could decrease the complexity of data in a way that can be processed in short-term memory. (8:00).

Activity 5

Learning Objective: Explore useful online resources for assessing data visualizations to help identify misinformation in data visualizations.

Element of Engagement (in class)

Please think about the following questions for a brief group discussion in class on July 29: 

  1. What are some common sources of bias in data visualization, and how can they be mitigated to ensure accurate and fair representation of data?
  2. How do pre-attentive attributes and visual encoding techniques enhance the effectiveness of data visualizations? Can you provide examples where these principles can be applied in your own work?

Optional Additional Resources

*You don’t have to read the following; just sharing for those who want to learn more about visualizing data.

  • Data visualizations that are built using erroneous data and questionable, even biased, methodologies may lead to a) misinterpretation that can lead to flawed conclusions, and b) perpetuation of biased methods and information if not scrutinized publicly. For instance, assuming a correlation between two variables based solely on a scatter plot without considering confounding factors can be misleading. Critical analysis helps us avoid such pitfalls. Have you asked yourself why you might hesitate to question data, whether in tabular format or a visualization? How might we stop hesitating to judge data? What questions should we ask ourselves or others? Perhaps this video by @EricSiegel may help you in answering these questions or at least make you laugh a little. (12:46).
  • In their Hands-On Data Visualization online book, Jack Dougherty and Ilya Ilyankou warn "to watch out for people who lie with visualizations." Even though data visualizations are created to share information, inspire exploration, and invoke insights, there are several examples that intentionally or unintentionally skew the results or illustrate the data questionably. Two questions that we will address in this unit are: