This lesson gives a basic introduction into data visualization. The first part requires no computer. The second (supplementary) part includes a walkthrough of generating a descriptive visualization.
It is based on the lesson template used in Data Carpentry and Software Carpentry workshops.
Prerequisites
For the introductory design part, there are no pre-requisites! For the computing part, a basic familiarity with the command line and Python are recommended.
| Setup | Download files required for the lesson | |
| 00:00 | 1. Introduction | Why do we need data visualization at all? |
| 00:20 | 2. What makes a visualization effective? Co-creating best practices | What makes a data visualization effective at communicating information? |
| 01:00 | 3. Accessible Visualizations | How can we make our visualizations accessible to the widest possible range of viewers? |
| 01:10 | 4. The Cognition of Visual Information |
How does our brain process visual information?
What attributes are pre-attentive, which ones aren’t? How can this knowledge help us design more effective visualizations? |
| 01:30 | 5. Space |
How does our understanding of visual information relate to space?
How can we use (negative) space effectively to highlight important information? |
| 01:35 | 6. Working with Colour | How can we use colour effectively to convey information and highlight imporant key points? |
| 01:35 | 7. Visual Hierarchy and Gestalt Psychology | How can we ensure that a visualization as a whole is consistent and presents information clearly? |
| 01:45 | 8. Choosing a Type of Visualization | What is the difference between continuous, categorical, nominal, ordinal, and proportional data, and how do these different data types affect our choice of visualization? |
| 02:05 | 9. Interactive Visualizations |
How can we use interactive data visualizations to make complex pieces of information accessible?
What types of interactivity are there, where are they useful? |
| 02:15 | 10. Data Visualization & Society |
How does data visualization impact our lives?
How can data visualization be used to (mis-)inform the public? |
| 02:35 | 11. Wireframing a Visualization | Can we design a better representation of an existing data set and visualization? |
| 02:35 | 12. Data Visualization: A Walkthrough in Python | How can we take an existing visualization and build an improved version in Python? |
| 03:45 | 13. Data Visualization: A Walkthrough in Python with Altair |
How can we take an existing visualization and build an improved version in Python?
Can we use the library Altair to design the figures? Can we make our graphic interactive? |
| 04:55 | 14. A Gallery of Figures | |
| 04:55 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.