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storytelling with data podcast: #78 Visualize This with Nathan Yau

·51 min·3 clips

The time-use chart shows parents’ median relaxation time shrinking while child-focused and household work rise.

1. Storytelling with Data podcast episode #78 centers on Nathan Yau and the refreshed second edition of Visualize This. 2. Cole Nussbaumer Knaflic hosts, and Nathan Yau is the guest as the creator of Flowing Data, author of Data Points, and author of Visualize This. 3. The episode asks how Nathan moved from basic statistical charts to a book about choosing, refining, and questioning data visualizations. 4. Nathan says a UCLA guest lecture by Mark Hansen in the design media arts department introduced him to data art rather than charts. 5. He says that lecture led him to search for visualization examples and find Information Aesthetics by Andrew Vandamore. 6. Nathan describes that early influence as “feeling data” and experiencing data, not only optimizing it. 7. He says Flowing Data started from a $4.99 hosting plan that included flowingdata.com and a class project using Unix, Google Maps, R, and Python. 8. Nathan explains that he later used Flowing Data in Buffalo, New York, as a catalog and learning tool while he felt isolated. 9. He says the first archives were him “trying to figure things out” and sending examples to classmates. 10. Nathan describes the new edition of Visualize This as updated for new tools, a more refined process, and a stronger emphasis on analysis before chart choice. 11. He says the book’s structure follows how he takes a raw data set to a finished graphic through time, categories, relationships, and space. 12. Nathan says the book uses concrete code examples so readers can reproduce the charts and compare their output with his screenshots. 13. He compares the learning process to Lego, where the beginning and the end are shown, but the intermediate steps are built through practice. 14. Nathan says his workflow depends on asking many questions, starting broad and getting specific, like learning about a person on a first date. 15. He says that approach helps him find “the interesting bits” and avoid stopping at broad summaries. 16. Nathan warns that data collection can be distorted by bias, typos, measurement changes, and reclassified categories, using 2010 statistician income as an example. 17. He says he often looks at microdata and noisy details because they can reveal the reality better than aggregate means or medians. 18. The episode’s tone is conversational and method-focused, with Cole using the book’s examples to pull out Nathan’s process step by step. 19. People who work with data, charts, or analytics books will get the most from this episode. 20. People wanting a general tech or business interview without visualization detail may skip it.

As heard by us

A practical conversation about chart anomalies and data quality.

The episode pairs Storytelling with Data and Nathan Yau in a clear, useful conversation about charts, data quality, and the danger of reading a sudden pattern too quickly.

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Why you'd press play

If you want charts that tell the truth without losing the room, start here.

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