Raising Heretics
summary
Argues that data science education must teach survivorship bias and deliberate skepticism by using real datasets and measurable outcomes not toy exercises. Describes building ADSEI project lessons, spending hours reverse‑engineering an AEC senate ballot export, running year‑10 projects on climate, elections and microbats and year‑11 collaborations with biologists, using spreadsheets not Python for most classes, and reporting that one class doubled girls in class when switched to authentic data projects.
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chapters (24)
- 0:00Introduction
- 3:10Heresies in history
- 3:55Galilei
- 4:10Alice Evans
- 4:45Barbara McClellan
- 5:06Helicobacter pylori
- 5:35World War II planes
- 6:11Survivor bias
- 6:52AI
- 7:26Higher View
- 8:27The Charisma of Numbers
- 8:48Models vs Algorithms
- 9:30Automating Inequality
- 9:48Biometric Mirror
- 11:20Data Science Education
- 14:00Tech Education
- 15:03Teaching
- 16:05My own time
- 16:43ADSES mission
- 17:28ADSES projects
- 17:54Feedback
- 18:54Diversity
- 19:28Questions
- 23:37Conclusion
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