chris wiggins edited this page May 7, 2018 · 76 revisions

Resources

Lecture Notes

(see Syllabus for overview of course)

  • Lecture 1, Jan 17: Course introduction Slides
  • Lecture 2, Jan 22: boyd, Wallach (setting the stakes)
  • Lecture 3, Jan 29: data: it's made of people (Tufecki "Engineering" (2014), Desrosières Ch 1, Igo (19th c.))
  • Lecture 4, Feb 5: societal risk, social physics (Desrosières Ch 3, Quetelet (19th c), Porter Ch 2, Gigerenzer 1.6)
  • Lecture 5, Feb 12: quantitative racism (Desrosières Ch 4, Galton, Gould)
  • Lecture 6, Feb 19: IQ, regression-v-causation (Gould on Spearman; Spearman; Freedman on Yule (19th /20th c))
  • Lecture 7, Feb 26: high church mathematical statistics/hypothesis testing (Fisher, Neyman, Pearson; Gerd (20th c))
  • Lecture 8, Mar 5: WWII as birth of computing w/data: Bayes, Bletchley, "Women at the Dawn"... (WWII)
  • Lecture 9, Mar 19: birth+death of AI: Turing '50, Dartmouth '55, Lighthill '73 (post-war)
  • Lecture 10, Mar 26: Data at "The Labs": Tukey and EDA; Chambers
  • Lecture 11, April 2: Machine Learning as a Trading Zone, Breiman's "two cultures", AI2.0
  • Lecture 12, April 9: 50 years of Data Science (and data engineering)
  • Lecture 13, April 16: Ethics: defining & enforcing; research & industry
  • Lecture 14, April 23: Persuasion architectures; surveillance capitalism; you are the product
  • Lecture 15, April 30: what did we learn? (Last day of class)

Lab Notebooks (Jupyter)

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