The art and science of teaching data science (Nordstat)

Modern statistics is fundamentally a computational discipline, but too often this fact is not reflected in our statistics curricula. With the rise of data science, it has become increasingly clear that students want, expect, and need explicit training in this area of the discipline. Additionally, recent curricular guidelines clearly state that working with data requires extensive computing skills and that statistics students should be fluent in accessing, manipulating, analyzing, and modeling with professional statistical analysis software. In this talk, we introduce the design philosophy behind an introductory data science course, discuss in progress and future research on student learning as well as new directions in assessment and tooling as we scale up the course.

Transcript

1. Image credit: Thomas Pedersen, data-imaginist.com/art the art and science of teaching data science mine çetinkaya-rundel bit.ly/ds-art-sci-nordstat mine-cetinkaya-rundel cetinkaya.mine@gmail.com @minebocek duke university & rstudio
2. How can we effectively and ef fi ciently teach data science to students with little to no background in computing and statistical thinking? How can we equip them with the skills and tools for reasoning with various types of data and leave them wanting to learn more?
3. demonstrate concrete course examples share a few tips provide open-source teaching resources goals
5. your fi rst data visualization + R / RStudio, R Markdown, simple Git
6. your fi rst data visualization + R / RStudio, R Markdown, simple Git fundamentals of data & data viz, confounding variables, Simpson’s paradox, tidy data, recoding & transforming, web scraping & iteration + collaboration on GitHub
7. your fi rst data visualization + R / RStudio, R Markdown, simple Git fundamentals of data & data viz, confounding variables, Simpson’s paradox, tidy data, recoding & transforming, web scraping & iteration + collaboration on GitHub ethical considerations around misrepresentation of data, relying on ML algorithms and the biases they might carry, privacy of one’s own data and reusing others’ data
8. your fi rst data visualization + R / RStudio, R Markdown, simple Git fundamentals of data & data viz, confounding variables, Simpson’s paradox, tidy data, recoding & transforming, web scraping & iteration + collaboration on GitHub ethical considerations around misrepresentation of data, relying on ML algorithms and the biases they might carry, privacy of one’s own data and reusing others’ data building & selecting models, visualising interactions, prediction & validation, inference via simulation
9. your fi rst data visualization + R / RStudio, R Markdown, simple Git fundamentals of data & data viz, confounding variables, Simpson’s paradox, tidy data, recoding & transforming, web scraping & iteration + collaboration on GitHub ethical considerations around misrepresentation of data, relying on ML algorithms and the biases they might carry, privacy of one’s own data and reusing others’ data building & selecting models, visualising interactions, prediction & validation, inference via simulation choose your own adventure: text analysis, Bayesian inference, Interactive visualization and reporting + communication & dissemination
11. ‣ Go to RStudio Cloud - bit.ly/dsbox-cloud ‣ Start the project titled UN Votes
12. ‣ Go to RStudio Cloud - bit.ly/dsbox-cloud ‣ Start the project titled UN Votes ‣ Open the R Markdown document called unvotes.Rmd
13. ‣ Go to RStudio Cloud - bit.ly/dsbox-cloud ‣ Start the project titled UN Votes ‣ Open the R Markdown document called unvotes.Rmd ‣ Knit the document and review the data visualisation you just produced
14. ‣ Go to RStudio Cloud - bit.ly/dsbox-cloud ‣ Start the project titled UN Votes ‣ Open the R Markdown document called unvotes.Rmd ‣ Knit the document and review the data visualisation you just produced ‣ Then, look for “France” in the code and replace it with another country Knit again, and review how the voting patterns of the country you picked compares to the United States and United Kingdom
15. three questions that keep me up at night… 1 what should students learn? 2 how will students learn best? 3 what tools will enhance student learning?
16. three questions that keep me up at night… 1 what should students learn? 2 how will students learn best? 3 what tools will enhance student learning? content pedagogy infrastructure
17. content
18. ex. 1 fi sheries of the world
20. ✴ data joins
21. ✴ data joins ✴ data science ethics
22. ✴ data joins ✴ data science ethics ✴ critique ✴ improving data visualisations
23. ✴ data joins ✴ data science ethics ✴ critique ✴ improving data visualisations ✴ mapping
24. Project: 2016 US Election Redux Question: Would the outcome of the 2016 US Presidential Elections been di ff erent had Bernie Sanders been the Democrat candidate? Team: 4 Squared
25. ex. 2 First Minister’s COVID brie fi ngs
27. ✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions
28. ✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions ✴ functions ✴ iteration
29. ✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions ✴ functions ✴ iteration ✴ data visualisation ✴ interpretation
30. ✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions ✴ functions ✴ iteration ✴ data visualisation ✴ interpretation ✴ text analysis
31. ✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions ✴ functions ✴ iteration ✴ data visualisation ✴ interpretation ✴ text analysis ✴ data science ethics robotstxt::paths_allowed("https://www.gov.scot") #> www.gov.scot #> [1] TRUE
32. Project: The North South Divide: University Edition Question: Does the geographical location of a UK university a ff ect its university score? Team: Fried Egg Jelly Fish
33. pedagogy
34. teams: weekly labs in teams + periodic team evaluations + term project in teams
35. teams: weekly labs in teams + periodic team evaluations + term project in teams “minute paper”: weekly online quizzes ending with a brief re fl ection of the week’s material
37. # A tibble: 19 x 2 bigram n <chr> <int> 1 question 7 19 2 question 8 16 3 questions 7 12 4 join function 9 5 question 2 9 6 choice questions 7 7 first question 7 8 multiple choice 7 9 correct answer 6 10 necessarily improve 6 11 join functions 5 12 question 1 5 13 7 8 4 14 airline names 4 15 data frames 4 16 feel like 4 17 many options 4 18 right answer 4 19 x axis 4
38. teams: weekly labs in teams + periodic team evaluations + term project in teams peer feedback: on projects “minute paper”: weekly online quizzes ending with a brief re fl ection of the week’s material
40. teams: weekly labs in teams + periodic team evaluations + term project in teams peer feedback: on projects “minute paper”: weekly online quizzes ending with a brief re fl ection of the week’s material web native (aka COVID friendly)
42. teams: weekly labs in teams + periodic team evaluations + term project in teams peer feedback: on projects “minute paper”: weekly online quizzes ending with a brief re fl ection of the week’s material web native (aka COVID friendly) creativity: assignments that make room for creativity
45. infrastructure & tooling
46. student-facing + 📦 ghclass + instructor-facing 📦 checklist + + 📦 learnr + 📦 parsermd 📦 gradethis 📦 learnrhash
47. 📦 ghclass + +
48. openness
49. datasciencebox.org
50. rstudio-education.github.io/dsbox
51. on
52. introds.org
53. rstd.io/design-ds-class
54. Image credit: Thomas Pedersen, data-imaginist.com/art the art and science of teaching data science mine çetinkaya-rundel mine-cetinkaya-rundel cetinkaya.mine@gmail.com @minebocek bit.ly/ds-art-sci-nordstat duke university & rstudio