Data Science in a(n Ever-Evolving) Box

What should a first course in data science for students who have limited to no experience with statistics and programming look like? How do we teach it in a way that lends itself to iteration as the landscape of data science evolves and that scales to more students and more instructors? In this talk I will aim to accomplish two goals to answer these questions: (1) Introduce a semester-long, modern introductory data science curriculum, along with its design philosophy, implementation details (particularly as class sizes increase), technical infrastructure, and real examples from course content as well as from student projects. (2) Discuss how I've open-sourced this curriculum at datasciencebox.org for sharing with and re-use / adaptation by other instructors and what it takes to maintain this open-source project as the landscape of data science, data science education curriculum guidelines, and data science tooling evolves.

Transcript

1. mine-cetinkaya-rundel cetinkaya.mine@gmail.com @minebocek MINE ÇETINKAYA-RUNDEL DUKE UNIVERSITY + POSIT fosstodon.org/@minecr (N EVER-EVOLVING) 🔗 bit.ly/dsbox-evolving
2. Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science, 2nd Edition. Program Import Tidy Transform Visualize Model Communicate Understand Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science, 2nd Edition. DOING DATA SCIENCE
3. Communicate TEACHING DATA SCIENCE
4. Welcome to the first day of class!
5. ‣ Go to Posit Cloud ‣ Start the project titled UN Votes
6. ‣ Go to Posit Cloud ‣ Start the project titled UN Votes ‣ Open the Quarto document called unvotes.qmd
7. ‣ Go to Posit Cloud ‣ Start the project titled UN Votes ‣ Open the Quarto document called unvotes.qmd ‣ Render the document and review the data visualization you just produced
8. ‣ Go to Posit Cloud ‣ Start the project titled UN Votes ‣ Open the Quarto document called unvotes.qmd ‣ Render the document and review the data visualization you just produced ‣ Then, look for the character string “Turkey” in the code and replace it with another country of your choice ‣ Render again, and review how the voting patterns of the country you picked compare to the United States and the United Kingdom
9. Let’s take a look at the rest of the semester!
10. Content Tooling Pedagogy
11. Content Tooling Pedagogy
12. Tooling Pedagogy Content in 3 examples
13. FISHERIES OF THE WORLD 1
15. ✴ data joins fisheries |> select(country) #> # A tibble: 82 × 1 #> country #> <chr> #> 1 Angola #> 2 Argentina #> 3 Australia #> 4 Bangladesh #> 5 Brazil #> 6 Cambodia #> 7 Cameroon #> 8 Canada #> 9 Chad #> 10 Chile # ℹ 72 more rows continents #> # A tibble: 245 × 2 #> country continent #> <chr> <chr> #> 1 Afghanistan Asia #> 2 Åland Islands Europe #> 3 Albania Europe #> 4 Algeria Africa #> 5 American Samoa Oceania #> 6 Andorra Europe #> 7 Angola Africa #> 8 Anguilla Americas #> 9 Antigua & Barbuda Americas #> 10 Argentina Americas #> # ℹ 235 more rows fisheries <- left_join(fisheries, continents) Joining with `by = join_by(country)`
16. ✴ data joins ✴ data science ethics fisheries |> filter(is.na(continent)) #> # A tibble: 3 × 5 #> country capture aquaculture total continent #> <chr> <dbl> <dbl> <dbl> <chr> #> 1 Democratic Republic of the Congo 237372 3161 240533 NA #> 2 Hong Kong 142775 4258 147033 NA #> 3 Myanmar 2072390 1017644 3090034 NA fisheries <- fisheries |> mutate( continent = case_when( country == "Democratic Republic of the Congo" ~ "Africa", country == "Hong Kong" ~ "Asia", country == "Myanmar" ~ "Asia", .default = continent ) )
17. ✴ data joins ✴ data science ethics ✴ critique ✴ improving data visualisations
18. ✴ data joins ✴ data science ethics ✴ critique ✴ improving data visualisations ✴ mapping
19. Project: Regional differences in average GPA and SAT Question: Exploring the regional differences in average GPA and SAT score across the US and the factors that could potentially explain them. Team: Mine’s Minions
20. COVID BRIEFINGS 2
22. ✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions
23. ✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions ✴ functions ✴ iteration
24. ✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions ✴ functions ✴ iteration ✴ data visualisation ✴ interpretation
25. ✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions ✴ functions ✴ iteration ✴ data visualisation ✴ interpretation ✴ text analysis
26. ✴ 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
27. Project: Factors Most Important to University Ranking Question: Explore how various metrics (e.g., SAT/ACT scores, admission rate, region, Carnegie classification) predict rankings on the Niche College Ranking List. Team: 2cool4school
28. SPAM FILTERS 3
29. ✴ logistic regression ✴ prediction
30. ✴ logistic regression ✴ prediction ✴ decision errors ✴ sensitivity / specificity ✴ intuition around loss functions
31. Project: Predicting League of Legends success Question: After 10 minutes into the game, whether a gold lead or an experienced lead was a better predictor of which team wins? Team: Blue Squirrels
32. Project: A Critique of Hollywood Relationship Stereotypes Question: How has the average age difference between two actors in an on- screen relationship changed over the years? Furthermore, do on-screen same-sex relationships have a different average age gap than on-screen heterosexual relationships? Team: team300
33. Content Tooling Pedagogy
34. live coding: in every “lecture”, along with time for students to attempt exercises on their own “minute paper”: weekly online quizzes ending with a brief reflection of the week’s material creativity: assignments that make room for creativity peer feedback: at various stages of the project teams: weekly labs in teams + periodic team evaluations + term project in teams
35. Çetinkaya-Rundel, Mine, Mine Dogucu, and Wendy Rummerfield. "The 5Ws and 1H of term projects in the introductory data science classroom." Statistics Education Research Journal 21.2 (2022): 4-4.
37. Content Pedagogy Tooling
38. student-facing + 📦 ghclass + instructor-facing 📦 checklist + + 📦 learnr + 📦 gradethis 📦 learnrhash or another browser/ server-based solution …
39. course organization students members assignments repos
40. course organization teams teams projects repos
43. Beckman, M. D., Çetinkaya-Rundel, M., Horton, N. J., Rundel, C. W., Sullivan, A. J., & Tackett, M. "Implementing version control with Git and GitHub as a learning objective in statistics and data science courses." Journal of Statistics and Data Science Education 29. (2021): S132-S144.
44. Content Tooling Pedagogy
45. Openness + Scalability
46. datasciencebox.org
47. datasciencebox.org
48. github.com/tidyverse/datascience-box
49. github.com/tidyverse/dsbox
50. AUDIENCE I have been teaching with R for a while, but I want to update my teaching materials I’m new to teaching with R and need to build up my course materials This teaching slide deck I came across on Twitter is pretty cool, but I have no idea what type of course it belongs in
51. on COMMUNITY
52. sta199-f22-1.github.io EXAMPLE
53. pos.it/conf TRAINING
54. SCHOLARSHIP Çetinkaya-Rundel, Mine, and Victoria Ellison. "A fresh look at introductory data science." Journal of Statistics and Data Science Education 29.sup1 (2021): S16-S26.
55. (N EVER-EVOLVING) mine-cetinkaya-rundel cetinkaya.mine@gmail.com @minebocek fosstodon.org/@minecr thank you! 🔗 bit.ly/dsbox-evolving