Data Science in a(n Ever-Evolving) Box

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mine-cetinkaya-rundel cetinkaya.mine@gmail.com @minebocek MINE ÇETINKAYA-RUNDEL DUKE UNIVERSITY + POSIT fosstodon.org/@minecr (N EVER-EVOLVING) 🔗 bit.ly/dsbox-evolving

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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

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Communicate TEACHING DATA SCIENCE

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Welcome to the first day of class!

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‣ Go to Posit Cloud ‣ Start the project titled UN Votes

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‣ Go to Posit Cloud ‣ Start the project titled UN Votes ‣ Open the Quarto document called unvotes.qmd

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‣ 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

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‣ 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

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Let’s take a look at the rest of the semester!

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Content Tooling Pedagogy

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Content Tooling Pedagogy

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Tooling Pedagogy Content in 3 examples

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FISHERIES OF THE WORLD 1

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✴ data joins fisheries |> select(country) #> # A tibble: 82 × 1 #> country #> #> 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 #> #> 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)`

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✴ data joins ✴ data science ethics fisheries |> filter(is.na(continent)) #> # A tibble: 3 × 5 #> country capture aquaculture total continent #> #> 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 ) )

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✴ data joins ✴ data science ethics ✴ critique ✴ improving data visualisations

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✴ data joins ✴ data science ethics ✴ critique ✴ improving data visualisations ✴ mapping

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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

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COVID BRIEFINGS 2

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✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions

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✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions ✴ functions ✴ iteration

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✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions ✴ functions ✴ iteration ✴ data visualisation ✴ interpretation

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✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions ✴ functions ✴ iteration ✴ data visualisation ✴ interpretation ✴ text analysis

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✴ 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

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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

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SPAM FILTERS 3

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✴ logistic regression ✴ prediction

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✴ logistic regression ✴ prediction ✴ decision errors ✴ sensitivity / specificity ✴ intuition around loss functions

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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

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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

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Content Tooling Pedagogy

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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

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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.

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Content Pedagogy Tooling

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student-facing + 📦 ghclass + instructor-facing 📦 checklist + + 📦 learnr + 📦 gradethis 📦 learnrhash or another browser/ server-based solution …

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course organization students members assignments repos

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course organization teams teams projects repos

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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.

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Content Tooling Pedagogy

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Openness + Scalability

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datasciencebox.org

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datasciencebox.org

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github.com/tidyverse/datascience-box

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github.com/tidyverse/dsbox

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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

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on COMMUNITY

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sta199-f22-1.github.io EXAMPLE

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pos.it/conf TRAINING

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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.

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(N EVER-EVOLVING) mine-cetinkaya-rundel cetinkaya.mine@gmail.com @minebocek fosstodon.org/@minecr thank you! 🔗 bit.ly/dsbox-evolving