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
   

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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
   

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3. Communicate
   TEACHING DATA SCIENCE
   

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4. Welcome to
   
   
   the first day of class!
   

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

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

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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
   

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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
   

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

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10. Content
    Tooling
    Pedagogy
    

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11. Content
    Tooling
    Pedagogy
    

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12. Tooling
    Pedagogy
    Content
    in 3 examples
    

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13. FISHERIES OF THE WORLD
    1
    

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15. ✴ 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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16. ✴ 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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17. ✴ data joins
    
    
    ✴ data science ethics
    
    
    ✴ critique
    
    
    ✴ improving data
    visualisations
    

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

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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
    

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20. COVID BRIEFINGS
    2
    

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

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

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

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

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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
    

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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
    

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28. SPAM FILTERS
    3
    

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29. ✴ logistic regression
    
    
    ✴ prediction
    

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

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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
    

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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
    

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33. Content
    Tooling
    Pedagogy
    

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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
    

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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.
    

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36. View Slide

37. Content
    Pedagogy Tooling
    

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

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39. course
    organization
    students
    members
    assignments
    repos
    

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40. course
    organization
    teams
    teams
    projects
    repos
    

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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.
    

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44. Content
    Tooling
    Pedagogy
    

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45. Openness
    
    
    + Scalability
    

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46. datasciencebox.org
    

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47. datasciencebox.org
    

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48. github.com/tidyverse/datascience-box
    

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49. github.com/tidyverse/dsbox
    

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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
    

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51. on
    COMMUNITY
    

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52. sta199-f22-1.github.io
    EXAMPLE
    

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53. pos.it/conf
    TRAINING
    

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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.
    

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

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