Data Science in a Box

Data Science in a Box (datasciencebox.org) is an open-source project that aims to equip educators with concrete information on content and infrastructure for designing and painlessly running a semester-long modern introductory data science course with R. In this talk we outline five guiding pedagogical principles that underlie the choice of topics and concepts introduced in the course as well as their ordering, highlight a sample of examples and assignments that demonstrate how the pedagogy is put into action, introduce `dsbox` -- the companion R package for datasets used in the course as well as interactive tutorials, and share sample student work and feedback. We will also walk through a quick start guide for faculty interested in using all or some of these resources in their teaching.

Transcript

1. bit.ly/dsbox-adsa
   mine-cetinkaya-rundel
   cetinkaya.mine@gmail.com
   @minebocek
   MINE ÇETINKAYA-RUNDEL
   UNIVERSITY OF EDINBURGH +
   DUKE UNIVERSITY +
   RSTUDIO
   

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2. bit.ly/dsbox-adsa
   Three questions that keep me up at night…
   1 What should my students learn?
   2 How will my students learn best?
   3 What tools will enhance my students’ learning?
   

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3. bit.ly/dsbox-adsa
   1 What should my students learn?
   2 How will my students learn best?
   3 What tools will enhance my students’ learning?
   Three questions that keep me up at night…
   Content
   Pedagogy
   Infrastructure
   

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4. bit.ly/dsbox-adsa
   Infrastructure
   Pedagogy
   Content
   

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5. bit.ly/dsbox-adsa
   Infrastructure
   Pedagogy
   Content
   

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6. bit.ly/dsbox-adsa
   

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7. bit.ly/dsbox-adsa
   datasciencebox.org
   rstudio-education/datascience-box
   

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8. bit.ly/dsbox-adsa
   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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9. bit.ly/dsbox-adsa
   TOPICS
   

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10. bit.ly/dsbox-adsa
    CONTENTS
    
    30
    slide
    decks
    
    10
    application
    exercises
    
    13
    computing
    labs
    ✍
    8
    homework
    assignments
    ✔
    2
    take-home
    exams
    
    1
    open-ended
    project
    website
    datasciencebox.org
    repository
    
    8
    interactive
    tutorials
    package
    dsbox
    
    …
    videos
    

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11. bit.ly/dsbox-adsa
    DESIGN PRINCIPLES
    
    cherish
    day one
    
    skip baby
    steps
    
    start
    with cake
    
    leverage the
    ecosystem
    
    hide the
    veggies
    

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12. DESIGN PRINCIPLES
    Which kitchen would you
    rather bake a cake?
    

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13. DESIGN PRINCIPLES
    Which kitchen would you
    rather bake a cake?
    

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14. DESIGN PRINCIPLES
    Cherish day one
    rstd.io/dsbox-cloud
    

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15. DESIGN PRINCIPLES
    How do you prefer your
    cake recipes? Words only,
    or words & pictures?
    

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16. DESIGN PRINCIPLES
    How do you prefer your
    cake recipes? Words only,
    or words & pictures?
    

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17. DESIGN PRINCIPLES
    Start with cake
    ‣ Open today’s demo project
    ‣ Knit the document and discuss the results with your neighbor
    ‣ Then, change Turkey to a different country, and plot again
    

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18. DESIGN PRINCIPLES
    Start with cake
    With great examples, comes a great amount of code…
    but let’s focus on the task at hand…
    ‣ Open today’s demo project
    ‣ Knit the document and discuss the results with your neighbor
    ‣ Then, change Turkey to a different country, and plot again
    

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19. DESIGN PRINCIPLES
    Start with cake
    un_votes %>%
    filter(country %in% c("UK & NI", “US”, "Turkey")) %>%
    inner_join(un_roll_calls, by = "rcid") %>%
    inner_join(un_roll_call_issues, by = "rcid") %>%
    group_by(country, year = year(date), issue) %>%
    summarize(
    votes = n(),
    percent_yes = mean(vote == "yes")
    ) %>%
    filter(votes > 5) %>% # only use records where there are more than 5 votes
    ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +
    geom_smooth(method = "loess", se = FALSE) +
    facet_wrap(~ issue) +
    labs(
    title = "Percentage of Yes votes in the UN General Assembly",
    subtitle = "1946 to 2015",
    y = "% Yes",
    x = "Year",
    color = "Country"
    )
    

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20. DESIGN PRINCIPLES
    Start with cake
    un_votes %>%
    filter(country %in% c("UK & NI", “US”, "Turkey")) %>%
    inner_join(un_roll_calls, by = "rcid") %>%
    inner_join(un_roll_call_issues, by = "rcid") %>%
    group_by(country, year = year(date), issue) %>%
    summarize(
    votes = n(),
    percent_yes = mean(vote == "yes")
    ) %>%
    filter(votes > 5) %>% # only use records where there are more than 5 votes
    ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +
    geom_smooth(method = "loess", se = FALSE) +
    facet_wrap(~ issue) +
    labs(
    title = "Percentage of Yes votes in the UN General Assembly",
    subtitle = "1946 to 2015",
    y = "% Yes",
    x = "Year",
    color = "Country"
    )
    

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21. DESIGN PRINCIPLES
    Start with cake
    un_votes %>%
    filter(country %in% c("UK & NI", “US”, "Turkey")) %>%
    inner_join(un_roll_calls, by = "rcid") %>%
    inner_join(un_roll_call_issues, by = "rcid") %>%
    group_by(country, year = year(date), issue) %>%
    summarize(
    votes = n(),
    percent_yes = mean(vote == "yes")
    ) %>%
    filter(votes > 5) %>% # only use records where there are more than 5 votes
    ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +
    geom_smooth(method = "loess", se = FALSE) +
    facet_wrap(~ issue) +
    labs(
    title = "Percentage of Yes votes in the UN General Assembly",
    subtitle = "1946 to 2015",
    y = "% Yes",
    x = "Year",
    color = "Country"
    )
    

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22. DESIGN PRINCIPLES
    Start with cake
    un_votes %>%
    filter(country %in% c("UK & NI", “US”, “France")) %>%
    inner_join(un_roll_calls, by = "rcid") %>%
    inner_join(un_roll_call_issues, by = "rcid") %>%
    group_by(country, year = year(date), issue) %>%
    summarize(
    votes = n(),
    percent_yes = mean(vote == "yes")
    ) %>%
    filter(votes > 5) %>% # only use records where there are more than 5 votes
    ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +
    geom_smooth(method = "loess", se = FALSE) +
    facet_wrap(~ issue) +
    labs(
    title = "Percentage of Yes votes in the UN General Assembly",
    subtitle = "1946 to 2015",
    y = "% Yes",
    x = "Year",
    color = "Country"
    )
    

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23. DESIGN PRINCIPLES
    Start with cake
    

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24. DESIGN PRINCIPLES
    Start with cake
    

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25. DESIGN PRINCIPLES
    Which motivates you
    more to learn how to
    cook: perfectly chopped
    onions or ratatouille?
    

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26. DESIGN PRINCIPLES
    Which motivates you
    more to learn how to
    cook: perfectly chopped
    onions or ratatouille?
    

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27. DESIGN PRINCIPLES
    Skip baby steps
    Re-insert
    

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28. DESIGN PRINCIPLES
    Which is more likely to
    appeal to someone who
    has never tried broccoli?
    

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29. DESIGN PRINCIPLES
    Which is more likely to
    appeal to someone who
    has never tried broccoli?
    

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30. DESIGN PRINCIPLES
    Hide the veggies
    ‣ Today we go from this to that
    ‣ And do so in a way that is easy to replicate for another state
    →
    

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31. DESIGN PRINCIPLES
    Lesson: Web scraping essentials for
    turning a structured table into a data
    frame in R.
    Hide the veggies
    

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32. DESIGN PRINCIPLES
    Lesson: Web scraping essentials for
    turning a structured table into a data
    frame in R.
    Ex 1: Scrape the table off the web and
    save as a data frame.
    Hide the veggies
    

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33. DESIGN PRINCIPLES
    Lesson: Web scraping essentials for
    turning a structured table into a data
    frame in R.
    Ex 1: Scrape the table off the web and
    save as a data frame.
    Ex 2: What other information do we need
    represented as variables to make this figure?
    Hide the veggies
    

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34. DESIGN PRINCIPLES
    Lesson: Web scraping essentials for
    turning a structured table into a data
    frame in R.
    Ex 1: Scrape the table off the web and
    save as a data frame.
    Ex 2: What other information do we need
    represented as variables to make this figure?
    Lesson: “Just enough” regex
    Hide the veggies
    

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35. DESIGN PRINCIPLES
    If you are already taking a
    baking class, which will be
    easier to venture on to?
    

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36. DESIGN PRINCIPLES
    If you are already taking a
    baking class, which will be
    easier to venture on to?
    

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37. DESIGN PRINCIPLES
    Leverage the ecosystem
    student + instructor instructor
    

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38. bit.ly/dsbox-adsa
    USAGE
    in full
    to jumpstart /
    overhaul your
    teaching
    in bits & pieces
    to supplement
    your teaching
    

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39. bit.ly/dsbox-adsa
    LICENSE
    

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40. bit.ly/dsbox-adsa
    COMMUNITY
    on
    

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41. bit.ly/dsbox-adsa
    FUTURE
    scalability
    tooling
    peer review
    assessment
    curriculum
    reach & impact
    engagement
    community
    collaboration
    growth
    

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42. bit.ly/dsbox-adsa
    FUTURE
    bit.ly/fresh-ds
    

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43. mine-cetinkaya-rundel
    cetinkaya.mine@gmail.com
    @minebocek
    MINE ÇETINKAYA-RUNDEL
    UNIVERSITY OF EDINBURGH + DUKE UNIVERSITY + RSTUDIO
    datasciencebox.org
    bit.ly/dsbox-adsa
    

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44. datasciencebox.org
    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
    + …
    

    View Slide