The art and science of teaching data science

Abstract: 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-ares
   mine-cetinkaya-rundel
   cetinkaya.mine@gmail.com
   @minebocek
   

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2. 2016 GAISE
   1. Teach statistical thinking.
   ‣ Teach statistics as an investigative process of problem-solving and decision making.
   Students should not leave their introductory statistics course with the mistaken impression
   that statistics consists of an unrelated collection of formulas and methods. Rather,
   students should understand that statistics is a problem-solving and decision making
   process that is fundamental to scientific inquiry and essential for making sound decisions.
   ‣ Give students experience with multivariable thinking. We live in a complex world in
   which the answer to a question often depends on many factors. Students will encounter
   such situations within their own fields of study and everyday lives. We must prepare our
   students to answer challenging questions that require them to investigate and explore
   relationships among many variables. Doing so will help them to appreciate the value of
   statistical thinking and methods.
   2. Focus on conceptual understanding.
   3. Integrate real data with a context and purpose.
   4. Foster active learning.
   5. Use technology to explore concepts and analyse data.
   6. Use assessments to improve and evaluate student learning.
   amstat.org/asa/files/pdfs/GAISE/GaiseCollege_Full.pdf
   

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3. 2016 GAISE
   1. Teach statistical thinking.
   ‣ Teach statistics as an investigative process of problem-solving and decision making.
   Students should not leave their introductory statistics course with the mistaken impression
   that statistics consists of an unrelated collection of formulas and methods. Rather,
   students should understand that statistics is a problem-solving and decision making
   process that is fundamental to scientific inquiry and essential for making sound decisions.
   ‣ Give students experience with multivariable thinking. We live in a complex world in
   which the answer to a question often depends on many factors. Students will encounter
   such situations within their own fields of study and everyday lives. We must prepare our
   students to answer challenging questions that require them to investigate and explore
   relationships among many variables. Doing so will help them to appreciate the value of
   statistical thinking and methods.
   2. Focus on conceptual understanding.
   3. Integrate real data with a context and purpose.
   4. Foster active learning.
   5. Use technology to explore concepts and analyse data.
   6. Use assessments to improve and evaluate student learning.
   amstat.org/asa/files/pdfs/GAISE/GaiseCollege_Full.pdf
   1 NOT a
   commonly used
   subset of tests
   and intervals
   and produce
   them with hand
   calculations
   

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4. 2016 GAISE
   1. Teach statistical thinking.
   ‣ Teach statistics as an investigative process of problem-solving and decision making.
   Students should not leave their introductory statistics course with the mistaken impression
   that statistics consists of an unrelated collection of formulas and methods. Rather,
   students should understand that statistics is a problem-solving and decision making
   process that is fundamental to scientific inquiry and essential for making sound decisions.
   ‣ Give students experience with multivariable thinking. We live in a complex world in
   which the answer to a question often depends on many factors. Students will encounter
   such situations within their own fields of study and everyday lives. We must prepare our
   students to answer challenging questions that require them to investigate and explore
   relationships among many variables. Doing so will help them to appreciate the value of
   statistical thinking and methods.
   2. Focus on conceptual understanding.
   3. Integrate real data with a context and purpose.
   4. Foster active learning.
   5. Use technology to explore concepts and analyse data.
   6. Use assessments to improve and evaluate student learning.
   amstat.org/asa/files/pdfs/GAISE/GaiseCollege_Full.pdf
   2 Multivariate
   analysis
   requires the use
   of computing
   

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5. 2016 GAISE
   1. Teach statistical thinking.
   ‣ Teach statistics as an investigative process of problem-solving and decision making.
   Students should not leave their introductory statistics course with the mistaken impression
   that statistics consists of an unrelated collection of formulas and methods. Rather,
   students should understand that statistics is a problem-solving and decision making
   process that is fundamental to scientific inquiry and essential for making sound decisions.
   ‣ Give students experience with multivariable thinking. We live in a complex world in
   which the answer to a question often depends on many factors. Students will encounter
   such situations within their own fields of study and everyday lives. We must prepare our
   students to answer challenging questions that require them to investigate and explore
   relationships among many variables. Doing so will help them to appreciate the value of
   statistical thinking and methods.
   2. Focus on conceptual understanding.
   3. Integrate real data with a context and purpose.
   4. Foster active learning.
   5. Use technology to explore concepts and analyse data.
   6. Use assessments to improve and evaluate student learning.
   amstat.org/asa/files/pdfs/GAISE/GaiseCollege_Full.pdf
   3 NOT use
   technology that
   is only
   applicable in the
   intro course or
   that doesn’t
   follow good
   science
   principles
   

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6. 2016 GAISE
   1. Teach statistical thinking.
   ‣ Teach statistics as an investigative process of problem-solving and decision making.
   Students should not leave their introductory statistics course with the mistaken impression
   that statistics consists of an unrelated collection of formulas and methods. Rather,
   students should understand that statistics is a problem-solving and decision making
   process that is fundamental to scientific inquiry and essential for making sound decisions.
   ‣ Give students experience with multivariable thinking. We live in a complex world in
   which the answer to a question often depends on many factors. Students will encounter
   such situations within their own fields of study and everyday lives. We must prepare our
   students to answer challenging questions that require them to investigate and explore
   relationships among many variables. Doing so will help them to appreciate the value of
   statistical thinking and methods.
   2. Focus on conceptual understanding.
   3. Integrate real data with a context and purpose.
   4. Foster active learning.
   5. Use technology to explore concepts and analyse data.
   6. Use assessments to improve and evaluate student learning.
   amstat.org/asa/files/pdfs/GAISE/GaiseCollege_Full.pdf
   4 Data analysis
   isn’t just
   inference and
   modelling, it’s
   also data
   importing,
   cleaning,
   preparation,
   exploration, and
   visualisation
   

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7. a course that satisfies these four
   points is looking more like today’s
   intro data science courses than
   (most) intro stats courses
   but this is not because
   intro stats is inherently
   “bad for you”
   instead it is because it’s time to visit
   intro stats in light of emergence of
   data science
   

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8. View Slide

9. fundamentals of
   data & data viz,
   confounding variables,
   Simpson’s paradox
   +
   R / RStudio,
   R Markdown, simple Git
   tidy data, data frames
   vs. summary tables,
   recoding &
   transforming,
   web scraping & iteration
   +
   collaboration on GitHub
   

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10. fundamentals of
    data & data viz,
    confounding variables,
    Simpson’s paradox
    +
    R / RStudio,
    R Markdown, simple Git
    tidy data, data frames
    vs. summary tables,
    recoding &
    transforming,
    web scraping & iteration
    +
    collaboration on GitHub
    building & selecting
    models,
    visualising interactions,
    prediction & validation,
    inference via simulation
    

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11. fundamentals of
    data & data viz,
    confounding variables,
    Simpson’s paradox
    +
    R / RStudio,
    R Markdown, simple Git
    tidy data, data frames
    vs. summary tables,
    recoding &
    transforming,
    web scraping & iteration
    +
    collaboration on GitHub
    building & selecting
    models,
    visualising interactions,
    prediction & validation,
    inference via simulation
    data science ethics,
    text analysis,
    Bayesian inference
    +
    communication &
    dissemination
    

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12. fundamentals of
    data & data viz,
    confounding variables,
    Simpson’s paradox
    +
    R / RStudio,
    R Markdown, simple Git
    tidy data, data frames
    vs. summary tables,
    recoding &
    transforming,
    web scraping & iteration
    +
    collaboration on GitHub
    building & selecting
    models,
    visualising interactions,
    prediction & validation,
    inference via simulation
    data science ethics,
    text analysis,
    Bayesian inference
    +
    communication &
    dissemination
    

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13. ‣ Go to RStudio Cloud
    ‣ Start the project titled UN Votes
    

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14. ‣ Go to RStudio Cloud
    ‣ Start the project titled UN Votes
    ‣ Open the R Markdown document called unvotes.Rmd
    

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15. ‣ Go to RStudio 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
    

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16. ‣ Go to RStudio 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 the character string “Turkey” in the code and replace it with
    another country of your choice
    ‣ Knit again, and review how the voting patterns of the country you picked
    compares to the United States and United Kingdom & Northern Ireland
    

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17. 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?
    

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18. 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
    

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19. content
    

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20. ex. 1
    fisheries of the world
    

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22. ✴ data joins
    

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23. ✴ data joins
    ✴ data science ethics
    

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

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

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26. Project: 2016 US Election Redux
    Question: Would the outcome of the 2016 US Presidential Elections been
    different had Bernie Sanders been the Democrat candidate?
    Team: 4 Squared
    

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27. ex. 2
    First Minister’s COVID briefings
    

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28. View Slide

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

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

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

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

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33. ✴ 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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34. Project: The North South Divide: University Edition
    Question: Does the geographical location of a UK university affect its
    university score?
    Team: Fried Egg Jelly Fish
    

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35. ex. 3
    spam filters
    

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36. ✴ logistic regression
    ✴ prediction
    

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

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38. Project: Spotify Top 100 Tracks of 2017/18
    Question: Is it possible to predict the year a song made the Top Tracks
    playlist based on its metadata?
    Team: weR20
    year ~ danceability + energy + key + loudness + mode + speechiness +
    acousticness + instrumentalness + liveness + valence + tempo +
    duration_s
    2017
    name artists
    I'm the One DJ Khaled
    Redbone Childish Gambino
    Sign of the Times Harry Styles
    2018
    name artists
    Everybody Dies In Their Nightmares XXXTENTACION
    Jocelyn Flores XXXTENTACION
    Plug Walk Rich The Kid
    Moonlight XXXTENTACION
    Nevermind Dennis Lloyd
    In My Mind Dynoro
    changes XXXTENTACION
    

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39. pedagogy
    

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40. teams: weekly labs in teams +
    periodic team evaluations +
    term project in teams
    peer feedback: used
    minimally so far, but
    positive experience
    “minute paper”: weekly online
    quizzes ending with a brief
    reflection of the week’s material
    

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41. View Slide

42. # A tibble: 19 x 2
    bigram n
    
    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
    

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43. teams: weekly labs in teams +
    periodic team evaluations +
    term project in teams
    peer feedback: used
    minimally so far, but
    positive experience
    “minute paper”: weekly online
    quizzes ending with a brief
    reflection of the week’s material
    creativity: assignments that
    make room for creativity
    

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44. View Slide

45. View Slide

46. infrastructure & tooling
    

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47. student-facing
    +
    
    ghclass
    +
    instructor-facing
    
    checklist
    +
    +
    
    learnr
    +
    
    parsermd
    
    gradethis
    
    learnrhash
    

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48. 
    ghclass
    + +
    

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49. 
    ghclass
    +
    

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50. openness
    

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51. View Slide

52. View Slide

53. View Slide

54. on
    

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55. 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-ares
    

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