The art and science of teaching data science (UoE Biology)

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-uoe-bio
   mine-cetinkaya-rundel
   cetinkaya.mine@gmail.com
   @minebocek
   

   View Slide

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 scienti
   fi
   c 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
   fi
   elds 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/
   fi
   les/pdfs/GAISE/GaiseCollege_Full.pdf
   

   View Slide

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 scienti
   fi
   c 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
   fi
   elds 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/
   fi
   les/pdfs/GAISE/GaiseCollege_Full.pdf
   1 NOT a
   commonly used
   subset of tests
   and intervals
   and produce
   them with hand
   calculations
   

   View Slide

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 scienti
   fi
   c 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
   fi
   elds 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/
   fi
   les/pdfs/GAISE/GaiseCollege_Full.pdf
   2 Multivariate
   analysis
   requires the use
   of computing
   

   View Slide

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 scienti
   fi
   c 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
   fi
   elds 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/
   fi
   les/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
   

   View Slide

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 scienti
   fi
   c 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
   fi
   elds 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/
   fi
   les/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 satis
   fi
   es 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. ‣ Go to RStudio Cloud
   
   
   ‣ Start the project titled UN Votes
   

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

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11. ‣ 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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12. ‣ 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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13. 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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14. 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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15. content
    

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16. ex. 1
    
    
    fi
    sheries of the world
    

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17. View Slide

18. ✴ data joins
    

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19. ✴ data joins
    
    
    ✴ data science ethics
    

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

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

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

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23. ex. 2
    
    
    First Minister’s COVID brie
    fi
    ngs
    

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24. View Slide

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

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

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

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

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29. ✴ 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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30. Project: The North South Divide: University Edition
    
    
    Question: Does the geographical location of a UK university a
    ff
    ect its
    university score?
    
    
    Team: Fried Egg Jelly Fish
    

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31. ex. 3
    
    
    spam
    fi
    lters
    

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32. ✴ logistic regression
    
    
    ✴ prediction
    

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33. ✴ logistic regression
    
    
    ✴ prediction
    
    
    ✴ decision errors
    
    
    ✴ sensitivity /
    speci
    fi
    city
    
    
    ✴ intuition around
    loss functions
    

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34. 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 F
    l
    ores XXXTENTACION
    
    
    Plug Walk Rich The Kid
    
    
    Moonlight XXXTENTACION
    
    
    Nevermind Dennis Lloyd
    
    
    In My Mind Dynoro
    
    
    changes XXXTENTACION
    

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35. pedagogy
    

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36. 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
    re
    fl
    ection of the week’s material
    

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37. View Slide

38. # 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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39. 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
    re
    fl
    ection of the week’s material
    creativity: assignments that
    make room for creativity
    

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40. View Slide

41. View Slide

42. infrastructure & tooling
    

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

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44. 📦
    ghclass
    + +
    

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45. 📦
    ghclass
    +
    

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46. openness
    

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47. View Slide

48. View Slide

49. on
    

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50. View Slide

51. View Slide

52. 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-uoe-bio
    

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