Introductory data science, a fresh look

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 will describe a fresh approach to teaching data science at the introductory level, introduce the design philosophy behind the curriculum, and give examples from course materials as well as from student projects. We will also discuss new directions in assessment and tooling as we scale up the course and move it online.

Transcript

1. introductory data science
   a fresh look
   bit.ly/fresh-ds-jmm mine-cetinkaya-rundel
   cetinkaya.mine@gmail.com
   minebocek
   mine çetinkaya-rundel
   

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2. How can we effectively and
   ef
   f
   i
   ciently teach data science
   
   
   to students with little to no
   
   
   background in computing
   
   
   and statistical thinking? How can we equip them with
   
   
   the skills and tools for reasoning
   with various types of data
   
   
   and leave them wanting
   
   
   to learn more?
   

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3. demonstrate concrete course examples
   share a few tips
   provide open-source teaching resources
   goals
   

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4. data visualisation
   
   
   data wrangling, tidying, acquisition
   
   
   exploratory data analysis
   
   
   predictive modeling + uncertainty quanti
   f
   i
   cation
   
   
   effective communication of results
   interactive visualizations
   
   
   text analysis
   
   
   machine learning
   
   
   Bayesian inference
   
   
   …
   consistent syntax | tidyverse
   
   
   reproducibility | R Markdown
   
   
   version control and collaboration | Git + GitHub
   focus on
   emphasise
   foray into
   

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5. topics
   

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7. ex. 1
   
   
   united nations
   

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8. ‣ Go to RStudio Cloud
   
   
   ‣ Start the project titled UN
   Votes
   rstd.io/dsbox-cloud
   

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9. ‣ Go to RStudio Cloud
   
   
   ‣ Start the project titled UN
   Votes
   
   
   ‣ Open the R Markdown
   document called unvotes.Rmd
   rstd.io/dsbox-cloud
   

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10. ‣ 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
    rstd.io/dsbox-cloud
    

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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
    
    
    ‣ 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
    rstd.io/dsbox-cloud
    

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13. ex. 2
    
    f
    i
    sheries of the world
    

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15. fisheries %>% select(country)
    
    
    #> # A tibble: 75 x 1
    
    
    #> country
    
    
    #>
    
    
    #> 1 Algeria
    
    
    #> 2 Angola
    
    
    #> 3 Argentina
    
    
    #> 4 Australia
    
    
    #> 5 Bangladesh
    
    
    #> 6 Brazil
    
    
    #> 7 Cambodia
    
    
    #> 8 Canada
    
    
    #> 9 Chile
    
    
    #> 10 Colombia
    
    
    #> # … with 65 more rows
    continents
    
    
    #> # A tibble: 245 x 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
    
    
    #> # … with 235 more rows
    fisheries <- left_join(fisheries, continents)
    
    
    Joining, by = “country"
    ✓ data joins
    

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16. fisheries %>%
    
    
    filter(is.na(continent))#> # A tibble: 75 x 1
    
    
    #> # A tibble: 5 x 4
    
    
    #> country capture aquaculture continent
    
    
    #>
    
    
    #> 1 Congo, Democratic Republic of the 220000 2965 NA
    
    
    #> 2 Hong Kong 161964 4130 NA
    
    
    #> 3 Myanmar 1742956 474510 NA
    
    
    #> 4 Other 9685851 786993 NA
    
    
    #> 5 Taiwan (Republic of China) 1017243 304756 NA
    ✓ data joins
    ✓ ethics
    

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17. ✓ data joins
    ✓ ethics
    ✓ critique
    ✓ improving
    
    
    visualisations
    

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18. ✓ data joins
    ✓ ethics
    ✓ critique
    ✓ improving
    
    
    ✓ visualisations
    ✓ mapping
    

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20. ex. 3
    
    
    First Minister’s COVID brie
    f
    i
    ngs
    

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22. robotstxt::paths_allowed("https://www.gov.scot/")
    
    
    www.gov.scot
    
    
    [1] TRUE
    ✓ ethics
    

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23. ✓ web scraping
    ✓ text parsing
    ✓ data types
    ✓ regular expressions
    ✓ ethics
    

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24. ✓ web scraping
    ✓ text parsing
    ✓ data types
    ✓ regular expressions
    ✓ functions
    ✓ iteration
    ✓ ethics
    

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25. ✓ web scraping
    ✓ text parsing
    ✓ data types
    ✓ regular expressions
    ✓ functions
    ✓ iteration
    ✓ visualisation
    ✓ interpretation
    ✓ ethics
    

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26. ✓ web scraping
    ✓ text parsing
    ✓ data types
    ✓ regular expressions
    ✓ functions
    ✓ iteration
    ✓ visualisation
    ✓ interpretation
    ✓ text analysis
    ✓ ethics
    

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28. ex. 3
    
    
    spam
    f
    i
    lters
    

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29. ✓ logistic regression
    ✓ prediction
    

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30. ✓ logistic regression
    ✓ prediction
    ✓ decision errors
    ✓ sensitivity /
    speci
    f
    i
    city
    ✓ intuition around
    loss functions
    

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32. ✓ machine learning
    for text data
    

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33. ✓ repetition
    tips
    

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34. ✓ repetition
    ✓ re
    f
    l
    ection
    # 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 tips
    

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35. tips
    ✓ repetition
    ✓ re
    f
    l
    ection
    ✓ creativity
    

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36. tips
    ✓ re
    f
    l
    ection
    ✓ creativity
    ✓ peer review
    

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37. tips
    ✓ repetition
    ✓ re
    f
    l
    ection
    ✓ creativity
    ✓ peer review
    ✓ real work
    f
    l
    ows
    

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38. toolbox
    student
    

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39. toolbox
    instructor
    

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40. datasciencebox.org
    

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41. Mine Çetinkaya-Rundel &
    
    
    Victoria Ellison (2020)
    
    
    A Fresh Look at Introductory
    Data Science
    
    
    Journal of Statistics Education
    
    
    DOI: 10.1080/10691898.2020.1804497
    

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42. introds.org
    

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43. bit.ly/fresh-ds-jmm
    mine-cetinkaya-rundel
    cetinkaya.mine@gmail.com
    minebocek
    datasciencebox.org
    

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