Teaching data science, responsibly

Transcript

1. teaching data science,
   responsibly
   🔗 bit.ly/teach-ds-responsible
   mine-cetinkaya-rundel
   cetinkaya.mine@gmail.com
   minebocek
   mine çetinkaya-rundel
   Photo by charlesdeluvio on Unsplash
   

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2. thread elements of responsible data science
   throughout a curriculum
   feature instruction of ethics as a
   standalone unit in a curriculum
   goals
   convince you that we need to both…
   and do so with examples
   

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3. introductory data science course
   undergraduate curriculum in statistics
   and data science
   scope
   

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

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6. responsible computing
   

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7. reproducibility
   

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8. #1:
   
   
   convince researchers
   to adopt a
   reproducible
   research workflow
   #2:
   
   
   train new researchers
   who don’t have any
   other workflow
   

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9. data
   
   
   analysis
   - descriptive stats
   
   
   - plots & tables
   
   
   - model output
   write-up
   - research question
   
   
   & context
   
   
   - interpretations
   
   
   - conclusions
   lab
   
   
   report
   copy-paste copy-paste
   traditional
   

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10. a better
    approach
    text block
    data analysis
    text block
    data analysis
    text block
    or
    

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11. version control
    

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12. each
    assignment
    as a Git repo
    distributed
    on GitHub
    collected
    under a
    course
    organization
    

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13. responsible data collection
    

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14. web scraping
    

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15. activity:
    
    
    scrape and
    analyze Nicola
    Sturgeon’s
    COVID
    briefings
    

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16. robotstxt::paths_allowed("https://www.gov.scot/")
    
    
    www.gov.scot
    
    
    [1] TRUE
    first ask, can I?
    

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17. actually, first ask, should I?
    

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19. finding data sources
    

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20. get students out of the mindset of “internet
    search as the only way to access data” and
    connect them with domain experts, data
    librarians, etc.
    

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21. responsible datasets
    

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22. encoding people
    

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23. don’t use variables that reinforce
    the idea that gender is
    dichotomous or that
    exclude LGBT+ people
    present data analyses that
    reinforce negative
    stereotypes about
    marginalized groups
    do present
    analyses that
    are inclusive
    give context
    when using
    data where
    gender is
    dichotomized
    be mindful
    when collecting
    data on
    students for in-
    class exercises
    

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24. https://www.significancemagazine.com/culture/624-lgbt-resources-for-statisticians-and-data-scientists
    

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25. drawing maps
    

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26. activity:
    
    
    improve a
    visualization on
    fisheries around
    the world
    

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27. 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"
    

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28. 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
    

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29. responsible visualizations
    

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30. activity:
    
    
    assess and
    improve
    accessibility
    

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31. responsible exposure
    

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32. providing choices
    

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33. activity:
    
    
    make first data
    visualization
    within the first
    15 minutes of
    course
    

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35. responsible models + algorithms
    

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36. ordering topics
    

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38. assigning sentiment
    

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40. responsible modules + threads
    

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42. responsible sharing
    

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43. 🔗 datasciencebox.org
    

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45. responsible activities
    ?
    

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46. 🔗 bit.ly/teach-ds-responsible
    mine-cetinkaya-rundel
    cetinkaya.mine@gmail.com
    minebocek
    Photo by charlesdeluvio on Unsplash
    thank you!
    

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