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Teaching data science, responsibly

Teaching data science, responsibly

Mine Cetinkaya-Rundel

April 08, 2022
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  1. 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
  2. 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
  3. #1: convince researchers to adopt a reproducible research workflow #2:

    train new researchers who don’t have any other workflow
  4. data analysis - descriptive stats - plots & tables -

    model output write-up - research question & context - interpretations - conclusions lab report copy-paste copy-paste traditional
  5. 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.
  6. 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
  7. fisheries %>% select(country) #> # A tibble: 75 x 1

    #> country #> <chr> #> 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 #> <chr> <chr> #> 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"
  8. fisheries %>% filter(is.na(continent))#> # A tibble: 75 x 1 #>

    # A tibble: 5 x 4 #> country capture aquaculture continent #> <chr> <dbl> <dbl> <chr> #> 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