teaching data science, responsibly 🔗 bit.ly/teach-ds-responsible mine-cetinkaya-rundel [email protected] minebocek mine çetinkaya-rundel Photo by charlesdeluvio on Unsplash

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

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

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

No content

responsible computing

reproducibility

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

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

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

version control

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

responsible data collection

web scraping

activity: scrape and analyze Nicola Sturgeon’s COVID briefings

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

actually, first ask, should I?

No content

finding data sources

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.

responsible datasets

encoding people

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

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

drawing maps

activity: improve a visualization on fisheries around the world

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"

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

responsible visualizations

activity: assess and improve accessibility

responsible exposure

providing choices

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

No content

responsible models + algorithms

ordering topics

No content

assigning sentiment

No content

responsible modules + threads

No content

responsible sharing

🔗 datasciencebox.org

No content

responsible activities ?

🔗 bit.ly/teach-ds-responsible mine-cetinkaya-rundel [email protected] minebocek Photo by charlesdeluvio on Unsplash thank you!