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
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
3. introductory data science course undergraduate curriculum in statistics and data science scope
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
6. responsible computing
7. reproducibility
8. #1: convince researchers to adopt a reproducible research workflow #2: train new researchers who don’t have any other workflow
9. data analysis - descriptive stats - plots & tables - model output write-up - research question & context - interpretations - conclusions lab report copy-paste copy-paste traditional
10. a better approach text block data analysis text block data analysis text block or
11. version control
12. each assignment as a Git repo distributed on GitHub collected under a course organization
13. responsible data collection
14. web scraping
15. activity: scrape and analyze Nicola Sturgeon’s COVID briefings
16. robotstxt::paths_allowed("https://www.gov.scot/") www.gov.scot [1] TRUE first ask, can I?
17. actually, first ask, should I?
19. finding data sources
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.
21. responsible datasets
22. encoding people
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
24. https://www.significancemagazine.com/culture/624-lgbt-resources-for-statisticians-and-data-scientists
25. drawing maps
26. activity: improve a visualization on fisheries around the world
27. 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"
28. 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
29. responsible visualizations
30. activity: assess and improve accessibility
31. responsible exposure
32. providing choices
33. activity: make first data visualization within the first 15 minutes of course
35. responsible models + algorithms
36. ordering topics
38. assigning sentiment
40. responsible modules + threads
42. responsible sharing
43. 🔗 datasciencebox.org
45. responsible activities ?
46. 🔗 bit.ly/teach-ds-responsible mine-cetinkaya-rundel cetinkaya.mine@gmail.com minebocek Photo by charlesdeluvio on Unsplash thank you!