Introductory data science, a fresh look (Young ISA)

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

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How can we effectively and ef fi 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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demonstrate concrete course examples share a few tips provide open-source teaching resources goals

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data visualisation data wrangling, tidying, acquisition exploratory data analysis predictive modeling + uncertainty quanti fi 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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topics

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ex. 1 united nations

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

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‣ 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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‣ 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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‣ 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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ex. 2 fi sheries of the world

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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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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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✓ data joins ✓ ethics ✓ critique ✓ improving visualisations

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

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ex. 3 First Minister’s COVID brie fi ngs

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

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

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

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

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

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ex. 3 spam fi lters

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✓ logistic regression ✓ prediction

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✓ logistic regression ✓ prediction ✓ decision errors ✓ sensitivity / speci fi city ✓ intuition around loss functions

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✓ machine learning for text data

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✓ repetition tips

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✓ repetition ✓ re fl 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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tips ✓ repetition ✓ re fl ection ✓ creativity

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tips ✓ re fl ection ✓ creativity ✓ peer review

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tips ✓ repetition ✓ re fl ection ✓ creativity ✓ peer review ✓ real work fl ows

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toolbox student

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toolbox instructor

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🔗 datasciencebox.org

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🔗 introds.org

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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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Journal of Statistics Education Special Issue on Computing in the Curriculum 🔗 tandfonline.com/doi/full/10.1080/10691898.2020.1870416 🔗 causeweb.org/cause/webinars

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