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Image credit: Thomas Pedersen, data-imaginist.com/art the art and science of teaching data science mine çetinkaya-rundel bit.ly/ds-art-sci-nordstat mine-cetinkaya-rundel cetinkaya.mine@gmail.com @minebocek duke university & rstudio

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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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your fi rst data visualization + R / RStudio, R Markdown, simple Git

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your fi rst data visualization + R / RStudio, R Markdown, simple Git fundamentals of data & data viz, confounding variables, Simpson’s paradox, tidy data, recoding & transforming, web scraping & iteration + collaboration on GitHub

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your fi rst data visualization + R / RStudio, R Markdown, simple Git fundamentals of data & data viz, confounding variables, Simpson’s paradox, tidy data, recoding & transforming, web scraping & iteration + collaboration on GitHub ethical considerations around misrepresentation of data, relying on ML algorithms and the biases they might carry, privacy of one’s own data and reusing others’ data

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your fi rst data visualization + R / RStudio, R Markdown, simple Git fundamentals of data & data viz, confounding variables, Simpson’s paradox, tidy data, recoding & transforming, web scraping & iteration + collaboration on GitHub ethical considerations around misrepresentation of data, relying on ML algorithms and the biases they might carry, privacy of one’s own data and reusing others’ data building & selecting models, visualising interactions, prediction & validation, inference via simulation

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your fi rst data visualization + R / RStudio, R Markdown, simple Git fundamentals of data & data viz, confounding variables, Simpson’s paradox, tidy data, recoding & transforming, web scraping & iteration + collaboration on GitHub ethical considerations around misrepresentation of data, relying on ML algorithms and the biases they might carry, privacy of one’s own data and reusing others’ data building & selecting models, visualising interactions, prediction & validation, inference via simulation choose your own adventure: text analysis, Bayesian inference, Interactive visualization and reporting + communication & dissemination

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‣ Go to RStudio Cloud - bit.ly/dsbox-cloud ‣ Start the project titled UN Votes

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‣ Go to RStudio Cloud - bit.ly/dsbox-cloud ‣ Start the project titled UN Votes ‣ Open the R Markdown document called unvotes.Rmd

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‣ Go to RStudio Cloud - bit.ly/dsbox-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

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‣ Go to RStudio Cloud - bit.ly/dsbox-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 “France” in the code and replace it with another country Knit again, and review how the voting patterns of the country you picked compares to the United States and United Kingdom

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three questions that keep me up at night… 1 what should students learn? 2 how will students learn best? 3 what tools will enhance student learning?

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three questions that keep me up at night… 1 what should students learn? 2 how will students learn best? 3 what tools will enhance student learning? content pedagogy infrastructure

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content

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ex. 1 fi sheries of the world

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✴ data joins

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✴ data joins ✴ data science ethics

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✴ data joins ✴ data science ethics ✴ critique ✴ improving data visualisations

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✴ data joins ✴ data science ethics ✴ critique ✴ improving data visualisations ✴ mapping

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Project: 2016 US Election Redux Question: Would the outcome of the 2016 US Presidential Elections been di ff erent had Bernie Sanders been the Democrat candidate? Team: 4 Squared

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ex. 2 First Minister’s COVID brie fi ngs

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✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions

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✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions ✴ functions ✴ iteration

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

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

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✴ web scraping ✴ text parsing ✴ data types ✴ regular expressions ✴ functions ✴ iteration ✴ data visualisation ✴ interpretation ✴ text analysis ✴ data science ethics robotstxt::paths_allowed("https://www.gov.scot") #> www.gov.scot #> [1] TRUE

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Project: The North South Divide: University Edition Question: Does the geographical location of a UK university a ff ect its university score? Team: Fried Egg Jelly Fish

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pedagogy

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teams: weekly labs in teams + periodic team evaluations + term project in teams

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teams: weekly labs in teams + periodic team evaluations + term project in teams “minute paper”: weekly online quizzes ending with a brief re fl ection of the week’s material

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# 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

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teams: weekly labs in teams + periodic team evaluations + term project in teams peer feedback: on projects “minute paper”: weekly online quizzes ending with a brief re fl ection of the week’s material

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teams: weekly labs in teams + periodic team evaluations + term project in teams peer feedback: on projects “minute paper”: weekly online quizzes ending with a brief re fl ection of the week’s material web native (aka COVID friendly)

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teams: weekly labs in teams + periodic team evaluations + term project in teams peer feedback: on projects “minute paper”: weekly online quizzes ending with a brief re fl ection of the week’s material web native (aka COVID friendly) creativity: assignments that make room for creativity

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infrastructure & tooling

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student-facing + 📦 ghclass + instructor-facing 📦 checklist + + 📦 learnr + 📦 parsermd 📦 gradethis 📦 learnrhash

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📦 ghclass + +

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openness

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datasciencebox.org

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rstudio-education.github.io/dsbox

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on

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introds.org

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rstd.io/design-ds-class

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Image credit: Thomas Pedersen, data-imaginist.com/art the art and science of teaching data science mine çetinkaya-rundel mine-cetinkaya-rundel cetinkaya.mine@gmail.com @minebocek bit.ly/ds-art-sci-nordstat duke university & rstudio