Image credit: Thomas Pedersen, data-imaginist.com/art the art and science of teaching data science mine çetinkaya-rundel bit.ly/ds-art-sci-wids mine-cetinkaya-rundel cetinkaya.mine@gmail.com @minebocek fosstodon.org/@minecr

2016 GAISE 1. Teach statistical thinking. ‣ Teach statistics as an investigative process of problem-solving and decision making. Students should not leave their introductory statistics course with the mistaken impression that statistics consists of an unrelated collection of formulas and methods. Rather, students should understand that statistics is a problem-solving and decision making process that is fundamental to scienti fi c inquiry and essential for making sound decisions. ‣ Give students experience with multivariable thinking. We live in a complex world in which the answer to a question often depends on many factors. Students will encounter such situations within their own fi elds of study and everyday lives. We must prepare our students to answer challenging questions that require them to investigate and explore relationships among many variables. Doing so will help them to appreciate the value of statistical thinking and methods. 2. Focus on conceptual understanding. 3. Integrate real data with a context and purpose. 4. Foster active learning. 5. Use technology to explore concepts and analyse data. 6. Use assessments to improve and evaluate student learning. amstat.org/asa/ fi les/pdfs/GAISE/GaiseCollege_Full.pdf

2016 GAISE 1. Teach statistical thinking. ‣ Teach statistics as an investigative process of problem-solving and decision making. Students should not leave their introductory statistics course with the mistaken impression that statistics consists of an unrelated collection of formulas and methods. Rather, students should understand that statistics is a problem-solving and decision making process that is fundamental to scienti fi c inquiry and essential for making sound decisions. ‣ Give students experience with multivariable thinking. We live in a complex world in which the answer to a question often depends on many factors. Students will encounter such situations within their own fi elds of study and everyday lives. We must prepare our students to answer challenging questions that require them to investigate and explore relationships among many variables. Doing so will help them to appreciate the value of statistical thinking and methods. 2. Focus on conceptual understanding. 3. Integrate real data with a context and purpose. 4. Foster active learning. 5. Use technology to explore concepts and analyse data. 6. Use assessments to improve and evaluate student learning. amstat.org/asa/ fi les/pdfs/GAISE/GaiseCollege_Full.pdf 1 NOT a commonly used subset of tests and intervals and produce them with hand calculations

2016 GAISE 1. Teach statistical thinking. ‣ Teach statistics as an investigative process of problem-solving and decision making. Students should not leave their introductory statistics course with the mistaken impression that statistics consists of an unrelated collection of formulas and methods. Rather, students should understand that statistics is a problem-solving and decision making process that is fundamental to scienti fi c inquiry and essential for making sound decisions. ‣ Give students experience with multivariable thinking. We live in a complex world in which the answer to a question often depends on many factors. Students will encounter such situations within their own fi elds of study and everyday lives. We must prepare our students to answer challenging questions that require them to investigate and explore relationships among many variables. Doing so will help them to appreciate the value of statistical thinking and methods. 2. Focus on conceptual understanding. 3. Integrate real data with a context and purpose. 4. Foster active learning. 5. Use technology to explore concepts and analyse data. 6. Use assessments to improve and evaluate student learning. amstat.org/asa/ fi les/pdfs/GAISE/GaiseCollege_Full.pdf 2 Multivariate analysis requires the use of computing

2016 GAISE 1. Teach statistical thinking. ‣ Teach statistics as an investigative process of problem-solving and decision making. Students should not leave their introductory statistics course with the mistaken impression that statistics consists of an unrelated collection of formulas and methods. Rather, students should understand that statistics is a problem-solving and decision making process that is fundamental to scienti fi c inquiry and essential for making sound decisions. ‣ Give students experience with multivariable thinking. We live in a complex world in which the answer to a question often depends on many factors. Students will encounter such situations within their own fi elds of study and everyday lives. We must prepare our students to answer challenging questions that require them to investigate and explore relationships among many variables. Doing so will help them to appreciate the value of statistical thinking and methods. 2. Focus on conceptual understanding. 3. Integrate real data with a context and purpose. 4. Foster active learning. 5. Use technology to explore concepts and analyse data. 6. Use assessments to improve and evaluate student learning. amstat.org/asa/ fi les/pdfs/GAISE/GaiseCollege_Full.pdf 3 NOT use technology that is only applicable in the intro course or that doesn’t follow good science principles

2016 GAISE 1. Teach statistical thinking. ‣ Teach statistics as an investigative process of problem-solving and decision making. Students should not leave their introductory statistics course with the mistaken impression that statistics consists of an unrelated collection of formulas and methods. Rather, students should understand that statistics is a problem-solving and decision making process that is fundamental to scienti fi c inquiry and essential for making sound decisions. ‣ Give students experience with multivariable thinking. We live in a complex world in which the answer to a question often depends on many factors. Students will encounter such situations within their own fi elds of study and everyday lives. We must prepare our students to answer challenging questions that require them to investigate and explore relationships among many variables. Doing so will help them to appreciate the value of statistical thinking and methods. 2. Focus on conceptual understanding. 3. Integrate real data with a context and purpose. 4. Foster active learning. 5. Use technology to explore concepts and analyse data. 6. Use assessments to improve and evaluate student learning. amstat.org/asa/ fi les/pdfs/GAISE/GaiseCollege_Full.pdf 4 Data analysis isn’t just inference and modelling, it’s also data importing, cleaning, preparation, exploration, and visualisation

a course that satis fi es these four points is looking more like today’s intro data science courses than (most) intro stats courses but this is not because intro stats is inherently “bad for you” instead it is because it’s time to visit intro stats in light of emergence of data science

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‣ Go to Posit Cloud ‣ Start the project titled UN Votes

‣ Go to Posit Cloud ‣ Start the project titled UN Votes ‣ Open the Quarto document called unvotes.qmd

‣ Go to Posit Cloud ‣ Start the project titled UN Votes ‣ Open the Quarto document called unvotes.qmd ‣ Render the document and review the data visualization you just produced

‣ Go to Posit Cloud ‣ Start the project titled UN Votes ‣ Open the Quarto document called unvotes.qmd ‣ Knit the document and review the data visualization you just produced ‣ Then, look for the character string “Turkey” in the code and replace it with another country of your choice ‣ Render again, and review how the voting patterns of the country you picked compare to the United States and the United Kingdom

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?

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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ex. 1 fi sheries of the world

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✴ data joins

✴ data joins ✴ data science ethics

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

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

Project: Regional differences in average GPA and SAT Question: Exploring the regional differences in average GPA and SAT score across the US and the factors that could potentially explain them. Team: Mine’s Minions

ex. 2 First Minister’s COVID brie fi ngs

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

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

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

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

✴ 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

Project: Factors Most Important to University Ranking Question: Explore how various metrics (e.g., SAT/ACT scores, admission rate, region, Carnegie classi fi cation) predict rankings on the Niche College Ranking List. Team: 2cool4school

ex. 3 spam fi lters

✴ logistic regression ✴ prediction

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

Project: Predicting League of Legends success Question: After 10 minutes into the game, whether a gold lead or an experienced lead was a better predictor of which team wins? Team: Blue Squirrels

Project: A Critique of Hollywood Relationship Stereotypes Question: How has the average age difference between two actors in an on-screen relationship changed over the years? Furthermore, do on-screen same-sex relationships have a different average age gap than on-screen heterosexual relationships? Team: team300

pedagogy

teams: weekly labs in teams + periodic team evaluations + term project in teams peer feedback: used minimally so far, but positive experience “minute paper”: weekly online quizzes ending with a brief re fl ection of the week’s material

teams: weekly labs in teams + periodic team evaluations + term project in teams peer feedback: used minimally so far, but positive experience “minute paper”: weekly online quizzes ending with a brief re fl ection of the week’s material creativity: assignments that make room for creativity

Çetinkaya-Rundel, Mine, Mine Dogucu, and Wendy Rummer fi eld. "The 5Ws and 1H of term projects in the introductory data science classroom." Statistics Education Research Journal 21.2 (2022): 4-4.

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infrastructure & tooling

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

course organization students members assignments repos

course organization teams teams projects repos

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Beckman, M. D., Çetinkaya- Rundel, M., Horton, N. J., Rundel, C. W., Sullivan, A. J., & Tackett, M. "Implementing version control with Git and GitHub as a learning objective in statistics and data science courses." Journal of Statistics and Data Science Education 29.sup1 (2021): S132-S144.

openness

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on

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Çetinkaya-Rundel, Mine, and Victoria Ellison. "A fresh look at introductory data science." Journal of Statistics and Data Science Education 29.sup1 (2021): S16-S26.

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-wids fosstodon.org/@minecr