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Let them eat cake (first)! - DiZ

Let them eat cake (first)! - DiZ

Backwards design, designing educational curricula by setting goals before choosing instructional methods and forms of assessment, is a widely accepted approach to course development. In this talk, we introduce a course design approach inspired by backwards design, where students are exposed to results and findings of a data analysis first and then learn about the building blocks of the methods and techniques used to arrive at these results. We present this approach in the context of an introductory data science course that focuses on exploratory data analysis, modeling, and effective communication while requiring reproducibility and collaboration. The talk will feature examples of in-class activities as well as details of the course curriculum.

Mine Cetinkaya-Rundel

November 17, 2021
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  1. bit.ly/eat-cake-diz Q Imagine you’re new to baking, and you’re in

    a baking class. I’m going to present two options for starting the class. Which one gives you better sense of the fi nal product?
  2. bit.ly/eat-cake-diz 1 (1) Identify desired data analysis results (2) Determine

    building blocks (3) Plan learning experiences and instruction backwards design
  3. bit.ly/eat-cake-diz 2 GAISE 2016 1 NOT a commonly used subset

    of tests and intervals and produce them with hand calculations 2 Multivariate analysis requires the use of computing 3 NOT use technology that is only applicable in the intro course or that doesn’t follow good science principles 4 Not just inference & modeling, also data importing, cleaning, preparation, exploration, & visualization
  4. bit.ly/eat-cake-diz First data viz + Introduction to computational toolkit: R,

    RStudio, R Markdown, simple git Fundamentals of data, visualization, and tidying, recoding, transforming, summarizing, and importing from fi les and scraping from the web + collaboration on GitHub Ethics of working and communicating with data, algorithmic bias and what to keep in mind to minimize the risk of bias creeping in Building & selecting models, visualizing interactions, prediction & validation, inference via simulation Varies: Bayesian inference, interactive reporting and visualization, text analysis, machine learning, …
  5. bit.ly/eat-cake-diz Go to rstudio.cloud (or some other server based solution)

    Log in with your ID & pass > hello R! Install R Install RStudio Install the following packages: tidyverse rmarkdown … Load these packages Install git
  6. bit.ly/eat-cake-diz # Declare variables x < - 8 y <

    - "monkey" z < - FALSE class(x) #> [1] "numeric" class(y) #> [1] “character" class(z) #> [1] "logical" Declare the following variables Then, determine the class of each variable Open today’s demo project Knit the document and discuss the results with your neighbor Then, change Turkey to a different country, and plot again
  7. bit.ly/eat-cake-diz but let’s focus on the task at hand… Open

    today’s demo project Knit the document and discuss the results with your neighbor Then, change Turkey to a different country, and plot again
  8. bit.ly/eat-cake-diz un_votes %>% f i lter(country %in% c("UK & NI",

    “US”, "Turkey")) %>% inner_join(un_roll_calls, by = "rcid") %>% inner_join(un_roll_call_issues, by = "rcid") %>% group_by(country, year = year(date), issue) %>% summarize( votes = n(), percent_yes = mean(vote = = "yes") ) %>% f i lter(votes > 5) %>% # only use records where there are more than 5 votes ggplot(mapping = aes(x = year, y = percent_yes, color = country)) + geom_smooth(method = "loess", se = FALSE) + facet_wrap(~ issue) + labs( title = "Percentage of Yes votes in the UN General Assembly", subtitle = "1946 to 2015", y = "% Yes", x = "Year", color = "Country" )
  9. bit.ly/eat-cake-diz un_votes %>% f i lter(country %in% c("UK & NI",

    “US”, "Turkey")) %>% inner_join(un_roll_calls, by = "rcid") %>% inner_join(un_roll_call_issues, by = "rcid") %>% group_by(country, year = year(date), issue) %>% summarize( votes = n(), percent_yes = mean(vote = = "yes") ) %>% f i lter(votes > 5) %>% # only use records where there are more than 5 votes ggplot(mapping = aes(x = year, y = percent_yes, color = country)) + geom_smooth(method = "loess", se = FALSE) + facet_wrap(~ issue) + labs( title = "Percentage of Yes votes in the UN General Assembly", subtitle = "1946 to 2015", y = "% Yes", x = "Year", color = "Country" ) "Turkey"
  10. bit.ly/eat-cake-diz un_votes %>% f i lter(country %in% c("UK & NI",

    “US”, “France")) %>% inner_join(un_roll_calls, by = "rcid") %>% inner_join(un_roll_call_issues, by = "rcid") %>% group_by(country, year = year(date), issue) %>% summarize( votes = n(), percent_yes = mean(vote = = "yes") ) %>% f i lter(votes > 5) %>% # only use records where there are more than 5 votes ggplot(mapping = aes(x = year, y = percent_yes, color = country)) + geom_smooth(method = "loess", se = FALSE) + facet_wrap(~ issue) + labs( title = "Percentage of Yes votes in the UN General Assembly", subtitle = "1946 to 2015", y = "% Yes", x = "Year", color = "Country" ) "France"
  11. bit.ly/eat-cake-diz Q Which motivates you more to learn how to

    cook: perfectly chopped onions or ratatouille?
  12. bit.ly/eat-cake-diz Q Which motivates you more to learn how to

    cook: perfectly chopped onions or ratatouille?
  13. bit.ly/eat-cake-diz Create a visualization displaying whether the vote was on

    an amendment. Create a visualization displaying how US, UK, and Turkey voted over the years on issues of arms control and disarmament, colonialism, economic development, human rights, nuclear weapons, and Palestinian con fl ict.
  14. bit.ly/eat-cake-diz ggplot(data = un_votes_joined, mapping = aes(x = year, y

    = percent_yes, color = country)) + geom_smooth(method = "loess", se = FALSE)
  15. bit.ly/eat-cake-diz ggplot(data = un_votes_joined, mapping = aes(x = year, y

    = percent_yes, color = country)) + geom_smooth(method = "loess", se = FALSE) + facet_wrap(~ issue)
  16. bit.ly/eat-cake-diz ggplot(data = un_votes_joined, mapping = aes(x = year, y

    = percent_yes, color = country)) + geom_smooth(method = "loess", se = FALSE) + facet_wrap(~ issue) + labs( title = "Percentage of 'Yes' votes in the UN General Assembly", subtitle = "1946 to 2015", y = "% Yes", x = "Year", color = "Country" )
  17. bit.ly/eat-cake-diz students will encounter lots of new challenges along the

    way — let that happen, and then provide a solution
  18. bit.ly/eat-cake-diz Lesson: Web scraping essentials for turning a structured table

    into a data frame in R. Ex 1: Scrape the data off the web and save as a row of a data frame.
  19. bit.ly/eat-cake-diz Lesson: Web scraping essentials for turning a structured table

    into a data frame in R. Ex 2: How do we get the same information from each speech’s webpage and combine all information in a data frame? Ex 1: Scrape the data off the web and save as a row of a data frame.
  20. bit.ly/eat-cake-diz Lesson: Web scraping essentials for turning a structured table

    into a data frame in R. Lesson: “Just enough” string parsing, regular expressions, and iteration Ex 2: How do we get the same information from each speech’s webpage and combine all information in a data frame? Ex 1: Scrape the data off the web and save as a row of a data frame.
  21. bit.ly/eat-cake-diz score rank ethnicity gender bty_avg <dbl> <chr> <chr> <chr>

    <dbl> 1 4.7 tenure track minority female 5 2 4.1 tenure track minority female 5 3 3.9 tenure track minority female 5 4 4.8 tenure track minority female 5 5 4.6 tenured not minority male 3 6 4.3 tenured not minority male 3 7 2.8 tenured not minority male 3 8 4.1 tenured not minority male 3.33 9 3.4 tenured not minority male 3.33 10 4.5 tenured not minority female 3.17 … … … … … … 463 4.1 tenure track minority female 5.33 Hamermesh, Parker. “Beauty in the classroom: instructors pulchritude and putative pedagogical productivity”, Econ of Ed Review, Vol 24-4. Estimate the difference between the average evaluation score of male and female faculty.
  22. bit.ly/eat-cake-diz t.test(evals$score ~ evals$gender) # Welch Two Sample t -

    test # data: evals$score by evals$gender # t = -2.7507, df = 398.7, p - value = 0.006218 # alternative hypothesis: true difference in # means is not equal to 0 # 95 percent conf i dence interval: # -0.24264375 -0.04037194 # sample estimates: # mean in group female mean in group male # 4.092821 4.234328 library(tidyverse) library(infer) evals %>% specify(score ~ gender) %>% generate(reps = 15000, type = "bootstrap") %>% calculate(stat = "diff in means", order = c("male", "female")) %>% summarise( l = quantile(stat, 0.025), u = quantile(stat, 0.975) )
  23. bit.ly/eat-cake-diz library(tidyverse) library(infer) evals %>% specify(score ~ gender) %>% generate(reps

    = 15000, type = "bootstrap") %>% calculate(stat = "diff in means", order = c("male", "female")) %>% summarise(l = quantile(stat, 0.025), u = quantile(stat, 0.975)) # l u # 0.0410 0.243
  24. bit.ly/eat-cake-diz Let them eat cake (first)!* mine-cetinkaya-rundel [email protected] @minebocek *

    You can tell them all about the ingredients later! bit.ly/eat-cake-diz bit.ly/repo-eat-cake