Infusing Data science into the statistics curriculum

Slides from my 2017 USCOTS workshop

Transcript

1. INFUSING DATA SCIENCE INTO THE STATISTICS CURRICULUM USCOTS 2017 Adam Loy Lawrence University Shonda Kuiper Grinnell College Laura Chihara Carleton College Ivan Ramler St. Lawrence University
2. WELCOME!
3. ds4stats.github.io/uscots2017/
5. 2014 CURRICULUM GUIDELINES • Teach how to “think with data” by having students work with real-world, unstructured datasets and train them to better communicate nuanced statistical ideas. • Practice using all steps of the scientific method to tackle real research questions. All too often, undergraduate statistics majors are handed a “canned” data set and told to analyze it using the methods currently being studied. This approach may leave them unable to solve more complex problems out of context. • Formulate good questions, consider whether available data are appropriate for addressing the problem, choose from a set of different tools, undertake the analyses in a reproducible manner, assess the analytic methods, draw appropriate conclusions, and communicate results.
6. R TUTORIAL GOALS Start with a modern and engaging question Teach core technical skills Allow students to experiment with the data, find their own patterns, and ask their own questions Allow for students of all abilities to get involved, but be easily adaptable for more advanced students.
7. CLASS TESTING Introductory courses • First-year tutorial, no prerequisite (Grinnell) • Intro. stats, Lock5 book (Lawrence) Advanced courses • Online resources for statistical modeling (Grinnell) • Homework and class examples for data science courses (Lawrence and Carleton)
8. DATA VISUALIZATION WITH ggplot2 (Assumes familiarity with types of plots used in Stat101)
9. http://bit.ly/ds4stats-rstudio Username: email prefix (all lowercase, only alphanumeric) Password: full email address (as you typed it for USCOTS)
10. RECIPE ggplot(data = <data_set>, mapping = aes(x = <x_var>, y = <y_var>)) + geom_<type>()
 Data Mappings for entire canvas Add a layer/element What mark do we need to draw? 1. What do we want R to draw? 2. What does R need to know?
11. BUILDING BLOCKS • data • geometric object (the marks we actually draw) • aesthetic mappings (how we draw the marks) • statistical transformations (how we transform the data before plotting) • faceting (small multiples)
12. DATA WRANGLING WITH dplyr (Assumes familiarity with basic summary statistics)
13. DATA VERBS Verb Meaning filter pick specific observations (i.e. specific rows) arrange reorder the rows select pick variables by their names (i.e. specific columns) mutate add new calculated columns to a data frame summarize aggregate many rows into a single row group_by group data into rows by a set of variables
14. DISCUSSION
15. LESSONS LEARNED • Make project folders available, not individual files • Extra help in lab is necessary • Ask IT early to install/update packages and activate .Rmd sharing • Review what code chunks are and how they work for the first few weeks
16. QUESTIONS • What topics do you wish you could include in your courses? • What resources would help you implement these ideas? • What did we miss?