preface
PART I: Introduction
1 what is “data science for the liberal arts?”
2 getting started
3 what R stands for …
4 exploring R world
Part II: Towards data literacy
5 now draw the rest of the owl
- 5.1 time for hands-on experience
- 5.2 assignment
6 literate programming
7 principles of data visualization
8 visualization in R with ggplot
9 on probability and statistics
10 reproducibility and the replication crisis
I Part III Towards data proficiency
11 wrangling and tidying
12 finding, exploring, cleaning, and combining data
13 applied data science
- 13.1 public health and covid
- 13.2 other datasets in and beyond R
  - 13.2.1 make/extract/combine your own data
  - 13.2.2 keep it manageable
14 strings, factors, dates, and times
15 lists
16 loops, functions, and beyond
17 from correlation to multiple regression
- 17.1 bivariate analysis: Galton’s height data
  - 17.1.1 correlations based on small samples are unstable: A Monte Carlo demonstration
  - 17.1.2 from correlation to regression
- 17.2 multivariate data
18 cross-validation
19 prediction and classification
- 19.1 from regression to classification: selection of a threshold
- 19.2 another approach to classification: k-nearest neighbor
20 machine learning: chihuahuas vs muffins, and other distinctions and ideas
21 working with text: a case study
22 an introduction to networks
- 22.1 key network concepts
23 case study: the network structure of computational social science
24 some ethical concerns for the data scientist
25 exercises
- 25.1 generating correlated data (GPA and SAT) and putting it in a Google Sheet
- 25.2 categorical probability and Venn diagrams
26 More exercises
- 26.1 Getting data from a package like MDSR
References

Prereqs and coreqs as sets of eligible students

26 More exercises

26.1 Getting data from a package like MDSR

library(tidyverse)
library(mdsr)
x <- data(package = "mdsr")
x <- x$results |> 
    as_tibble() |> 
    select(Item, Title)