• preface
    • the role of the liberal arts in data science
    • some features of the text
    • the book is for you
  • Introduction
  • 1 what is “data science for the liberal arts?”
    • 1.1 the incompleteness of the data science Venn diagram
      • 1.1.1 additional domains
      • 1.1.2 an additional dimension
    • 1.2 Google and the liberal arts
    • 1.3 TMI
    • 1.4 discussion: what are your objectives in data science?
  • 2 getting started
    • 2.1 are you already a programmer and statistician?
    • 2.2 spreadsheets - some best practices
    • 2.3 setting up your machine: some basic tools
    • 2.4 a (modified) 15-minute rule
    • 2.5 installing R and RStudio desktop
  • 3 what R stands for …
    • 3.1 some key characteristics of R
      • 3.1.1 base R and packages
    • 3.2 cha-cha-cha-changes
    • 3.3 some technical characteristics
    • 3.4 finding help
    • 3.5 Wickham and R for Data Science 2e
  • 4 exploring R world
    • 4.1 go to the movies
    • 4.2 go into the clouds
    • 4.3 open the box
    • 4.4 go to (data)camp
    • 4.5 learn to knit
    • 4.6 older approaches
      • 4.6.1 using Swirl
      • 4.6.2 reading/watching Roger Peng’s text and/or videos
  • References

Data science for the liberal arts

Data science for the liberal arts

Kevin Lanning

2025-01-04