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Slides for the "A first-year undergraduate data science course" talk at useR 2016 at Stanford
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Slides for the "A first-year undergraduate data science course" talk at useR 2016 at Stanford.

You can watch the video of this talk here.

Course info

The course titled Better Living Through Data Science: Exploring / Modeling / Predicting / Understanding. Previous course home pages:

Talk outline

  • Course info
    • Overview: audience, content, description
    • Structure:
      • Skills: data wrangling, EDA, visualization, basic inference, modeling, effective communication of results
      • Computation: R + RStudio + git/GitHub
      • Case studies: movie reviews, sports, airline delays, paris paintings, ...
      • Assessment: in class team exercises, individual HW, midterm + final project, take home final exam
  • Computation:
    • R/RStudio: server
    • R Markdown: why and how
      • Why:
        • Noble goal: Only workflow is a reproducible workflow
        • Teaching goal:
          • Seeing code and output in one place helps learning
          • Syntax highlighting
        • Efficiency goal: Easier grading!
      • How: Knit early and often
    • git/GitHub via RStudio: why and how
      • Why: Early introducton + marketibility
      • How:
        • As course management system
        • For team collaboration
        • Details on org/repo
      • Challenges:
        • Remember to pull before starting work
        • Resolving merge conflicts
        • Sometimes RStudio interface isn't sufficient
        • First assignment an individual assignment (not team)
  • Exercise examples:
    • modeling paris paintings
    • basketball data scraping + shiny
  • Interest and impact:
    • data on student interest for course
    • curricular changes inspired by course
      • gender balance comment
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