Slides #

If you are registered for the course, you can find the material on UM Learn.

Some of these slides borrow heavily from other sources. In particular, I would like to acknowledge R for Data Science and Rafael Irizarry’s notes on data visualization.

• Course overview
• Summary statistics
• Data visualization
• Principles of effective visualization)
• Linear regression
  • Residual analysis
  • More examples
• Relational data
  • Mutating joins
  • Filtering joins
  • RDBMS
• Text data
  • Regular expressions
  • Sentiment analysis
• Web scraping
  • More scraping examples
  • Scraping with XPath
  • APIs; you can find the recorded lecture here
• Logistic regression
• Automating data analysis tasks