"Small" data are datasets that allow interaction, visualization, exploration and analysis on a local machine to drive business intelligence. This course explores the difference between "small" data and big data and provides an introduction to applied data analysis, with an emphasis on a conceptual framework for thinking about data from both statistical and machine learning perspectives. Class lectures will be supplemented by data-driven problem sets and a project.

Topics Include

  • Binary classification
  • Bootstrapping
  • Causal inference
  • Experimental design
  • Machine Learning
  • Regression
  • Statistics (frequentist, Bayesian)
  • Multiple hypothesis testing