The modern data analyst is no longer adequately served by spreadsheet-type software and off-the-shelf one-size-fits-all software solutions. Companies are looking for multi-skilled individuals who can:

  •  translate business challenges into discrete solvable problems, 
  •  operationalize these problems into testable hypotheses,  
  •  apply appropriate statistical methods to data to rigorously test these hypotheses, 
  •  and derive actionable business strategy and inform decision making.

This class focuses on solving problems with a computational applied approach and as such a basic background in statistics is essential. We will explore the description and visualization of data, designing and performing experiments; creating statistical models based on domain knowledge; testing our understanding against data; and generating appropriate conclusions. We will learn a wide range of essential statistical methods and gain hands-on experience using different statistical techniques and tools.


Basic knowledge of undergraduate statistics is advised.

Completion of SWIRL R tutorial is strongly advised: