Advancing into Analytics

Chapter 1. Foundations of Exploratory Data Analysis

“You never know what is gonna come through that door,” Rick Harrison says in the opening of the hit show Pawn Stars. It’s the same in analytics: confronted with a new dataset, you never know what you are going to find. This chapter is about exploring and describing a dataset so that we know what questions to ask of it. The process is referred to as exploratory data analysis, or EDA.

What Is Exploratory Data Analysis?

American mathematician John Tukey promoted the use of EDA in his book, Exploratory Data Analysis (Pearson). Tukey emphasized that analysts need first to explore the data for potential research questions before jumping into confirming the answers with hypothesis testing and inferential statistics.

EDA is often likened to “interviewing” the data; it’s a time for the analyst to get to know it and learn about what interesting things it has to say. As part of our interview, we’ll want to do the following:

• Classify our variables as continuous, categorical, and so forth

• Summarize our variables using descriptive statistics

• Visualize our variables using charts

EDA gives us a lot to do. Let’s walk through the process using Excel and a real-life dataset. You can find the data in the star.xlsx workbook, which can be found in the datasets folder of this book’s repository, under the star subfolder. This dataset was collected for a study to examine the impact of class size on test scores. For this and other Excel-based ...