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Exploratory Data Analysis EDA

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What is Exploratory Data Analysis?

exploratory data analysis

EDA takes a broad look at data and tries to make sense of it.

Exploratory Data Analysis (EDA) is an approach to analyzing data. It’s where the researcher takes a bird’s eye view of the data and tries to make some sense of it. It’s often the first step in data analysis, implemented before any formal statistical techniques are applied. Although specific statistical techniques can be used, like creating histograms or box plots, EDA is not a set of techniques or procedures; the Engineering Statistics Handbook calls EDA a “philosophy.” EDA is considered by some to be more of an art form than a science.

Exploratory data analysis is a complement to inferential statistics, which tends to be fairly rigid with rules and formulas. EDA involves the analyst trying to get a “feel” for the data set, often using their own judgment to determine what the most important elements in the data set are.

The purpose of exploratory data analysis is to:

Other, specific knowledge can be obtained through EDA such as creating a ranked list of relevant factors. You may not necessarily include all of the above items in your data analysis, although it’s likely you’ll want to include at least a few. They should be viewed as guidelines, rather than rigid rules.

Types of Exploratory Data Analysis

EDA falls into four main areas:

  • Univariate non-graphical — looking at one variable of interest, like age, height, income level etc.
  • Univariate graphical.
  • Multivariate non-graphical — analysis of multiple variables at the same time.
  • Multivariate graphical.
Exploratory Data Analysis EDA was last modified: March 23rd, 2017 by Andale

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