Reading

Exploratory data analysis involves a variety of data and techniques about how the data is used to extract the underlying information and hidden nuances. Just by looking at the data tables a normal user cannot understand much, exploratory data analysis techniques enable him to look at the data from a different perspective. Here we will look at a few techniques and their categories.

Important Pre-requisites

• P-Values
• The T-test
• $\chi^2$ Tests

Visualizing Categorical Data

One of the most important things to keep in mind while displaying any sort of graphic is to set expectations for what we might see in the graphic. Consequently, we'll glean more information from the graphic when we observe something that we didn't expect initially.

Motivation

• Generally, there's very little to learn from the plots of single categorical variables but it's worth looking at them at least once. This stems from the fact that we might observe certain patterns in these plots which might not be immediately visible in complex plots of, let's say, multivariate categorical data.
• Plotting categorical variables is not straightforward because there is a lot of preparatory steps involved in the process.

Some Basic Definitions

• Nominal Data
• Ordinal Data
• Discrete Data

Features

• Features of Categorical Variables

Good Practices