Data exploration, often called exploratory analysis, uses descriptive statistical methods to learn about and understand the characteristics of a dataset.

This includes exploring measures of central tendency (e.g. mean, median), measures of spread (standard deviation, range, variance). It also might include exploring the structure of the data, for example splitting the dataset by a categorical variable, or creating visualisations to view the data in different ways.

This stage of analysis is often where a lot of data cleaning happens, as you can often spot missing data or outliers during this process.