Raw data often is not useful for answering questions, making claims, or telling a story. In order to derive understanding it is usually useful to have a summary of the data which provides measures of the centrality, spread, and shape of the dataset.

A primary use of data is in understanding patterns and relationships across different variables and scenarios. As all data contains variability it is important to understand and analyze distributions both within and across variables.

Variability is omnipresent within data and datasets. Working with data depends on understanding, explaining, and quantifying variability of all forms (variability within a group, between different groups, or between samples).

While some datasets can be explored by hand, as they get bigger and more complex it becomes necessary to use digital tools for analysing data. It is important to understand which tools to use for which application or scenario, the affordances and tradeoffs, and the ethical considerations of using certain tools.

Interpreting, creating, and using models is a central component of working with data. Models are both a way to analyze data and a source of data.