Understanding the various levels of data is necessary to perform feature engineering. When it comes time to build new features, or fix old ones, we must have ways of identifying how to work with every column.

Here is a quick table to summarize what is and isn't possible at every level:

The following is a table showing the types of statistics allowed at each level:

And finally, the following is a table showing purely the graphs that are and are not possible at each level:

Whenever you are faced with a new dataset, here is a basic workflow to follow:

1. Is the data organized or unorganized? Does our data exist in a tabular format with distinct rows and columns, or does it exist as a mess of text in an unstructured format?
2. Is each column quantitative or qualitative? Are the values in the cells numbers that represent quantity, or strings that do not?
3. At what level of data is each column? Are the values at the nominal, ordinal, interval, or ratio level?
4. What graphs can I utilize to visualize my data—bar, pie, box, histogram, and so on?