Nominal Data
The first type of categorical data that we
encounter in data science are nominal data.
Nominal data are a type of categorical data.
That is, they are used to represent named
qualities.
However, nominal data have no natural rank
order to them (they differ by their name alone).
For example, the colors red, green, and yellow
all describe the color of apples.
However, no one color is greater than or less than another color.
These three colors have no natural rank order
to them.
They differ by their name alone.
Other examples of nominal data include: your
name, your credit card number, and the name
of the city where you were born.
The key distinction is that nominal values
have no natural order to them.
However, they can still be sorted alphabetically.
There are a limited number of mathematical operations that we can perform on nominal data
We can test two nominal values for equality
(i.e. we can determine if they are the same named category).
In addition, we can determine their mode (i.e.
we can get the most frequently occurring category
in a set of nominal values).
Despite these limitations, nominal data are
still quite useful in data science.