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.