This type of data represents the characteristics of an object; for example, gender, marital status, type of address, or categories of the movies. This data is often referred to as qualitative datasets in statistics. To understand clearly, here are some of the most common types of categorical data you can find in data:

• Gender (Male, Female, Other, or Unknown)
• Marital Status (Annulled, Divorced, Interlocutory, Legally Separated, Married, Polygamous, Never Married, Domestic Partner, Unmarried, Widowed, or Unknown)
• Movie genres (Action, Adventure, Comedy, Crime, Drama, Fantasy, Historical, Horror, Mystery, Philosophical, Political, Romance, Saga, Satire, Science Fiction, Social, Thriller, Urban, or Western)

• Blood type (A, B, AB, or O)
• Types of drugs (Stimulants, Depressants, Hallucinogens, Dissociatives, Opioids, Inhalants, or Cannabis)

A variable describing categorical data is referred to as a categorical variable. These types of variables can have one of a limited number of values. It is easier for computer science students to understand categorical values as enumerated types or enumerations of variables. There are different types of categorical variables:

• A binary categorical variable can take exactly two values and is also referred to as a dichotomous variable. For example, when you create an experiment, the result is either success or failure. Hence, results can be understood as a binary categorical variable. 
• Polytomous variables are categorical variables that can take more than two possible values. For example, marital status can have several values, such as annulled, divorced, interlocutory, legally separated, married, polygamous, never married, domestic partners, unmarried, widowed, domestic partner, and unknown. Since marital status can take more than two possible values, it is a polytomous variable. 

Most of the categorical dataset follows either nominal or ordinal measurement scales. Let's understand what is a nominal or ordinal scale in the next section.