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Categorical Data

Data is referred to as a collection of information. Data can be of anything, i.e. data of toys, buildings, cities, etc. Data can vary from place to place and person to person. Depending on the type of data, we can classify data into two types:
1. Numerical data (quantitative data)
2. Categorical data (qualitative data)

• Numerical data: The data that considers only quantitative values of any data are numerical. For example, how many children are there in a class, how many computers are there in the computer lab, etc., belong to numerical data.

• Categorical data: The data which defines the quality of the data, like size, colour, taste, etc., are all considered under categorical data.
Let us study more about categorical data.

Categorical data

Categorical, as the term denotes, means the data that is differentiated based on categories or qualities. The grouping in categorical data is done based on raw information that we have.

These qualities can be anything – food, colour, taste, type of clothes, different age groups, etc. It does not mean that categorical data only deals with the qualitative analysis of the data.

Numerical values also denoted the quality of the data. For example, which city has more population is a type of numerical data, but which city has more population so that we can decide which needs the focus of development is categorical data.

Therefore, categorical data and numerical data are linked to each other. Examples of categorical data

1. Different types of seasons in a year.
2. Different types of plants in a home.
3. Various types of food items in the kitchen.
4. Multiple amounts of ingredients in the fridge.
5. Different festivals occur in a particular month.

Categorical data will have particular categories, such as hair colours, perfume brands, book genres, film genres, age groups, festivals, etc.

Types of categorical data

There are two types of categorical data:

• Nominal categorical data: The term nominal is derived from the Latin word nomen, meaning name. The nominal categorical data includes labelled or named data. These data do not consider any numerical value for grouping the data. The data is purely differentiated based on the qualities of the names of the data.
For example, different types of flowers, seasons, various cities in India, etc., all belong to the nominal categorical data.
• Ordinal categorical data: In this data, we can represent a certain measure or scale. We can group the data based on measurements. This kind of data may not be specific and may vary depending upon the numerical values. Due to numbers, ordinal categorical data possesses the properties of both categorical and numerical data.

Ordinal data can be studied with the help of graphs and pictorial representations. This is so because the data is too large to check in the numbers manually.

Therefore, we need to represent them in graphical representation for better understanding.

For example, survey data, poll data, etc., are considered under ordinal categorical data.

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