What is Categorical Data?

Data is information collected for a specific use and which is usually expressed as facts or numbers. When the data can be grouped into different, well-defined, mutually-exclusive categories, it’s called categorical data.

Categorical data is qualitative in nature. It can be in the form of numbers, but these numbers won’t have mathematical properties, such as sum, multiplication, and other operations. For example, a variable with two values such as “1” indicates Yes and “0” indicating No.

Moreover, categorical data can be classified into ordinal data and nominal data. The difference between the two is the capacity to organize the data within a category according to a specified order or scale that ordinal data has. An example of ordinal data is the days of the week.

Categorical data is usually more challenging to handle than numerical or quantitative data. However, the more structured it is, the easier to manage it. Additional problems include a higher tendency for human error. For example, a person may enter “Monday” and “Monday.” While both represent the same value, the algorithm may need to learn to consider them the same.

Although it is usually required that all the possible categories are known in advance, there are some cases where this is not possible. In these cases, techniques such as the Dirichlet process, which considers an infinite initial number of categories, are useful.


Why is Categorical Data Invaluable?

Both categorical and numerical data are the main foundations of analytics. Together, they are the material that algorithms take as input and work with to produce insights.

Categorical data is widely used in classification problems, where sometimes it’s transformed into numerical values for easier manipulation.

It is also considered in the Natural Language Processing techniques used to extract useful information from texts.


Categorical Data and LogicPlum

At the core of LogicPlum’s businesses is data. Within it, categorical data is one of the central elements that make modeling and classification possible. Using this platform over more classical methods is its automated mode of operation, which provides a way to obtain accurate results quickly.

Additional Resources