FBLA Data Science & AI Practice Test

Data that is classified by labels or categories is known as categorical data. This type of data is often used in statistical analysis and machine learning, where it can help categorize and group similar items based on their attributes. Categorical data includes variables that can take on a limited, fixed number of possible values, which are often represented as names or labels.

For example, colors, types of animals, or customer feedback ratings can all be considered categorical data. It allows researchers and data scientists to classify and analyze data effectively based on distinct categories rather than focusing solely on numerical values. In contrast, unstructured data refers to information that is not organized in a pre-defined format, numeric data consists of numbers that can be quantified, and structured data is organized in a predefined manner, often in tables or databases, but does not inherently categorize data into labels or groups. This makes categorical data a specific type of structured data that is focused on classification.