Categorical Data
Categorical data, also known as qualitative data, represents variables that can be divided into distinct groups or categories without a natural numerical order. It is used to classify items based on attributes, labels, or characteristics, such as gender, color, or product type. This type of data is fundamental in statistics, data science, and machine learning for tasks like classification, grouping, and analysis of non-numeric attributes.
Developers should learn about categorical data when working with datasets that include non-numeric features, such as in data preprocessing for machine learning models or database design. It is essential for handling variables like user demographics, product categories, or survey responses, where encoding techniques (e.g., one-hot encoding) are applied to convert categories into numerical formats for computational analysis. Understanding categorical data helps in building accurate models and ensuring data integrity in applications like recommendation systems or customer segmentation.