Variables are fundamental in data science, representing data points that can vary. They are crucial for analysis, modeling, and interpreting data effectively.
Variables come in different types: categorical, numerical, discrete, and continuous. Each type has unique characteristics and uses in data analysis.
Categorical variables represent distinct categories or groups. Examples include gender, location, and product type. They are used to group data and perform classification tasks.
Numerical variables represent measurable quantities. They can be discrete (e.g., counts) or continuous (e.g., temperature). They are essential for quantitative analysis and statistical modeling.
Understanding variables is crucial for effective data analysis. They help in creating accurate models, drawing insights, and making data-driven decisions.