A scatter plot is a common type of data visualization that displays individual data points as dots on a two-dimensional grid. Each point on a scatter plot represents a pair of numerical values, with one variable plotted along the x-axis and the other along the y-axis. By mapping two variables in this way, scatter plots allow you to quickly see patterns, trends, and potential relationships between them.

Scatter plots are especially useful for identifying correlations—such as whether an increase in one variable tends to correspond to an increase or decrease in another. They also help you spot clusters, outliers, and the overall spread of your data. For example, you might use a scatter plot to visualize the relationship between students' study hours and their exam scores, or to explore how temperature and ice cream sales are related.

Because each dot represents a unique data point, scatter plots are ideal for visualizing large datasets where understanding the relationship between two continuous variables is key. They are less effective for categorical data or for showing parts of a whole, but they excel at uncovering trends and patterns that might be hidden in raw numbers.