Foundations of Data Analysis: Cluster, Cohort, and Regression

Learn about methods of data analysis that will help us explore and interpret data.

Different types of data need to be analyzed differently. For example, usage metrics need to be analyzed in relation to a time period where we can track how customers use our product over time. But when we start to dive into customer behavior, we should segment the user base and understand how the different clusters of customers behave in contrast with each other.

In this lesson, we’ll learn the most important methods for analyzing data and how to use them to set up API product analytics.

Cluster analysis

Cluster analysis is a way to use statistics to find groups of similar observations in a set of data. It is a way to break up a large set of different data into smaller, more similar groups based on patterns and relationships in the data. The following illustration shows the plot of customers across the number of developers on their team on the x-axis and the time to the first Hello World metric on the y-axis. In this example, we can see that there are clusters forming in the plot, showing that Small and Medium-sized Businesses (SMBs) tend to be in a similar range for these two variables.

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