Learn How to Perform Cluster Analysis

Cluster analysis is a powerful toolkit in the data science workbench. It is used to find groups of observations (clusters) that share similar characteristics. These similarities can inform all kinds of business decisions; for example, in marketing, it is used to identify distinct groups of customers for which advertisements can be tailored.

Explore Hierarchical and K-Means Clustering Techniques

In this course, you will learn about two commonly used clustering methods - hierarchical clustering and k-means clustering. You won't just learn how to use these methods, you'll build a strong intuition for how they work and how to interpret their results. You'll develop this intuition by exploring three different datasets: soccer player positions, wholesale customer spending data, and longitudinal occupational wage data.

Hone Your Skills with a Hands-On Case Study

You’ll finish the course by applying your new skills to a case study based around average salaries and how they have changed over time. This will combine hierarchical clustering techniques such as occupation trees, preparing for exploration, and plotting occupational clusters, with k-means techniques including elbow analysis and average silhouette widths.

DataCamp courses are comprised of a mixture of videos, articles, and practice exercises so that you have the chance to test and cement your new-found skills so that you feel confident applying them outside a course setting.

cluster	Milk	Grocery	Frozen	cluster size
1	16950	12891	991	5
2	2512	5228	1795	29
3	10452	22550	1354	5
4	1249	3916	10888	6

Interpreting the wholesale customer clusters

Cluster Analysis in R

Hands-on interactive exercise