Enhance Your Data Science Toolkit with Cluster Analysis Cluster analysis is a valuable tool in the field of data science, allowing for the identification of groups of similar observations. These clusters can provide valuable insights for various business decisions, such as targeted marketing campaigns. In this comprehensive course, you will delve into two widely used clustering techniques: hierarchical clustering and k-means clustering. However, this course goes beyond mere instruction on how to use these methods. Instead, it aims to develop your intuition and understanding of their underlying principles, enabling you to effectively interpret their results. To facilitate this learning process, you will work with three diverse datasets: soccer player positions, wholesale customer spending data, and longitudinal occupational wage data. By exploring these datasets, you will gain hands-on experience and a deeper understanding of the clustering techniques. To further refine your skills, this course includes a practical case study focused on average salaries and their changes over time. This case study combines hierarchical clustering techniques, such as occupation trees and plotting occupational clusters, with k-means techniques, including elbow analysis and average silhouette widths. At DataCamp, we believe in a comprehensive learning approach. Therefore, this course incorporates a variety of resources, including videos, articles, and practice exercises. By engaging with these materials, you will have ample opportunities to test and solidify your newfound skills, ensuring that you feel confident in applying them beyond the course environment.