FeaturedUdemy
Cluster Analysis in R
4.9(71)44k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
Cluster analysis is an invaluable technique in data science for grouping observations with shared characteristics, enabling data-driven decisions in fields like marketing and economics. This intermediate course builds intuition for hierarchical and k-means clustering in R through practical exercises with soccer stats, wholesale customer spending, and wage data over time.
Who It's For
Intermediate R users and data analysts who want to build strong intuition for customer segmentation, pattern discovery, and grouping algorithms.
Key Takeaways
- Core Clustering Methods: Build intuition for hierarchical clustering (including occupation trees) and k-means clustering in R.
- Model Evaluation & Selection: Evaluate cluster validity using techniques like elbow analysis and average silhouette widths.
- Hands-On Case Study: Apply clustering skills to analyze occupational spending patterns and average salary trends over time.
About this course
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.
Advertisement