When you have large groups of objects, it is often helpful to split them into meaningful groups or clusters. One example of this would be to identify different types of customers so that a company can more efficiently route their calls to a helpline. As a second example, suppose an automobile manufacturer wanted to segment their market to target the ads more carefully. One approach might be to take a database of recent car sales, including the social demographics associated with each customer, and segment the population purchasing each type of automobile into meaningful groups.

Specialized approaches exist if your data contains information that relates to time and geography. You can use this additional information to identify geographical and temporal hotspots. Hotspots are regions of high activity or a high value of a particular variable. These results can help you focus your attention on a particular region where a problem is occurring more than usual, such as the incidence of asthma in a large city. In both cluster and hotspot analysis, the results can help you discover new and interesting features, problems, and red flags regarding the data being analyzed.

In this course, you will explore several powerful and commonly utilized techniques for performing both cluster and hotspot analysis. You will implement these techniques using the free and open-source statistical programming language R with real-world data sets. The focus will be on making these methods accessible and applicable to your work.

You are required to have completed the following courses or have equivalent experience before taking this course:

  • Understanding Data Analytics
  • Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis
 

How It Works

Course Length
2 weeks

Effort
8 to 10 hours of study per week

Format
100% online, instructor-led
  • Current and aspiring data scientists
  • Analysts
  • Engineers
  • Researchers
  • Technical managers
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