Introduction
To better meet customer expectations, clustering methods help us to design strategies based on segments rather than the entire population. We can, for example, design marketing campaigns, avoid customer leakage or assess credit risk more accurately by analyzing clusters in a given population and designing specific strategies for each cluster. Clustering methods are also useful for revealing which similar characteristics are shared by subgroups of a population.
This course presents different clustering techniques for revealing subgroups within a larger population, as well as methods for analyzing and understanding what characteristics distinguish each group. In addition, the course introduces the idea of dynamic clustering, or time-dependent clustering, in which we learn to track movements between clusters over time.
The focus of the course is practical and analytical, preparing students for jobs as data analysts/scientists or for a master's degree in Data Science for Business or Business Analytics.