Clustering methods help to design strategies based on customer segments rather than the entire population, in order to better meet customer expectations. 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 subgroups of a population share.
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.
- Unsupervised vs Supervised learning
- Key clustering algorithms, including: Hierarchical clustering, K-means, and density-based clustering methods
- Cluster analysis on different feature spaces
- Statistical methods to analyze the clusters
- Clustering vs Dynamic clustering
This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.