The course shows how different algorithms can be used to obtain a segmentation of insurance data. The methods covered range from centroid-based (k-means, k-prototypes) to probabilistic (Gaussian Mixture Models) and density-based (DBSCAN) approaches. We demonstrate how the clustering results can be visualized and evaluated. Moreover, it will be shown how the clustering results can be used to identify outliers in the data set. We also cover techniques that reduce the dimension of the data so that the segments can be computed either on aggregated information or using only a subset of the available information. The course puts an emphasis on the practical application and therefore showcases all concepts on an insurance data set.