Even (and more so) with advances in deep neural networks, understanding data remains a central challenge in data science, but central to its value. Data mapping techniques help us to see salient features of our data sets, explore different dimensions of the underlying systems, and support statistical decision making. I will discuss and demonstrate practical methods to data mapping, from dimensional reduction and clustering over self-organizing maps to deep learning-based variational autoencoders.