In many areas of science new tools and strategies are developed to measure multiple features at subjects and objects of interest. Typical for these new types of data is that they occur in large volumes, are high-dimensional, or are hierarchically structured. For example, in genetics (humans, animals, plants) data can be available at the levels of DNA, RNA, proteins, metabolites and all kinds of phenotypes. These new data require new techniques for analysis and visualization which are provided by a new science that combines elements of statistics and machine learning: Data Science.
This course will make you familiar with a modern toolbox of analysis techniques at the interface of statistics and machine learning. You will develop the skills to build and evaluate modeling strategies for complex (big, high-dimensional, hierarchically-structured) data as occurring in the areas relevant to WIAS and PE&RC. Moreover, the course will give you insights in the connections between modern modeling strategies and will teach you to ask the right questions in order to choose the best method for the data at hand. Illustrations will come from relevant case studies.