The course consists of 4 parts. After giving a more detailed overview of the course, Part I introduces R the main programming language that we will use. Part II covers what is often called descriptive statistics. It also gives us room to learn more about R when we massage data into shape, compute summary statistics and plot various different data types in various different ways.

Part III is the main theoretical part. It covers what is often called inferential statistics. Two aspects distinguish this course from the bulk of its cousins out there. First, we use a dual-pronged approach, i.e., we are going to introduce both the frequentist and the Bayesian approach to statistical inference side by side. The motivation for this is that seeing the contrast between the two will aid our understanding of either one. Second, we will use a computational approach, i.e., we foster understanding of mathematical notions with computer simulations or other variants of helpful code.

Part IV covers applications of what we have learned so far. It focuses on generalized linear models, a class of models that have become the new standard for analyses of experimental data in the social and psychological sciences, but are also very useful for data exploration on other domains.