Digital technologies provide us with an ever-increasing amount of data, e.g. in the context of online services, electronic commerce, financial services, digital humanities, computational social science, or life sciences. But how can we turn massive volumes of potentially noisy, time-stamped, and high-dimensional data into knowledge? How can we reason about relationships and patterns? Can we use these patterns to make predictions that can inform decision-making or help to design recommender systems? And how can we explore large volumes of noisy and high-dimensional data?

This course equips students both with theoretical and practical skills in data analytics, data science and statistical learning that can be used to address these questions. It combines a series of theory lectures on key concepts and algorithms with interactive practice lectures, which demonstrate how they can be applied using state-of-the-art python packages. The course material consists of annotated slides for theory lectures and jupyter notebooks for the practice lectures. Students can further test and deepen their knowledge through practical projects that accompany the course.