Modul

At the end of this course, participants should have a good understanding of the data-science process, i.e., the process of generating practical insights from large data sets, and of the different steps of this process. They should be able to explain and compare approaches for the analysis and management of large data sets in terms of their effectiveness and applicability. Participants should understand which problems are currently open in the field of Data Science and have gained insights into the current state of the art.

Our intention is to devote more attention to the Data Science process and to explicitly address the steps of this process. Techniques for analyzing large data sets are attracting great interest among users. The spectrum is broad and includes classic industries such as banks and insurance companies, but also newer players, such as Internet companies, social media, natural sciences and engineering. In all cases, the desire is to extract interesting patterns from very large data sets with as little effort as possible, and to monitor the behavior or systems. This lecture deals with the steps to extract knowledge from data, ranging from techniques to preprocess data to fundamental models to extract knowledge from data, e.g., in the form of statistics, association rules, clusters or systematic predictions.