The term "Data Science" has become an important buzzword in dealing with big data. Data scientists handle and analyze large amounts of data to generate information and derive recommendations for action that enable organizations to work more efficiently. To achieve this, various analytical tools and methods are used to extract valuable information from confusing data sets, from which hypotheses are subsequently derived.

This lecture provides an end-to-end look at the data science process. In the first part of the lecture, the fundamentals of data management are taught. It will be shown how large dynamic data sets can be integrated, consolidated and finally used for complex analysis tasks. In particular, the topics of data warehousing, multidimensional modeling and relational mapping are covered. Building on this, complex data models, e.g. graphs and visual analysis as a means of data interpretation are discussed. The second part of the lecture covers methods of data analysis. This includes classical statistical methods like classification, clustering and forecasting as well as machine learning methods like neural networks. Besides the methods themselves, modern analysis tools for their application are also presented. The theoretical content of the lectures is complemented by practical exercises.