Lecture

Companies in research and industry use data to safeguard decisions and to offer data-intensive products and services. The competencies required for these processes are summarised under the term Data Science. The analysis of large amounts of data is composed of scalable data management, parallel algorithms, statistical modeling, and secure handling of the complex interaction of various instruments and platforms and is anchored in various disciplines. On the one hand, this lecture is intended to explain to the participants what is expected of future data scientists and on the other hand to give them the skills they need to fulfill these expectations. The methodical knowledge imparted in the course is intended to be a short “how-to” and enable the participants to independently decide when and why certain methods are to be used. Since one of the biggest problems in data analysis is often the wrong question, the lecture will also look at the company perspective to solve typical company problems and ask the right questions for suitable data analysis. The lecture presents concepts and instruments that are needed throughout the entire data science pipeline. In addition to the correct approach, the lecture will discuss the interpretation of the analysis results as well as their visualization and transformation into business models. In the accompanying exercises, presented methods and algorithms will be applied in practice, focusing on web programming, statistics, and the manipulation of data sets.