Data Analytics for Information Systems (DAIS)

Contents

This course provides a hands-on introduction to master the essentials of data analytics and machine learning using R.

The growing ubiquity of information systems both in organizational and private consumer contexts increasingly makes large data streams available in various domains. As part of the digital transformation, knowing how to handle these data sets, how to analyze and to interpret them, becomes a more and more important skillset in companies, policymaking and in academic research.

The course builds on real-word data sets from information systems in the realm of consumer behavior, in particular in the resource consumption context. Based on hands-on examples and practical challenges, we cover fundamental data analytics methods using the software environment R.

The course starts with basic concepts from descriptive and inferential statistics that will be needed in the following course units, followed by an introduction to the statistics software R and R Studio. Students will be introduced to experimental design to distinguish between correlation and causation and to critically evaluate the validity and reliability of results. In the following, a large share of the course is dedicated to regression analysis, clustering, and different classification techniques. Students will apply these methods to data sets from concrete real-world challenges. The course closes with a discussion of relevant privacy regulations and also highlights social concerns and ethical aspects.

In the second half of the semester, students have the possibility to earn bonus points in a course project (self-study), by applying the skills and methods covered in the lecture and exercise sessions in the analysis of a large real-world dataset.