The course comprises of 12-14 weeks of teaching consisting of lectures, exercises, and a specialization project.

The lectures will focus on gaining a theoretical and methodological understanding of Big Data processes by reading and discussing relevant literature. In addition, you will be introduced to demonstrations and worked examples of analytics and visualization. Moreover, we will also reflect upon the epistemological, ethical, and political premises and consequences of Big Data practices in different societal sectors.

During the exercises you will work in small groups on hands-on tasks. The problems can revolve around a specific type of analytics or visualization (e.g., exploratory data analysis, classification, clustering), a specific application domain (e.g., marketing, financials, transportation), or a particular challenge of Big Data processes (e.g., handling of personal data). Furthermore, you will practice communicating and presenting your results and reflections during the exercises in order to prepare for the oral exam.

In the specialization project you work in small groups on a given or self-chosen Big Data project. During the project, you will run through all phases of a typical data analysis process, from business and data understanding over modeling and evaluation to deployment. The project will prepare you to design and conduct your master thesis project in the area of Big Data.

During the course you will learn to apply a number of software tools for analytics and visualization, such as Tableau and R. 

Reports are handed in by groups and students are examined as a group and evaluated on the basis of demonstration of fulfilment of the intended learning objectives for the course.

The hand in should be about 20 pages per group. Each group should have 3-5 members.


Duration of the exam: 30 minutes per group incl. assessment and feedback