Data Analysis and Business Intelligence

Course content

Advanced biostatistics with R (explorative data analysis, risk assessment, association measures, hypothesis testing), dispositive data (DWH, ODS) OLAP, ETL, multi-dimensional data modeling, data mining, DWH architectures, multi-dimensional modeling, reports according to IBCS

Learning outcomes

The students understand decision-making processes in the health care sector in particular at the management level. They can analyze complex facts and prepare decisions based on facts and data.

Recommended or required reading and other learning resources / tools

Books: Kemper et al.: Business Intelligence; Weiß: Basiswissen medizinische Statistik; Fahrmeir et al.: Statistik - Der Weg zur Datenanalyse; Bruce et al.: Practical Statistics for Data Scientists 50+ Essential Concepts Using R and Python

Mode of delivery

Lecture, group work, lab practical, presentation

Prerequisites and co-requisites

None

Assessment methods and criteria

Lecture: Exam; Tutorial: Continuous Assessment