Modulhandbuch (Module manual)

Knowledge of statistical inference and programming (e.g., R, Python, Stata)

​ The course is targeted at PhD students and young researchers who want to apply data science methods in their research. It covers various data preparation, statistical modeling, and visualization techniques for extracting knowledge from the vast and complex data sets that have emerged in business over the past years. The learning objective of the course is to enable participants to apply these techniques in design-oriented and/or quantitative empirical research projects. Topics include supervised learning for regression and classification (linear and non-linear models), unsupervised learning (e.g., clustering, dimensionality reduction), and text mining (e.g., topic modeling, sentiment analysis).