Applied HR Analytics

• Analysis of key figures (number of hits per job advertisement, costs per job application) • Analysis of the aging structure in companies • Aptitude-diagnostic processes for employee recruitment • Algorithms for behavioural prediction • Analysis of employee fluctuations • Analysis of grounds for dismissal • Measurement of employee satisfaction and performance • Regression analysis, forecasting methods • Methods of evidence-based decision-making

Mode of delivery

face to face



Recommended or required reading and other learning resources/tools

Edwards, M. und Edwards, K. (2019). Predictive HR Analytics: Mastering the HR Metric. Kogan Page. Isson, J.P., Harriott, J. and Fitzenz, J. (2016). People Analytics in the Era of Big Data: Changing the Way you attract, acquire, develop, and retain talent. John Wiley and Sons. Smith, T. (2013). HR Analytics: The what, why and how… Create Space Publishing. Sesil, J. (2013). Applying Advanced Analytics to HR Management Decisions. Pearson. Wirges, F. und Ahlbrecht, M. (2019). HR Analytics: Was HR-Verantwortliche und Führungskräfte wissen können und müssen. Berlin: Springer. Reindl, C und Krügl, S. (2017). People Analytics in der Praxis. Haufe.

Planned learning activities and teaching methods

Lecture, discussion, presentation, feedback, e-learning, self-organised learning, exercises

Assessment methods and criteria

Continuous assessment and final written exam

Prerequisites and co-requisites

Course Quantitative Research Methods and Statistics


Degree programme

Digital HR & angewandtes Arbeitsrecht



ECTS Credits


Language of instruction




Academic year



2 SS



Learning outcome

After successful completion of the course, the students can • analyse HR-related administrative and processual data • translate current questions of HR Management into specific hypotheses and investigate them with statistical methods • analyse key performance indicators • apply BI-tools and dashboards • make decisions concerning employees, cooperation and communication based on data protection regulations • make predictions based on collected data sets and describe connections

Course code