HR Analytics

• Fundamentals of data analytics (e.g. processes and techniques) and application contexts of HR analytics • Forms of data representation and visualisation (e.g. data formats, graphs, diagrams) • Introduction to selected areas of statistics • Introduction to the application of the programming language Julia • Functionality and exemplary application of machine learning • Fundamental methods for the analysis and interpretation of data • HRM examples and usage scenarios, including their application with regard to key figures from HR Management • Dealing with sensitive and personal data in connection with HR analytics, escpcially with regard to aspects of gender and diversity management

Mode of delivery

face to face

Type

compulsory

Recommended or required reading and other learning resources/tools

Runkler (2012): Data Analytics. Wiesbaden: Springer Provost/Fawcett (2013): Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc. Strohmeier/Piazza (2015): Human Resource Intelligence und Analytics. Springer. Wickham/Grolemund (2016): R for data science: import, tidy, transform, visualize, and model data. O'Reilly Media, Inc. Bruce/Bruce (2017): Practical statistics for data scientists: 50 essential concepts. O'Reilly Media, Inc. Khan/Millner (2020): Introduction to People Analytics: A Practical Guide to Data-driven HR. Kogan Page Publishers

Planned learning activities and teaching methods

Lecture (classroom teaching and online), guest lectures, exercises,group work, presentations, tests and quizzes, case studies

Assessment methods and criteria

Continuous assessment and final written exam

Prerequisites and co-requisites

none.

Infos

Degree programme

Digital HR & angewandtes Arbeitsrecht

Cycle

Master

ECTS Credits

3.00

Language of instruction

German

Curriculum

Part-Time

Academic year

2022

Semester

1 WS

Incoming

No

Learning outcome

After successful completion of the course, the students can • explain fundamental statistical methods for data analysis and apply them in connection with cases of application from HR Management • use the statistics coding language R for selected aspects of data analysis • explain forms of data representation and visualisation and apply them case-relatedly • describe application possibilities of machine learning in HRM • analyse and interpret selected forms of data in HR Management and relate them with key figures • explain challenges and risks when dealing with sensitive and personal data in connection with HR analytics, escpcially with regard to aspects of gender and diversity management

Course code

1705-21-01-BB-DE-05