Quantitative Data Analysis and Visualization for Business Environments
Quantitative Datenanalyse und Visualisierung im betriebswirtschaftlichen Kontext
6 LPWiSeENFÜS
Voraussetzungen
Empfohlen, im Wortlaut
Knowledge of the content of modules • 13714 Research Methods in Business Administration and Economics • 38402 Marktforschung • 38427 Forschungsmethoden der Betriebswirtschaftslehre
Inhalte
A practical research problem will be the focus of a group project in the second half of the semester. It will include a hackathon or seminar (typically one or two days) to work on the project and present a result. To prepare for the project, lectures and exercises will provide basics and guidance in visualization techniques, statistics, machine learning, and (select) multivariate methods. Examples may include: neural nets, decision trees, ANOVA, regression models, factor analysis, cluster analysis, empirical dynamic models, and more. This module starts a data analysis process from the intended final presentation and then works backwards through the process. Therefore, the module puts a strong focus on visualization, preparation, and presentation of results and findings.
Lernziele
Students are able to visualize and present data, analysis results, and data-driven research designs. They know to collect and measure data, structure datasets, and analyze data in ways that are both structured and sound, as well as practically relevant (from a business perspective). Students have a comprehensive perspective to interpret and probe multivariate methods and machine learning model results. Furthermore they are familiar with software packages for data analysis (e.g., R, JASP, Python, etc.)
Prüfungsleistung
• Short presentation (or tutorial design) of excercises, 5-10 min. (20%) • Midterm-exam, 45 min. (30%) • Final report: practial or research project in small groups (changes every term) including a Hackathon (ca. 15-20 Slides) and presentation, ca. 15 min. (50%)
Literatur (7)
- Script/Slides/Videos
- R-scripts and R-excercises + data sets
- Recommended literature: • Backhaus, K.; Erichson, B.; Plinke, W.; Weiber, R. (2016): Multivariate Analysemethoden. Springer Gabler • Hair, J.F.; Black, W.C.; Babin, B.J. ; Anderson, R.E. (2009): Multivariate Data Analysis, 7th Ed., Prentice Hall • James, G., Witten, D., Hastie, T. and Tibshirani, R., (2021): An introduction to statistical learning: with applications in R. • Berinato, S. (2016). Good charts: The HBR guide to making smarter, more persuasive data visualizations. Harvard Business Review Press.
- Backhaus, K.; Erichson, B.; Plinke, W.; Weiber, R. (2016): Multivariate Analysemethoden. Springer Gabler
- Hair, J.F.; Black, W.C.; Babin, B.J. ; Anderson, R.E. (2009): Multivariate Data Analysis, 7th Ed., Prentice Hall
- James, G., Witten, D., Hastie, T. and Tibshirani, R., (2021): An introduction to statistical learning: with applications in R.
- Berinato, S. (2016). Good charts: The HBR guide to making smarter, more persuasive data visualizations. Harvard Business Review Press.
Bemerkungen
No offer in winter semester 2025/26.
Quelle: Modulbeschreibung der BTU · abgerufen 15.10.2026Original