14037 - Quantitative Data Analysis and Visualization for Business Environments Modulübersicht

Module Number: 14037
Module Title:Quantitative Data Analysis and Visualization for Business Environments
  Quantitative Datenanalyse und Visualisierung im betriebswirtschaftlichen Kontext
Department: Faculty 5 - Business, Law and Social Sciences
Responsible Staff Member:
  • Prof. Dr. rer. pol. Dost, Florian
Language of Teaching / Examination:English
Duration:1 semester
Frequency of Offer: Every winter semester
Credits: 6
Learning Outcome: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.)
Contents: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.
Recommended Prerequisites:Knowledge of the content of modules
  • 13714 Research Methods in Business Administration and Economics 
  • 38402 Marktforschung 
  • 38427 Forschungsmethoden der Betriebswirtschaftslehre
Mandatory Prerequisites:None
Forms of Teaching and Proportion:
  • Lecture / 2 Hours per Week per Semester
  • Exercise / 2 Hours per Week per Semester
  • Self organised studies / 120 Hours
Teaching Materials and Literature:
  • 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.
Module Examination:Continuous Assessment (MCA)
Assessment Mode for Module Examination:
  • 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%)
Evaluation of Module Examination:Performance Verification – graded
Limited Number of Participants:None
Part of the Study Programme:
  • Master (research-oriented) / Angewandte Mathematik / PO 2019 - 1. SÄ 2021
  • Abschluss im Ausland / Betriebswirtschaftslehre / keine PO
  • Master (research-oriented) / Betriebswirtschaftslehre / PO 2017
  • Bachelor (research-oriented) / Künstliche Intelligenz / PO 2022
  • Master (research-oriented) / Mathematics / PO 2025
  • Master (research-oriented) / Wirtschaftsingenieurwesen / PO 2019
  • Master (research-oriented) / Wirtschaftsingenieurwesen / PO 2025
  • Master (research-oriented) - Reduced Semester / Wirtschaftsingenieurwesen / PO 2025
  • Master (research-oriented) - Reduced Semester / Wirtschaftsingenieurwesen - dual / PO 2025
  • Master (research-oriented) - Co-Op Programme with Practical Place / Wirtschaftsingenieurwesen - dual / PO 2025
 This module has been approved for the general studies.
Remarks:No offer in winter semester 2025/26.
Module Components:
  • Quantitative Daten Analysis and Visualization (Lecture)
  • Quantitative Daten Analysis and Visualization (Exercise)
Components to be offered in the Current Semester:
  • no assignment