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Module Number:
| 14037
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| Module Title: | Quantitative Data Analysis and Visualization for Business Environments |
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Quantitative Datenanalyse und Visualisierung im betriebswirtschaftlichen Kontext
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| Department: |
Faculty 5 - Business, Law and Social Sciences
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| Responsible Staff Member: | -
Prof. Dr. rer. pol. Dost, Florian
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| Language of Teaching / Examination: | English |
| Duration: | 1 semester |
| Frequency of Offer: |
Every winter semester
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| Credits: |
6
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| 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
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| Mandatory Prerequisites: | None |
| Forms of Teaching and Proportion: | -
Lecture
/ 2 Hours per Week per Semester
-
Exercise
/ 2 Hours per Week per Semester
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Self organised studies
/ 120 Hours
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| 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.
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| 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%)
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| Evaluation of Module Examination: | Performance Verification – graded |
| Limited Number of Participants: | None |
| Part of the Study Programme: | -
Master (research-oriented) /
Angewandte Mathematik /
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Angewandte Mathematik /
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PO 2018
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Master (applied) - Extended Semester /
Maschinenbau /
PO 2018
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Master (research-oriented) /
Maschinenbau /
PO 2006
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Maschinenbau /
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Master (research-oriented) - Reduced Semester /
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Wirtschaftsmathematik /
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Wirtschaftsmathematik - dual /
PO 2023
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| | This module has been approved for the general studies. |
| Remarks: | None |
| Module Components: | - Quantitative Daten Analysis and Visualization (Lecture)
- Quantitative Daten Analysis and Visualization (Exercise)
|
| Components to be offered in the Current Semester: | |
