337094a Marktforschung
| Zuletzt geändert: | 11.12.2024 / Sorace |
| EDV-Nr: | 337094a |
| Studiengänge: |
Online-Medien-Management (Bachelor, 7 Semester) , Prüfungsleistung im Modul Marktforschung
in Semester
3
Häufigkeit: immer |
| Dozent: | Prof. Dr. Jan Kirenz |
| Sprache: | Vorlesung deutsch, Materialien englisch |
| Art: | - |
| Umfang: | 2 SWS |
| ECTS-Punkte: | 3 |
| Workload: |
3 ECTS = 90 hours;
Written exam |
| Prüfungsform: | |
| Bemerkung zur Veranstaltung: | Gem. Senatssitzung v. 15.10.21 ab WS 21/22: Änd.d.Prüfungsform in KL, 90 Min. |
| Beschreibung: |
This course offers a comprehensive overview of data-driven decision-making techniques for business related topics. Beginning with the Business Model Canvas, it highlights the importance of data in competitive analysis. Learners will explore study design, including data collection methods and a practical guide on conducting experiments and A/B-tests, essential for strategic decision-making. The curriculum advances through data preparation and analysis, emphasizing competitive market analysis and the transformation of raw data into actionable insights. The course delves into analytical models, covering classification, regression, and cluster analysis for market trend prediction. Participants will also master the usage of Generative AI tools, learning to optimize their work outcomes through effective AI utilization.
By the end of this module, students are able to: - Effectively plan and execute market research projects - Conduct experiments and A/B tests - Use predictive models and clustering methods - Utilize modern tools and technologies for data analysis - Clearly and effectively communicate results |
| English Title: | Market research |
| English Abstract: | This course offers a comprehensive overview of data-driven decision-making techniques for business related topics. Beginning with the Business Model Canvas, it highlights the importance of data in competitive analysis. Learners will explore study design, including data collection methods and a practical guide on conducting experiments and A/B-tests, essential for strategic decision-making. The curriculum advances through data preparation and analysis, emphasizing competitive market analysis and the transformation of raw data into actionable insights. The course delves into analytical models, covering classification, regression, and cluster analysis for market trend prediction. Participants will also master the usage of Generative AI tools, learning to optimize their work outcomes through effective AI utilization. |
| Literatur: |
Çetinkaya-Rundel, M. & Hardin, J (2023). Introduction to Modern Statistics. OpenIntro. Inc. Harvard Business Review (2018). HBR Guide to Data Analytics Basics for Managers. Harvard Business Review Press. James, G., Witten, D., Hastie, T., Tibshirani, R. & Taylor, J. (2023). An Introduction to Statistical Learning with Python. New York: Springer. Lau, S., Gonzalez, J. & Nolan, D. (2023). Learning Data Science. O'Reilly Media, Inc. Weitere Literatur finden Sie in der HdM-Bibliothek. |