Veranstaltungsbeschreibung

337094a Marktforschung

Zuletzt geändert:	07.04.2024 / Kirenz
EDV-Nr:	337094a
Studiengänge:	Online-Medien-Management (Bachelor, 7 Semester) , Prüfungsleistung im Modul Marktforschung in Semester 3 Häufigkeit: immer Social Media Marketing & Management, (Bachelor, 7 Semester, Zulassung ab Wintersemester 2024/2025), Prüfungsleistung im Modul Marktforschung in Semester Häufigkeit: unregelmäßig
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.