337094b Statistik
| Zuletzt geändert: | 02.03.2024 / Kirenz |
| EDV-Nr: | 337094b |
| 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: | 2 |
| Workload: |
2 ECTS = 60 hours Examination: 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 provides an in-depth look at both basic and advanced statistical methods, aimed at fostering a deep understanding of data analysis and statistical inference. Using Python and Excel, the curriculum focuses on equipping students with the skills to effectively analyze, interpret, and present data. The content spans from data fundamentals, through exploratory data analysis of both categorical and numerical data, to more complex topics such as linear regression, logistic regression, and the foundations of statistical inference. The course culminates in applying these concepts to hypothesis testing and real-world data analysis. By the end, students will be adept at using statistical techniques and ready for careers or further study requiring data analysis proficiency.
By the end of this module, students are able to: - Understand statistical terms and concepts - Recognize patterns in data - Create effective charts and visualizations - Develop predictive models - Clearly and effectively communicate statistical results - Utilize modern tools and technologies for data analysis |
| English Title: | Statistics |
| English Abstract: | This course provides an in-depth look at both basic and advanced statistical methods, aimed at fostering a deep understanding of data analysis and statistical inference. Using Python and Excel, the curriculum focuses on equipping students with the skills to effectively analyze, interpret, and present data. The content spans from data fundamentals, through exploratory data analysis of both categorical and numerical data, to more complex topics such as linear regression, logistic regression, and the foundations of statistical inference. The course culminates in applying these concepts to hypothesis testing and real-world data analysis. By the end, students will be adept at using statistical techniques and ready for careers or further study requiring data analysis proficiency. |
| Literatur: |
Çetinkaya-Rundel, M. & Hardin, J (2023). Introduction to Modern Statistics. OpenIntro. Inc.
Weitere Literatur finden Sie in der HdM-Bibliothek. |