| Dozent/in |
Prof. Dr. Leif Brandes |
| Veranstaltungsart |
Vorlesung |
| Code |
HS251678 |
| Semester |
Herbstsemester 2025 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Di, 16.09.2025, 10:15 - 12:00 Uhr, 4.B47
Di, 23.09.2025, 10:15 - 12:00 Uhr, E.508
Di, 30.09.2025, 10:15 - 12:00 Uhr, E.508
Di, 14.10.2025, 11:00 - 13:00 Uhr, E.508
Di, 21.10.2025, 10:15 - 12:00 Uhr, 4.B47
Di, 28.10.2025, 10:15 - 12:00 Uhr, 4.B47
Di, 04.11.2025, 10:15 - 12:00 Uhr, 4.B47
Di, 11.11.2025, 10:15 - 12:00 Uhr, 4.B47
Di, 18.11.2025, 10:15 - 12:00 Uhr, 4.B55
Di, 25.11.2025, 10:15 - 12:00 Uhr, 4.B47
Di, 02.12.2025, 10:15 - 12:00 Uhr, 4.B47
Di, 09.12.2025, 10:15 - 12:00 Uhr, 4.B47
Di, 16.12.2025, 10:15 - 12:00 Uhr, 4.B47 |
| Umfang |
2 Semesterwochenstunden |
| Turnus |
weekly
|
| Inhalt |
Firms have nowadays more data about markets and consumers at their hands than ever before. At the same time, many firms are realizing that data access is not sufficient to derive key insights from data. For the latter, it is important that firms ask the right questions to the data (which is often harder than it might seem) and then use the appropriate methods to analyze the data. In this course, we cover a range of methods and applications in marketing analytics. Particular emphasis is placed on the implementation of these methods using state-of-the-art methods in Python. By the end of the course, students will have learned how to implement a broad range of relevant applications and methods, including customer segmentation, attribution modeling, customer churn prediction, deriving market positioning insights from user-generated content, analyzing customer reviews and images, demand forecasting, and others. |
| Lernziele |
Upon successful completion of this course, students will have learned to approach marketing analytics problems in a structured manner. In particular, students will be able to split problems into key steps, including data collection, data preprocessing, data analysis, and communicating the associated results. Students will learn how to implement these steps in Python using state-of-the-art methodologies. At the end of this course, students will have built an extensive library of Python scripts that enables them to address a broad range of marketing analytics problems (see the course content for examples) in practice. |
| Voraussetzungen |
Only for master students who have completed ‘Advanced Marketing Management’, or who are taking ‘Advanced Marketing Management’ in the HS25 term. No prior programming experience with Python is required. However, students are expected to be familiar with R (which will ease the transition). |
| Sprache |
Englisch |
| Anmeldung |
To attend the course / exercise, registration via e-learning platform OLAT is required. Registration is possible from 1 – 26 September 2025. The students themselves are responsible for checking the creditability of the course to their course of study.
Direct link to OLAT course: to follow
|
| Prüfung |
***IMPORTANT*** In order to acquire credits, resp. to take part in the examination, registration via the UniPortal within the examination registration period is REQUIRED. Further information on registration for the examination: www.unilu.ch/wf/pruefungen |
| Abschlussform / Credits |
Written exam / 4.5 Credits
|
| Hörer-/innen |
Nein |
| Kontakt |
leif.brandes@unilu.ch
|
| Anzahl Anmeldungen |
0 von maximal 25 |
| Literatur |
Mandatory literature:
This course will primarily draw on Yildirim & Kübler (2025). Applied Marketing Analytics Using Python, SAGE. A digital copy of the book can be rented for 180 days for the price of about 30 CHF. |