Diese Seite auf DE
Modul
Data Science: Evidence-based Marketing [M-WIWI-101647]
Credits
9Recurrence
Jedes SemesterDuration
2 SemesterLanguage
GermanLevel
4Version
5Responsible
Organisation
- KIT-Fakultät für Wirtschaftswissenschaften
Part of
Bricks
Identifier | Name | LP |
---|---|---|
T-WIWI-107720 | Market Research | 4.5 |
T-WIWI-103139 | Marketing Analytics | 4.5 |
Competence Certificate
The assessment is carried out as partial exams (according to Section 4 (2), 1-3 SPO) of the courses of this module, whose sum of credits must meet the minimum requirement of credits of this module. The assessment procedures are described for each course of the module separately.
The overall grade of the module is the average of the grades for each course weighted by the credits and truncated after the first decimal.
Competence Goal
Students
- possess advanced knowledge of relevant market research contents
- know many different qualitative and quantitative methods for measuring customer behavior, preparation of strategic decisions, making causal deductions, usage of social media data and sales forecasting
- possess the statistical skills required for working in marketing research
Prerequisites
Keine.
Content
This module provides in-depth knowledge of relevant quantitative and qualitative methods used in market research.Students can attend the following courses:
- The course “Market Research” provides contents of practical relevance for measuring customer attitudes and customer behavior. The participants learn using statistical methods for strategic decision-making in marketing. Students who are interested in writing their master thesis at the Marketing & Sales Research Group are required to take this course.
- The course "Marketing Analytics“ is based on "Market Research“ and teaches advanced statistical methods for analyzing relevant marketing and market research questions. Please note that a successful completion of "Market Research" is a prerequisite for the completion of "Marketing Analytics".
Recommendation
None
Workload
The total workload for this module is approximately 270 hours.