1796 Business Analytics II
Dr.habil. Nadine Schröder
Contact details
  • Type
  • Weekly hours
  • Language of instruction
09/27/21 to 10/03/21
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Day Date Time Room
Friday 10/22/21 08:00 AM - 11:00 AM LC.2.064 Raiffeisen PC Raum
Friday 10/29/21 08:00 AM - 11:00 AM LC.2.064 Raiffeisen PC Raum
Friday 11/05/21 08:00 AM - 11:00 AM LC.2.064 Raiffeisen PC Raum
Friday 11/19/21 08:00 AM - 11:00 AM LC.2.064 Raiffeisen PC Raum
Friday 12/03/21 08:00 AM - 11:00 AM Online-Einheit
Friday 12/17/21 08:00 AM - 11:00 AM Online-Einheit
Friday 01/14/22 08:00 AM - 11:00 AM Online-Einheit
Friday 01/21/22 09:30 AM - 11:00 AM Online-Einheit


The course builds upon methods and tools introduced in Business Analytics 1. It deepens the skills of the students by applying the tools to problems within the Marketing area. Beginning with basic data handling skills and progressing through statistical  methods, students get to understand the complexity of business decisions and dependencies between different stages of forecasting and planning processes. Students have to apply the tools to integrated real-world cases covering all topics included in the course. In this context, identification of input factors and underlying assumptions as well as correct interpretation of results are of high importance. Topics include:

1.    Introduction to Marketing and Marketing Research
2.    Basic Data Handling
3.    Basic Data Processing
4.    Data visualization
5.    Hypothesis Tests
6.    Analysis of Variance
7.    Regression Models
8.    Exploratory Factor Analysis

Learning outcomes

After attending this course, students will be able to understand and apply the principles, methods and tools of business analytics to problems in the Marketing area. This includes how to:

•    Handle data files in R
•    Use visualization tools to identify patterns and trends
•    Formulate and test hypothesis, and interpret their results in a business context
•    Apply analysis of variance, regression analysis and exploratory factor analysis, and interpret the results of such analyses to support data driven decision-making in a business context
•    Forecast based on historical data
•    Interpret the results at each stage and use them for the subsequent planning stages
•    Visualize results on a map

Attendance requirements

 Attendance requirement is met if a student is present for at least 80% of the lectures. The lecture on Oct 29, 2021 has been switched to an online lecture because the lecturer has been categorized as "K2". The students have been informed via email

Teaching/learning method(s)

The course is taught using a combination of lectures, class discussions, in-class assignments and practical application of the tools and methods to an integrated case study and other examples.


•    Homework exercises, 30 points (6 homeworks)
•    In-class assignments, 30 points 
•    Final exam, 40 points

In order to pass the class, you need to attend at least 80 % of all classes. If you fulfill these criteria, the following grading scale will be applied:
•    Excellent (1): 87.5% - 100.0%
•    Good (2): 75.0% - <87.5%
•    Satisfactory (3): 62.5% - <75.0%
•    Sufficient (4): 50.0% - <62.5%
•    Fail (5): <50.0%

Last edited: 2021-10-27