1607 Business Analytics II
Univ.Prof. Dr. Verena Dorner, Dr. Nour Jnoub, B.Sc.
Contact details
Weekly hours
Language of instruction
09/26/22 to 10/27/22
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Day Date Time Room
Wednesday 11/02/22 06:00 PM - 09:00 PM LC.2.064 PC Raum
Wednesday 11/09/22 06:00 PM - 09:00 PM LC.2.064 PC Raum
Wednesday 11/16/22 06:00 PM - 09:00 PM LC.2.064 PC Raum
Wednesday 11/30/22 06:00 PM - 09:00 PM LC.2.064 PC Raum
Wednesday 12/07/22 06:00 PM - 09:00 PM LC.2.064 PC Raum
Wednesday 12/14/22 06:00 PM - 09:00 PM LC.2.064 PC Raum
Wednesday 12/21/22 06:00 PM - 09:00 PM Online-Einheit
Wednesday 01/11/23 06:00 PM - 09:00 PM LC.2.064 PC Raum

The course builds upon methods and tools introduced in Business Analytics 1. It deepens the skills of the students with a particular focus on combining Business Analytics and Data Management to problems within business scenarios. Beginning with basic data handling skills and progressing through statistical and operations research methods, students get to understand the complexity of business decisions and dependencies between different stages of forecasting and planning processes and how these can be supported by information systems and databases.  Students have to apply the tools to an integrated real-world case study covering all topics included in the course. In this context, identification of input factors and underlying assumptions as well as the correct interpretation of results are of high importance. Topics include:

1. Introduction to an overall business scenario

2. Basic data handling

3. Basic data processing

4. Data modeling

5. Querying, integrating, and aggregating data using SQL

6. Data visualization

7. Statistical analysis: Hypothesis tests, variance, and regression

8. Exploratory data analysis

9. Building (web-based) data applications

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 concrete business scenarios. This includes how to:

  • Handle big data files in R, Excel, and with SQL in a Relational Database Management System
  • Model data in an effective and reduncancy-free manner in the relational model
  • Use visualization tools to identify patterns and trends
  • Formulate and test hypothesis, and interpret their results in a business context
  • Query and integrate business relevant data and build aggregations of the data
  • Apply analysis of variance, regression analysis and exploratory data analysis, and interpret the results of such analyses to support data driven decision-making in a business context
  • Interpret the results at each stage and use them for the subsequent planning stages
  • Visualize results, in graphs and on a map in a Web-based application.
Attendance requirements

Attendance of at least 80% of the course units is mandatory.

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
  • In-class assignments, 30 points
  • Final exam, 40 points


Grading scale:

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%


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Last edited: 2022-10-03