1422 Business Analytics II
Univ.Prof. Dr. Axel Polleres
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
03.10.2019 bis 07.10.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Wochentag Datum Uhrzeit Raum
Freitag 08.11.2019 09:00 - 12:00 TC.-1.61
Freitag 15.11.2019 09:00 - 12:00 LC.2.064 Raiffeisen Kurslabor
Freitag 29.11.2019 09:00 - 12:00 TC.-1.61
Freitag 06.12.2019 09:00 - 12:00 LC.2.064 Raiffeisen Kurslabor
Freitag 13.12.2019 09:00 - 12:00 LC.2.064 Raiffeisen Kurslabor
Freitag 20.12.2019 09:00 - 12:00 LC.2.064 Raiffeisen Kurslabor
Freitag 10.01.2020 09:00 - 12:00 LC.2.064 Raiffeisen Kurslabor
Freitag 17.01.2020 08:00 - 12:30 TC.-1.61

Inhalte der LV

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 a 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 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



Lernergebnisse (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 Systems
  •      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.

Regelung zur Anwesenheit

The attendance of at least 80% of the course units is a mandatory criterion.


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.

Leistung(en) für eine Beurteilung

•    Homework exercises, 30 points (6 homeworks)

•    In-class assignments, 30 points

•    Final exam, 40 points


In order to pass the class, you need attend at least 80 % of all classes. If you fulfill this constraint, 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%

Zuletzt bearbeitet: 03.11.2019