0931 Business Analytics I
Univ.Prof. Dr. Axel Polleres, Univ.Prof. Tina Wakolbinger, Ph.D., Dr.habil. Nadine Schröder, ao.Univ.Prof. Dr. Alois Geyer, Dr. Stefan Treitl
Weekly hours
Language of instruction
09/26/22 to 10/02/22
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Day Date Time Room
Monday 10/10/22 08:00 AM - 11:00 AM Online-Einheit
Monday 10/17/22 08:00 AM - 11:00 AM TC.0.01 ERSTE
Monday 10/24/22 08:00 AM - 11:00 AM TC.0.01 ERSTE
Monday 11/07/22 08:00 AM - 11:00 AM TC.0.01 ERSTE
Monday 11/14/22 08:00 AM - 11:00 AM TC.0.01 ERSTE
Monday 11/28/22 08:00 AM - 11:00 AM TC.0.01 ERSTE
Monday 12/12/22 08:00 AM - 11:00 AM Online-Einheit
Monday 01/16/23 08:30 AM - 10:30 AM TC.0.10 Audimax

The course focuses on data-driven decision-making in business and provides an introduction to methods and tools for that purpose. Students learn business analytics in the context of a variety of real-world examples. Thereby, they acquire basic data handling skills and learn how to apply statistical and operations research methods. Special emphasis is put on visualization and interpretation of results.

Topics include:

  1. Basic Data Handling
  2. Basic Data Processing and Visualization
  3. Hypothesis Tests
  4. Regression Analysis
  5. Exploratory Factor Analysis
  6. Simulation
  7. Optimization
Learning outcomes

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

  • Handle big data files in R and Excel
  • 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
  • Develop and apply simulation models for decision support
  • Formulate and solve a certain class of decision problems as linear programs
Attendance requirements

Attendance requirement is met if a student is present for at least 80% of the lectures.

Teaching/learning method(s)

The course is taught using a combination of lectures, class discussions, homework exercises and in-class assignments.

  • Homework assignment, 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 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%
Availability of lecturer(s)

For general, administrative questions, please use

For specific questions concerning certain contents, please contact the individual session-instructor directly via

Please use "[Business Analytics 1] - ..." as subject to your Emails

Last edited: 2022-05-04