1161 Business Analytics I
Univ.Prof. Dr. Axel Polleres, Sila Ada, M.S., ao.Univ.Prof. Dr. Alois Geyer, Univ.Prof. Tina Wakolbinger, Ph.D., Dr.habil. Nadine Schröder, Dr. Stefan Treitl
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
28.09.2020 bis 02.10.2020
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Wochentag Datum Uhrzeit Raum
Montag 05.10.2020 08:00 - 11:00 Online-Einheit
Montag 12.10.2020 08:00 - 11:00 Online-Einheit
Montag 19.10.2020 08:00 - 11:00 Online-Einheit
Montag 02.11.2020 08:00 - 11:00 Online-Einheit
Montag 16.11.2020 08:00 - 11:00 Online-Einheit
Montag 30.11.2020 08:00 - 11:00 Online-Einheit
Montag 14.12.2020 08:00 - 11:00 Online-Einheit
Montag 11.01.2021 13:30 - 15:00 Online-Einheit

Ablauf der LV bei eingeschränktem Campusbetrieb


For the classes in Business Analytics I the alternative scenario is a full distance mode. You will receive Lecture Casts for each session to prepare independently. During the announced time slots (see list of classes) remote sessions will take place via MS Teams. These sessions function as opportunities to ask questions and to solve exercises. Attendance is compulsory (via MS Teams), since also the In-Class Assignments will be held online within each session. The final exam will be conducted online, details follow duly.

Inhalte der LV

The course provides an introduction to methods and tools that support data-driven decision making in business. Beginning with basic data handling skills and progressing through statistical and operations research methods, students learn business analytics through real-world examples. A special emphasis will be put on visualization and interpretation of results.

Topics include:

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

Lernergebnisse (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

Regelung zur Anwesenheit

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


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

Leistung(en) für eine Beurteilung

  • 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%

Erreichbarkeit des/der Vortragenden

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

Zuletzt bearbeitet: 07.10.2020