Syllabus

Title
1304 Business Analytics I
Instructors
Univ.Prof. Dr. Axel Polleres, Sila Ada, M.S., ao.Univ.Prof. Dr. Alois Geyer, Dr. Stefan Treitl, Univ.Prof. Mag.Dr. Gerald Reiner, Dr.habil. Nadine Schröder
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
10/03/19 to 10/07/19
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Monday 10/14/19 08:00 AM - 11:00 AM TC.1.01 OeNB
Monday 10/21/19 08:00 AM - 11:00 AM TC.1.01 OeNB
Monday 10/28/19 08:00 AM - 11:00 AM TC.1.01 OeNB
Monday 11/04/19 08:00 AM - 11:00 AM TC.1.01 OeNB
Monday 11/11/19 08:00 AM - 11:00 AM TC.1.01 OeNB
Monday 12/02/19 08:00 AM - 11:00 AM TC.1.01 OeNB
Monday 12/09/19 08:00 AM - 11:00 AM TC.1.01 OeNB
Monday 12/16/19 08:00 AM - 09:30 AM TC.0.10 Audimax
Contents

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
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.

Assessment
  • 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 business-analytics-1@wu.ac.at.

For specific questions concerning certain contents, please contact the individual session-instructor directly via firstname.lastname@wu.ac.at

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

Last edited: 2019-10-02



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