Syllabus

Title
1143 Business Analytics II
Instructors
Martin Hrusovsky, Ph.D.
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
09/27/21 to 10/03/21
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Wednesday 10/20/21 03:00 PM - 06:00 PM LC.2.064 Raiffeisen PC Raum
Wednesday 10/27/21 03:00 PM - 06:00 PM LC.2.064 Raiffeisen PC Raum
Wednesday 11/03/21 03:00 PM - 06:00 PM LC.2.064 Raiffeisen PC Raum
Wednesday 11/17/21 03:00 PM - 06:00 PM LC.2.064 Raiffeisen PC Raum
Wednesday 12/01/21 03:00 PM - 06:00 PM Online-Einheit
Wednesday 12/15/21 03:00 PM - 06:00 PM Online-Einheit
Wednesday 01/12/22 03:00 PM - 06:00 PM LC.2.064 Raiffeisen PC Raum
Wednesday 01/19/22 03:00 PM - 06:00 PM LC.2.064 Raiffeisen PC Raum

Contents

The course builds upon methods and tools introduced in Business Analytics 1. It deepens the skills of the students by applying the tools to problems within the supply chain management area. 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.  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 Supply Chain Management

2. Basic Data Handling

3. Basic Data Processing

4. Data visualization

5. Hypothesis Tests

6. Analysis of Variance and Regression

7. Forecasting

8. Optimization

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 the supply chain management area. 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 and regression analysis, and interpret the results of such analyses to support data driven decision-making in a business context
  •        Forecast based on historical data
  •        Formulate and solve decision problems in transport and production context as linear programs
  •        Interpret the results at each stage and use them for the subsequent planning stages
  •        Visualize results on a map
 

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, in-class assignments and practical application of the tools and methods to an integrated case study and other examples.

Assessment

  •       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 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%
Last edited: 2021-05-25



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