Business Analytics

Lecturer:

Prof. Dr. Claudius Steinhardt

Workload:

150 hours; Contact hours: 36h; Self-study: 114 hours

ECTS:

5 ECTS

Module no. (Course no.):

3759 (37591 + 37592)

Content: 

  • Introduction
  • Data preprocessing
  • Exploratory data analysis
  • Preparing to model the data
  • Decision trees
  • Model evaluation techniques
  • Neural networks

Learning outcomes: 
Upon successful completion of this module, students will be able to:

  • Demonstrate a comprehensive understanding of key Business Analytics concepts and
    techniques.
  • Critically evaluate and compare various Data Mining methods based on problem context,
    strengths, and limitations.
  • Apply theoretical knowledge using industry-standard software or coding for solving analytical
    tasks efficiently and reflectively.
  • Translate analytical results into actionable business recommendations.

Assessment:

  • Written examination (mandatory): A 60-minutes written examination is mandatory
  • Project (not mandatory): Students may participate in a group project throughout the course. A bonus can be earned with which the grade of the written examination can be improved

Course materials: 

  • Larose, D., Larose, C.: "Discovering Knowledge in Data: An Introduction to Data Mining", Wiley (current edition).
  • Shmueli, G., Bruce, P., Patel, N.: "Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner", Wiley (current edition). 
  • Software: IBM SPSS Modeler, Python