4808155: C15 Data Mining & Predictive Analytics

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Semester:WS 19/20
Lektionen / Semester:52.0 L / 39.0 h
Selbststudium:141.0 h



Masterstudium Information Systems (01.09.2015)


Short description
The course covers various statistical techniques for making sense of the vast and complex data sets that have emerged in business in the past twenty years. Students will learn to detect patterns in large data sets of various formats (quantitative and qualitative) and translate them into actionable insights.


  • Data Visualization and Exploration
  • Supervised learning techniques for regression (e.g. linear regression)
  • Supervised learning techniques for classification (e.g. classification trees)
  • Unsupervised learning techniques (e.g. clustering, dimensionality reduction)
  • Deep Learning Fundamentals
  • Text mining (e.g. topic modeling)
  • Hands-on labs with Python


  • Students will know and understand the basic concepts and methods of data mining and predictive Analytics
  • Students will assess the assumptions and quality of statistical models
  • Students will select and apply the right statistical models for a given task or data set
  • Students will derive actionable insights from statistical results



The module integrates theoretical knowledge and practical skills in an interactive lecture. The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Voraussetzungen (inhaltlich)

Recommended previous knowledge

  • Module “Business Statistics I”
  • Module “Business Statistics II”
  • Basic knowledge of statistical software R - online course available: tryr.codeschool.com


Compulsory reading

  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. With Applications in R. New York: Springer (a free online version is available at http://www-bcf.usc.edu/~gareth/ISL/)

Further reading
  • Provost, F. & Fawcett, T. (2013). Data Science for Business. Sebastopol: O'Reilly Media


Written exam (90min)