4208155: C15 Data Mining & Predictive Analytics

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Semester:WS 16/17
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.


  • Supervised learning techniques for regression (e.g. linear regression, SVM)
  • Supervised learning techniques for classification (e.g. logistic regression, KNN)
  • Unsupervised learning techniques (e.g. clustering, dimensionality reduction)
  • Text mining (e.g. sentiment analysis)
  • Hands-on labs with R

Learning objectives
  • 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.

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