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Data Science for Business Students

 

This course is about extracting useful knowledge from data. It covers the fundamental principles or concepts that underly data science. We are going to avoid an algorithm-centered approach whenever possible, instead focusing on the selection and application of techniques, and the interpretation of results. We will study data science in a business context, i.e., we will mostly work with examples, case studies and data that are relevant for business. Programming skills are not required. Machine learning skills are not required. 

You should have a general interest in modern analytical approaches and in studying data. We expect you to be willing to learn new concepts and techniques as they become relevant.

At the end of the course students will understand basic concepts and techniques of applied data science, such as:

  • Correlation and supervised segmentation
  • Fitting models to data
  • Overfitting
  • Similarity and clustering
  • Visualizations of model performance
  • Analysis of large amounts of text

 

Fundamental Literature: 

 

 Provost, F.; Fawcett, T. (2013): Data Science for Business. 1. Aufl.: O'Reilly Media Inc