Decision Sciences & Systems
Technical University of Munich

Prof. Dr. Martin Bichler, Per Paulsen M.A., Dr. Paul Karänke, Dr. Vladimir Fux

Lectures + Tutorials in WS 15/16

Business Analytics (IN2028)


  • Time and place:
    Lecture: Monday, 3.30-5.00 pm, Gustav-Niemann Hörsaal 2 (5510.EG.001)
  • Description:
    Hörerkreis und ECTS
  • Requirements:
    Fundamental of algorithms and datastructures, databases, and statistics (Module description)
  • Registration:
    To participate please register via TUMonline. Registration starts at
  • Tutorials:
    Seminar room MI 01.10.011
    Further information regarding tutorials is here
  • Exam:
    At the end of the course the 75-minute exam will take place.
    Exam details can be found here


This is an introductory course in data analysis with a focus on various models to understand and predict human (choice) behavior from large data sets. The participants will familiarize themselves with relevant methods from econometrics and data mining as they are widely used in marketing. They will learn various methods for classification, numerical prediction, and clustering, which focus on wide-spread problems in business practice. During tutorials, the instructors will show how to analyze data with the R language. The participants will be able to apply their knowledge during the Data Mining Cup (DMC), an exercise where they get to analyze realistic data sets. 

Relevant articles:


19.10. Introduction

26.10. Inferential Statistics Revisited

02.11. Multiple Linear Regression and Panel Data (Fixed and Random Effects)

09.11. Generalized Linear Models (Logit and Poisson Regression)

16.11. Naïve Bayes and Bayes Nets

23.11. Decision Tree Learners

30.11. Data Preparation

07.12. Evaluation of Classifiers

14.12. Introduction into the Data Mining Cup

21.12. Ensemble Methods

11.01. Guest Lecture

18.01. Clustering and PCA

25.01. Principal Component Regression, Association Rule Mining

01.02. Presentation Data Mining Cup

Final Exam


The presentation slides for the lectures and tutorials are accessible via MOODLE. The contents of the lectures can be found in chapters from the following textbooks:

  • Ian Witten, Eibe Frank: Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Morgan Kauffman, 2011 (E-Book)
  • Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirnai: An Introduction to Statistical Learning, Springer, 2014 (E-Book)
  • Jay Kearns: Introduction to Probability and Statistics using R, 2010 (E-Book)

Literature for further reading:

  • Tom Mitchell: Machine Learning, Mc-Graw Hill, 1997.
  • Margaret H. Dunham: Data Mining: Introductory and Advanced Topics, Prentice Hall, 2003.
  • J. H. Wilson, B. Keating: Business Forecasting, McGraw-Hill, 2002
  • Jiawei Han, Micheline Kamber: Data Mining: Concepts and Techniques, Morgan Kauffman, 2001.
  • David J. Hand, Heikki Mannila, Padhraic Smyth: Principles of Data Mining, MIT Press, 2001.
  • Michael Berthold, David Hand: Intelligent Data Analysis, An Introduction, 2te Auflage, Springer Verlag, 2003.


Prof. Dr. Martin Bichler 
Room 01.10.061 (Garching) 
Phone: 289-17534 
E-Mail: This email address is being protected from spambots. You need JavaScript enabled to view it. 
Sprechstunde nach Vereinbarung

Per Paulsen, M.A. 
Room 01.10.055
Phone: 289-17506
E-Mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Dr. Paul Karänke
Room 01.10.057
Phone: 289-17504
E-Mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Dr. Vladimir Fux
Room 01.10.058
Phone: 289-17528
E-Mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

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