EESYS-ADAML-M: Applied Data Analytics and Machine Learning in R

Person responsible for module: Prof. Dr. Thorsten Staake

This course provides the theoretical foundation and conveys hands-on skills in the fields of data analytics
and machine learning using the statistics software GNU R. It uses real-word datasets from the realm of
energy efficiency and consumer behavior and conveys the subject matter through real-world examples
and practical challenges.
Following a refresher in descriptive statistic, the course covers
• an introduction to the statistics software GNU R,
• the design of field experiments and the use of Information Systems to collect behavioral data,
• techniques to formulate, solve, and interpret linear and logistic regression analyses,
• techniques to formulate, solve, and interpret clustering analyses,
• setting up, training, and evaluating machine learning algorithms, including KNN, regression, and
support vector machines, and
• ethical issues and data privacy regulations.

Learning outcomes:
After a successful participation in this course, participants can
• translate new business and research questions that can be answered using empirical methods into
suitable experimental designs,
• plan and conduct corresponding experiments,
• choose suitable methods from the set of methods presented in class to analyze the data,
• explain their design choices, the choice of methods, and the steps of the analyses,
• apply the methods correctly and efficiently using the statics software R,
• adjust the methods if needed to solve new and specific problems based on an understanding of the
necessary theories,
• interpret the outcome of such analyses and identify the strengths and limitations of the approaches,
• reflect upon data protection, privacy and ethical issues related to powerful techniques for data
acquisition and analytics.

Organizational details:

  • 6 ECTS / 180 h
  • Zulassungsvoraussetzung für die Belegung des Moduls: keine
  • Recommended prior knowledge:  This course requires a basic understanding of statistics (e.g., from a
    bachelor-level course). A statistics repetition and is part of the online
    material of the course and the of the first tutorials and should be
    complemented in self-study if necessary.
    Basic familiarity with a programming language.
  • Frequency: every winter semester
  • Mode of Delivery: Lectures and Tutorial - 4,00 SWS
  • Language: German/English
  • Examination: Written examination / Duration of Examination: 90 minutes