Data Science I

The course provides a full introduction to data science with an emphasis on hands-on examples. Students will acquire relevant knowledge of the whole data science chain: From storage/acquisition to statistical inference, machine learning models to visualization. It also serves as an introductory course to the Data Science project seminar.


The tutorials will be hands-on sessions, please get Jupyter notebook ready to run (e.g. by Anaconda or Google Colab) before the first session begins.

Course Overview

Prerequisites -
Language English
Schedule Lecture: Fridays 11:40 – 13:20 (weekly)
Tutorial: Tuesdays 09:50 – 11:30 (weekly)
Format Lecture and Tutorial (L2+T2), 5CP
Material Provided through Moodle (
FB18 Elektrotechnik und Informationstechnik – Data Science I 18-zo-2110-ue
Office Hours Dr.-Ing. Christian Debes (by email appointment)
Pertami Kunz (by email appointment)
Literature Lecture slides and tutorials from Moodle

  • Wes McKinney: Python for Data Analysis, O’Reilly, 2017
  • Christopher M. Bishop: Pattern Recognition and Machine Learning, 2011
  • James, Witten, Hastie and Tibshirani, Introduction to Statistical Learning, Springer, 2017