DE

Event

Introduction to Data Science [SS202511608]

Type
lecture (V)
Term
SS 2020
SWS
2
Language
Englisch
Appointments
14
Links
ILIAS

Lecturers

Organisation

  • Institut für Angewandte Informatik und Formale Beschreibungsverfahren

Part of

Literature

To be announced.

Appointments

  • 21.04.2020 14:00 - 15:30 - Room: 11.10 Kleiner Hörsaal Elektrotechnik
  • 28.04.2020 14:00 - 15:30 - Room: 11.10 Kleiner Hörsaal Elektrotechnik
  • 05.05.2020 14:00 - 15:30 - Room: 11.10 Kleiner Hörsaal Elektrotechnik
  • 12.05.2020 14:00 - 15:30 - Room: 11.10 Kleiner Hörsaal Elektrotechnik
  • 19.05.2020 14:00 - 15:30 - Room: 11.10 Kleiner Hörsaal Elektrotechnik
  • 26.05.2020 14:00 - 15:30 - Room: 11.10 Kleiner Hörsaal Elektrotechnik
  • 02.06.2020 14:00 - 15:30 - Room: 11.10 Kleiner Hörsaal Elektrotechnik
  • 09.06.2020 14:00 - 15:30 - Room: 11.10 Kleiner Hörsaal Elektrotechnik
  • 16.06.2020 14:00 - 15:30 - Room: 11.10 Kleiner Hörsaal Elektrotechnik
  • 23.06.2020 14:00 - 15:30 - Room: 11.10 Kleiner Hörsaal Elektrotechnik
  • 30.06.2020 14:00 - 15:30 - Room: 11.10 Kleiner Hörsaal Elektrotechnik
  • 07.07.2020 14:00 - 15:30 - Room: 11.10 Kleiner Hörsaal Elektrotechnik
  • 14.07.2020 14:00 - 15:30 - Room: 11.10 Kleiner Hörsaal Elektrotechnik
  • 21.07.2020 14:00 - 15:30 - Room: 11.10 Kleiner Hörsaal Elektrotechnik

Note

The main topic of this lecture is data science, i.e., methods to extract information from data with a scientific approach. We approach this topic from a practical side in this lecture. This means, that we concern ourselves directly with what algorithms do, and where they should be applied. The details of the algorithms and the theory behind them are not part of this lecture. Methods considered in this lecture include:

  • Association rule mining with the APRIORI approach
  • Clustering with k-means, EM for gaussian mixtures, DBSCAN, and single linkage clustering
  • Classification with k-nearest neighbor, decision trees, random forests, logistic regression, naive Bayes, support vector machines, and neural networks
  • Linear regression with ridge and lasso
  • Time series analysis with ARMA
  • Fundamentals of text mining

Additionally, we will consider the analysis of Big Data. In this context, we will consider the following topics:

  • The MapReduce paradigm
  • Apache Hadoop and Apache Spark