Data Science and Big Data Analytics (WS2017)

Teaching Staff: Steffen Herbold

Dates, Modules, etc.

  • Lecture: Tuesdays, 14:15-15:45 o'clock, Provisorischer Hörsaal B (Chemie)
  • Exercise: Thursdays, 13:15-14:45 o'clock, Room -1.101 (Informatik) - Date of first session will be announced via StudIP. The exericise will not start before November. Details will be given in the first lecture. 
  • Module: M.Inf.1151 (Göttingen), 2.17 Data Warehousing and Data Mining Techniques (ITIS)
  • The lecture will NOT be available via Webstream, due to the room change!


This lecture requires registration. The registration procedure will be explained during the first lecture. The maximum number of participants is 50. Registration and active participation in the exercise is mandatory in order to be allowed to participate in the final exam.


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:

  • k-means clustering
  • Linear regression
  • Logistic regression
  • Naive bayes
  • Decision trees
  • Text analysis

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

  • MapReduce
  • Hadoop
  • Languages for Hadoop
  • Mahout


The materials for this course are distributed via Stud.IP.

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