2185 Data Processing 2: Scalable Data Processing, Legal & Ethical Foundations of Data Science
Dr. Clemens Appl, LL.M., Dr. Jürgen Umbrich
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
28.11.2016 bis 05.01.2017
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Wochentag Datum Uhrzeit Raum
Montag 09.01.2017 17:30 - 21:30 D2.0.342 Teacher Training Raum
Mittwoch 11.01.2017 14:00 - 18:15 TC.5.02
Dienstag 17.01.2017 16:00 - 20:00 TC.5.14
Mittwoch 18.01.2017 14:00 - 18:15 TC.5.02
Montag 23.01.2017 14:00 - 18:00 TC.3.10
Mittwoch 25.01.2017 14:00 - 18:15 TC.5.02
Freitag 27.01.2017 09:00 - 11:00 TC.4.12

Inhalte der LV

This fast-paced class is intended for students interested in scalable handling of big data, understanding legal fundamentals and ethical frameworks in dealing with data in an international context.
The first part of the course focuses on gaining fundamental knowledge in dealing with large amounts of data and learning about efficient and scalable processing methods.The second part of the course will put emphasis on important aspects regarding legal and ethical principals related to data processing and data science.

Lernergebnisse (Learning Outcomes)

Students in the course will learn about the scalable handling of big data, unterstanding legal fundamentals and ethical frameworks in dealing with data in an international context.

This includes:

  • Basic knowledge about different scalable data processing frameworks and paradigms, including:
    • The Hadoop ecosystem
    • Map Reduce
    • Stream processing
    • Understanding of systems such as Google’s Pregel or BigTable, Signal/Collect and others 
  • The difference between public data vs. open data
  • Copyright protection of databases / Handling of different licensing schemes
  • Legal protection of personal Data and typical privacy issues
  • Ethical frameworks


The course will focus on in-class code walkthroughs of high-quality, well-commented code that students can later reference.
The course will balance project work and small homework assignments.

Leistung(en) für eine Beurteilung

Repitition quizes: 25% 

In-class participation: 25%

Homeworks: 50% (homeworks will mainly consist of adaptations and discussion of the practical examples presented in class)

Teilnahmevoraussetzung(en) und Vergabe von Wartelistenplätzen

Successful conclusion of the course 1 of SBWL Data Science.

Please be aware that for all courses in this SBWL registration is only possibly for students who successfully have completed the entry course (Einstieg in die SBWL: Data Science).

Note that for courses within the SBWL "Data Science" we can only accept students enrolled in one of WU's bachelor programmes who qualify for starting an SBWL; particularly, we cannot accept students from other courses and programmes enrolled at WU as 'Mitbeleger' only.

Erreichbarkeit des/der Vortragenden

During the lecture and based on individual appointments 
To request an appointment send an email to with the subject “[2185 - Data Processing 2]”
Zuletzt bearbeitet: 03.10.2016