Syllabus

Titel
5586 Data Processing 2: Scalable Data Processing, Legal & Ethical Foundations of Data Science
LV-Leiter/innen
Dr. Sabrina Kirrane
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
05.02.2019 bis 04.05.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Termine
Wochentag Datum Uhrzeit Raum
Freitag 10.05.2019 09:00 - 12:30 D5.1.004
Dienstag 14.05.2019 09:00 - 12:30 TC.1.02
Dienstag 21.05.2019 09:00 - 12:00 TC.4.03
Donnerstag 23.05.2019 09:30 - 13:00 TC.2.01
Dienstag 28.05.2019 09:30 - 13:00 TC.0.02 Red Bull
Donnerstag 13.06.2019 09:30 - 13:00 D3.0.225
Dienstag 18.06.2019 09:30 - 13:00 TC.3.03

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 course focuses on gaining fundamental knowledge in dealing with large amounts of data and learning about efficient and scalable processing methods. Throughout the course there will be an 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
    • Batch processing with Apache Spark
    • Stream processing with Apache Kafka
  • 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

Regelung zur Anwesenheit

at least 80% attendance

Lehr-/Lerndesign

The course will focus on in-class code walkthroughs of high-quality, well-commented code that students can later reference.
The course puts a particular emphasis on in-class discussion and project work.
 

Leistung(en) für eine Beurteilung

Repitition quizes: 25% 

In-class participation: 25%

Project: 50% (the project 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 the lecturers with the subject “[5586 - Data Processing 2]”.

Zuletzt bearbeitet: 27.02.2019



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