Syllabus

Title
2185 Data Processing 2: Scalable Data Processing, Legal & Ethical Foundations of Data Science
Instructors
Dr. Clemens Appl, LL.M., Dr. Jürgen Umbrich
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
11/28/16 to 01/05/17
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Monday 01/09/17 05:30 PM - 09:30 PM D2.0.342 Teacher Training Raum
Wednesday 01/11/17 02:00 PM - 06:15 PM TC.5.02
Tuesday 01/17/17 04:00 PM - 08:00 PM TC.5.14
Wednesday 01/18/17 02:00 PM - 06:15 PM TC.5.02
Monday 01/23/17 02:00 PM - 06:00 PM TC.3.10
Wednesday 01/25/17 02:00 PM - 06:15 PM TC.5.02
Friday 01/27/17 09:00 AM - 11:00 AM TC.4.12

Contents

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.

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

Teaching/learning method(s)

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.

Assessment

Repitition quizes: 25% 

In-class participation: 25%

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

Prerequisites for participation and waiting lists

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.

Availability of lecturer(s)

During the lecture and based on individual appointments 
To request an appointment send an email to backoffice@ai.wu.ac.at with the subject “[2185 - Data Processing 2]”
Last edited: 2016-10-03



Back