Syllabus

Title
1856 Data Processing 2: Scalable Data Processing, Legal & Ethical Foundations of Data Science
Instructors
Astrid Krickl, MSc., Assist.Prof. PD Dr. Sabrina Kirrane
Contact details
sabrina.kirrane@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/02/21 to 11/24/21
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Specialization in Business Administration Course III - Data Science
Course III - Data Science
Dates
Day Date Time Room
Tuesday 11/30/21 09:00 AM - 12:30 PM Online-Einheit
Tuesday 12/07/21 02:00 PM - 05:30 PM Online-Einheit
Tuesday 12/14/21 02:00 PM - 05:30 PM Online-Einheit
Tuesday 12/21/21 09:00 AM - 12:30 PM Online-Einheit
Tuesday 01/11/22 02:00 PM - 05:30 PM Online-Einheit
Tuesday 01/18/22 02:00 PM - 05:30 PM Online-Einheit
Tuesday 01/25/22 09:00 AM - 12:30 PM Online-Einheit

On this page:

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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 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.

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Learning outcomes

Students in the course will learn about the scalable handling of big data, understanding 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
  • Ethical frameworks
  • Intellectual Property Rights / handling of different licensing schemes
  • Legal protection of personal data and typical privacy issues
  • Algorithmic bias
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Attendance requirements

According to the examination regulation full attendance is intended for a PI. Absence in one unit is tolerated if a proper reason is given.

If a student cannot attend a particular class, the student should send an email to the course instructor before the class starts, providing a legitimate justification for their absence.

All students are expected to attend the 1:1 project feedback sessions with the instructors.

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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 puts a particular emphasis on in-class discussion and project work.
 
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Assessment

Homework: 15% 

Project Proposal: 15%

Project: 70% (the project will mainly consist of adaptations and discussion of the practical examples presented in class)

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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.
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Availability of lecturer(s)

During the lecture and based on individual appointments. To request an appointment send an email to the lecturers with the subject “[Data Processing 2]”.

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Other

Due to the COVID-19 pandemic this course will be run via distance mode including performance assessment.

Last edited: 2021-07-29



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