Syllabus

Title
4962 Data Processing 2: Scalable Data Processing, Legal & Ethical Foundations of Data Science
Instructors
Ines Akaichi, M.A., Assoz.Prof PD Dr. Sabrina Kirrane
Contact details
Sabrina.Kirrane@wu.ac.at, Ines.Akaichi@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
02/26/24 to 04/29/24
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Specialization in Business Administration Course III - Data Science
Course III - Data Science
Course III - Data Science
Dates
Day Date Time Room
Tuesday 05/07/24 09:00 AM - 01:00 PM TC.5.03
Wednesday 05/15/24 09:00 AM - 12:30 PM TC.-1.61 (P&S)
Wednesday 05/22/24 09:00 AM - 12:30 PM TC.-1.61 (P&S)
Tuesday 06/04/24 09:00 AM - 01:00 PM D4.0.022
Tuesday 06/11/24 09:00 AM - 12:30 PM TC.-1.61 (P&S)
Tuesday 06/18/24 09:00 AM - 12:30 PM TC.-1.61 (P&S)
Tuesday 06/25/24 09:00 AM - 01:00 PM TC.5.03

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
  • Legal and Ethical frameworks
    • Codes of Conduct
    • Intellectual Property Rights / handling of different licensing schemes
    • Relevant European Regulations
    • 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.

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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.
 

Week 1 - Lecture 1:

  • Introduction to Scalable Data Processing and Legal & Ethical Foundations of Data Science
  • Horizontal & Vertical Scalability
  • The Big Data Ecosystem
  • Ethics from & Data Science
  • Intellectual Property

Week 2 - Lab 1:

  • Deep Dive into Apache Spark
  • Movie Lens code walk through and exercise
  • Friends code walk through and exercise

Week 3 - Lab 2:

  • Deep dive into Machine Learning using Apache Spark
  • California Housing Linear Regression code walk through
  • Deep dive into natural language processing using Apache Spark
  • Classification of Data Science Tweets code walk through

Week 4 - Christmas Break

Week 5 - Lecture 2:

  • Anonymisation & Aggregation
  • Consent, Transparency & Compliance
  • Synthesized Data
  • Bias & Algorithmic Fairness
  • Big Data in Practice

Week 6 - Lab 3:

  • Deep dive into Apache Kafka & Stream Processing
  • Dealing with real world data from Reddit using Kafka and Python
  • Deep dive into Sentiment Analysis
  • Working with TextBlob, Kafka, and Spark to analyze real-time streaming data

Week 7 - Lab 4:

  • Project consultation sessions

Week 8 - Lecture 3:

  • The proposed Data Governance Act
  • The proposed Artificial Intelligence Act
  • The World Wide Web as a Distributed Data Source
  • Industry Trends
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Assessment

Homework and class participation: 15% 

Project Proposal: 15%

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

 

Grading Scheme:

90−100 Sehr gut (Really good) is the best possible grade and indicates outstanding performance with no or only minor errors.

80−89 Gut (Good) is the next-highest grade and is given for performance that is above-average standard but with some errors.

64−79 Befriedigend (Satisfactory) indicates generally sound work with a number of notable errors.

51−63 Genügend (Sufficient) is the lowest passing grade and is given if the standard has been met but with a significant number of shortcomings.

0−50 Nicht genügend (Insufficient) is the lowest possible grade and the only failing grade.

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Prerequisites for participation and waiting lists

Students need to register for course 1 of SBWL Data Science before registering for this course.

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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Readings

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

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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]”.

Last edited: 2024-03-10



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