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.
Syllabus
Title
0898 Data Processing 2: Scalable Data Processing, Legal & Ethical Foundations of Data Science
Instructors
Ines Akaichi, M.A., Assoz.Prof PD Dr. Sabrina Kirrane
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/09/24 to 11/29/24
Registration via LPIS
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
| Day | Date | Time | Room |
|---|---|---|---|
| Tuesday | 12/03/24 | 12:00 PM - 04:00 PM | TC.1.01 OeNB |
| Tuesday | 12/10/24 | 09:00 AM - 12:30 PM | TC.-1.61 (P&S) |
| Tuesday | 12/17/24 | 09:00 AM - 12:30 PM | TC.-1.61 (P&S) |
| Tuesday | 01/07/25 | 10:00 AM - 02:00 PM | TC.3.01 |
| Tuesday | 01/14/25 | 09:00 AM - 12:30 PM | TC.-1.61 (P&S) |
| Tuesday | 01/21/25 | 09:00 AM - 12:30 PM | TC.-1.61 (P&S) |
| Friday | 01/24/25 | 09:00 AM - 01:00 PM | D4.0.022 |
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.
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
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.
Attendance is absolutely mandatory at the first course date (an unexcused absence will result in the loss of a place).
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:
- 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 8 - Lecture 3:
- The proposed Data Governance Act
- The proposed Artificial Intelligence Act
- The World Wide Web as a Distributed Data Source
- Data Science and Artificial Intelligence Trends
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.
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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Last edited: 2024-08-02
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