After successful completion of the course, students are able to discuss legal and ethical aspects of the use of data, formulate a problem for solving it using Data Science approaches, design rigorous Data Science experiments, and interpret the results of complex data analyses.

The following topics are covered in the lectures:

• Introduction to Data Science
• Data and the data lifecycle
• Conceptual Experiment design
• Workflow paradigms
• Data management, reproducibilty and traceability
• Experiment error analysis and statistical testing
• Advanced experiment design

In addition, two exercises will be done.

The effort breakdown is:

9 2-hour lectures: 18h
Exercise 1: 10h
Exercise 2 (incl presentation): 30h
Exam preparation: 16h
Exam: 1h
SUM: 75h

Lectures, exercises

05.10.2023 14:15-15:45 – Hanbury: Introduction to Course and Data Science
12.10.2023 14:15-15:45 – Hanbury: Introduction to Ethical and Legal Aspects
19.10.2023 14:15-15:45 – Knees: Data Science Experiments
09.11.2023 14:15-15:45 – Knees: Experimental Designs I
16.11.2023 14:15-15:45 – Schindler: Hands-on Data Science Workflow
23.11.2023 14:15-15:45 – (Backup date - might not be needed)
30.11.2023 14:15-15:45 – Knees: Experimental Designs II
07.12.2023 14:15-15:45 – Knees: Experimental Designs III
14.12.2023 14:15-15:45 – Rauber: Reproducibility
21.12.2023 14:15-15:45 – Rauber: Data Management

11.01.2024 14:15-15:45 Project Presentations
18.01.2024 14:15-15:45 Project Presentations
25.01.2024 14:15-15:45 Project Presentations
25.01.2024 18:00-20:00 Written Exam

March 2024 Exam repeat 1
May 2024 Exam repeat 2

2 Exercises, Exam