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

Dates (all online via Zoom, Thu, 14:15-15:45)

First Meeting / Introduction to data science: Thu, Oct 14, 14:15-15:45

Zoom Meeting: https://tuwien.zoom.us/j/93329349139?pwd=ZHFaY2d5WG9rZDBkc3NFbmx0Skp2QT09

Meeting ID: 933 2934 9139, Password: 4cZkN8Kc

BLOCK Data Science

• Introduction to data science - data science process -Hanbury
• Data and the data lifecycle, ethical and legal aspects -Hanbury

BLOCK Conceptual Experiment Design

• Planning and Execution of Experiments, hypotheses, Data collection -Knees
• ML basics, Evaluation -Knees
• Workflow paradigms and environments  -Schindler, Knees
• Experiment Error Analysis and Statistical Testing 1 -Knees
• Experiment Error Analysis and Statistical Testing 2 -Knees

• Exercise 1 (individual): Design an experimental workflow for a given dataset 

BLOCK Reproducibility and traceability

• Reproducibility and traceability 1 -Rauber
• Reproducibility and traceability 2 -Rauber

• Exercise 2 (in groups): Reproduce experimental results from a paper

January 2022 Intermediate Group Presentations of Exercise 2

January 2022 Written Exam (online)

March 2022 Exam repeat

2 Exercises, Exam