Presentation 1 Room: Seminar Room "Zwenkauer See" - A.03.07 Welcome, course overview, the data science workflow, short introduction to data wrangling and data analytics / mining, overview of data quality aspects, examples and case studies of what can go wrong in data science.

Presentation 2 Room: Seminar Room "Zwenkauer See" - A.03.07 Data quality dimensions, data quality assessments, data quality frameworks; how data capturing, data processing, and data linkage can affect data quality.

Practical Session 1 Room: Seminar Room "Zwenkauer See" - A.03.07 Group discussions on participant’s own experiences of data quality issues encountered (30 min), short presentations by groups of the most serious / challenging data quality issues they identified in their group (30 min).

Presentation 3 Room: Seminar Room "Zwenkauer See" - A.03.07 Detailed discussions on assumptions and misconceptions in data science (from data capturing to processing and linkage), and how to identify and prevent them.

Presentation 4 Room: Seminar Room "Zwenkauer See" - A.03.07 Recommendations on how to improve data quality aspects, what data scientists should check with their data sources, how to ensure data are research ready, and summary of course content.

Practical Session 2 Room: Seminar Room "Zwenkauer See" - A.03.07 Group discussions on how to implement recommendations within your own organisation (30 min), short presentations on strategies to improve data quality (30 min), course wrap-up and feedback.