Formulating a research question, considering the design of a study, collecting data, analyzing data and
reporting of findings are crucial steps in any scientific research. However, other aspects that are equally
important often receive less attention and in some cases may even be neglected. These topics include:
• Data Quality;
• Data Presentation;
• Data Documentation.

Specific topics covered include Data preparation, Missingness, Data restructuring (e.g. categorization,
normalization, standardization), Visualisation. Other topics include:
• Data Reduction (PCA);
• Multiple testing;
• Stratification;
• Sample size.
Most of these topics discussed in this course lie at the cross-roads of Epidemiology and Biostatistics and cover issues that are typically faced by starting researchers.

For starting scientists it is important to realize that these aspects are an integral part of scientific research
and form the basis of the scientific integrity of any research project. In this course, the first three days will be
devoted to these topics. Furthermore, on the fourth day, the students will get an opportunity to gain some initial experience with hands-on exercises in the practical. Finally, on the last day, there will be lectures on selected topics (Big Data & Machine Learning), that are increasingly dominating the scientific landscape, but are usually covered from a statistical, mathematical or engineering perspective. Here, these topics will be discussed from the point of view of an epidemiologist, as there is much overlap with the practice of epidemiologic analysis!

Objectives

• To gain better understanding of issues related to Data Quality, Data Presentation and Data Documentation.

• To enable students to translate the theoretical insights into practical tools, which they can immediately implement in their own research.

Participant profile

The course is particularly intended for participants who have completed their data collection and move towards their first data analysis.

Assessment