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Data analysis in practice

This two-day course will be taught in an “inverse classroom” style: the students will be organized in groups and will analyze various biomedical datasets, using information provided by the trainers. The groups will present their results which will then be discussed with the other groups and the trainers.

The datasets will be chosen in order to cover the most common issues that can arise during statistical analysis, including verifying the assumptions of tests (the requirement for normality of data in particular), the handling of outliers and of missing data.

Teachers

Rachel Marcone, Joao Lourenco and Vincent Roh

Author

Rachel Marcone

Credits

Parts of this course were inspired by a previous course developed by Frederic Schutz and Isabelle Dupanloup.

Material

  • This website

Learning outcomes

General learning outcomes

After this course, you will be able to:

  • Choose basic statistical methods in order to analyse a dataset
  • Take care of common issues arising during a statistical analysis
  • Perform a statistical analysis
  • Communicate the results of the analysis effectively, both orally and in writing format

Prerequisites

  • Basic knowledge of statistics, in particular knowledge of which statistical tests to apply
  • Working knowledge of R, including the ability to load data into R, select data of interest, plot data, and perform basic tests