Get R_eady (10SMSTS-506/508)
Get R_eady: Introduction to Data Analysis for Empirical Research (10SMSTS-506)
Description
The course offers an introduction to data analysis in the transdisciplinary field of empirical research in the programming language R. The R system of statistical computing is openly available from https://www.r-project.org and provides a simple and flexible software environment for statistical analyses and graphics.
Tailored to the application of empirical research the course covers basics of functions and data formats in R, as well as the essential steps of a data analysis including data manipulation, descriptive statistics, statistical tests and graphical representations. Reflections on research methodology and transdisciplinarity will take place and critical thinking will be enhanced.
Target group
MA, PhD
ECTS Credits
Get R_eady: Prognostic & Prediction Modeling in Research (10SMSTS-508)
Description
Prognostic models to predict future events have increasingly been used across different fields, e.g. in the medical sciences (clinical prediction models, personalized medicine, prognostic models), in legal data science (predictive analytics), political sciences (scientific prediction), or related.
The derivation and validation of such models poses specific challenges, that require knowledge of distinct methodological aspects in order to develop models that are internally valid and can be generalized out-of-sample. This course covers traditional statistical as well as machine learning approaches for model development, sample size calculation, variable selection, methodological outcomes for the assessment of model performance, as well as model validation. The course encourages critical thinking regarding published prognostic models’ validity across different fields of research.
Target group
MA, PhD
ECTS Credits
1 ECTS
