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Heidi Seibold

Heidi Seibold


IGDORE | Munich, Germany


Authored Curriculum

Take a look at the content that I created on DataCamp.

My Most Recent Course

Survival Analysis in R

4 hours14 Videos50 Exercises11,614 Learners

DataCamp Course Completion

Take a look at all the courses I’ve completed on DataCamp.

My Work Experience

Where I've interned and worked during my career.

Self-employed | Jun 2022 - Present

Open Data Science Trainer / Consultant and Conference Moderator


Johner Institut GmbH | Jun 2021 - May 2022

Research and Education Ambassador


Helmholtz AI | Sep 2020 - May 2021

Group lead of the Open AI in Health group at Helmholtz AI


Bielefeld University | Nov 2019 - Aug 2020

Data Science Researcher


Ludwig-Maximilians-Universität München | Apr 2018 - Aug 2020



University of Zurich, Epidemiology, Biostatistics and Prevention


Institute | Dec 2014 - Mar 2018

PHD Candidate

Model-Based Recursive Partitioning for Stratified and Personalised Medicine

Institute | May 2014 - Nov 2014

IT Manager


Statistical Consulting Unit, LMU (StaBLab) | Jan 2012 - Apr 2014

Statistical Consultant


Institut für medizinische Informationsverarbeitung, Biometrie und


Epidemiologie, LMU | Feb 2013 - Mar 2014

Student Researcher

Project on gradient boosting and cross-validation

LMU - Ludwig-Maximilians-Universität München | Oct 2012 - Mar 2013

Teaching Assistant


My Education

Take a look at my formal education

Doctor of Philosophy (PhD), BiostatisticsUniversity of Zurich | 2018
Master of Science (MSc), StatisticsLudwig-Maximilians Universität München | 2014
StatisticsUniversidad Complutense de Madrid | 2012
Bachelor of Science (BSc), StatisticsLudwig-Maximilians Universität München | 2012

About Me

Heidi Seibold

Heidi is an independent researcher with IGDORE and research and education ambassador at Johner Institut. Her research is on the intersection of data science, open science and medicine. Heidi has collaborated on several R packages and was reproducibility editor for the Journal of Statistical Software. She promotes open and reproducible science and sees R and Git as some of the most powerful tools for computational reproducibility in statistics and machine learning. Heidi loves to teach, especially R related topics. In her free time she likes to cycle.

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