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>Heidi Seibold
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About The Speaker

Heidi Seibold

Reproducible Data Science - Data science projects are generally complex, involve data cleaning, visualization, and machine learning pipelines, and should, in the end, be reproducible and presentable. In this talk, I will show some issues with making data science projects reproducible and discuss some solutions such as automation, good naming, and structure of folders and files, literate programming, using stable computing environments, etc.

Reproducible Data Science

Data science projects are generally complex, involve data cleaning, visualization, and machine learning pipelines, and should, in the end, be reproducible and presentable. In this talk, I will show some issues with making data science projects reproducible and discuss some solutions such as automation, good naming, and structure of folders and files, literate programming, using stable computing environments, etc.

 

Heidi Seibold is a data science researcher and research software engineer. She believes that good research is reproducible, reusable and open and spends most of her time trying to improve the way we do research. She is a member of the LMU Open Science Center, a member of the Knowledge Exchange Open Scholarship expert group and a core member of OpenML. She teaches machine learning, R, and open and reproducible research.

Heidi studied statistics at LMU Munich and did her Ph.D. in computational Biostatistics at the University of Zurich. She worked as lead of the DIFUTURE analysis group, as deputy professor of biostatistics at LMU, and is currently working at LMU Munich, Bielefeld University and Helmholtz Zentrum München.