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Certification Course

Principles, Statistical and Computational Tools for Reproducible Data Science

Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.

In this course, you will learn:

Understand a set of concepts, thought patterns, analysis methodologies, and computational and statistical tools that facilitate data science and reproducible research when used together.
Case studies that highlight diverse techniques are used to teach the fundamentals of reproducible science.
Ensure data provenance and a repeatable experimental design using these key aspects.
Statistical approaches for analysing data in a repeatable manner.
Data repositories/Dataverse, Rmarkdown/R Notebook/Jupyter/Pandoc, and processes for reproducible data analysis and version control (Git/GitHub, Emacs/RStudio/Spyder), reproducible data (Data repositories/Dataverse), and reproducible dynamic report creation (Rmarkdown/R Notebook/Jupyter/Pandoc).