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).