Overview
This learning path is focused on R as a language for data analysis, statistics, and reproducible analytical work. It is intended for professionals who need a structured route into R without relying on scattered resources.
Main areas
- What R is and where it fits best
- R Studio as a practical environment for analysis work
- Packages, workspace management, and project structure
- Reproducible workflows for exploration, modeling, and reporting
- Progression from foundational R use to more advanced analytical programming
Typical modules
- Installing and configuring R and R Studio
- Packages, libraries, and workspace conventions
- Data import, exploration, and transformation in R
- R for reporting, notebooks, and reproducible outputs
- Advanced R topics for cleaner analytical code
Good fit for
- Analysts and researchers working with statistical workflows
- Teams that need reproducible analysis and reporting practices
- Professionals moving between Python, BI tools, and statistical computing
Note on the legacy material
Older versions of this area linked to a wide set of community resources and references. The current version keeps the topic structure while reframing it into an original, service-oriented training path.