accelearatoR will include Python collaterals.
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# Introduction
**acceleratoRs** are a collection of R based lightweight data science solutions that offer quick start for data scientists to experiment, prototype, and present their data analytics of specific domains.
**acceleratoRs** are a collection of R/Python based lightweight data science and AI solutions that offer quick start for data scientists to experiment, prototype, and present their data analytics of specific domains.
Each of accelerators shared in this repo is structured following the project template of the [Microsoft Team Data Science Process](https://azure.microsoft.com/en-us/documentation/learning-paths/data-science-process/), in a simplified and accelerator-friendly version. The analytics are scripted in R markdown (notebook), and can be used to conveniently yield outputs in various formats (ipynb, PDF, html, etc.).
Each of accelerators shared in this repo is structured following the project template of the [Microsoft Team Data Science Process](https://azure.microsoft.com/en-us/documentation/learning-paths/data-science-process/), in a simplified and accelerator-friendly version. The analytics are scripted in R markdown (Jupyter notebook), and can be used to conveniently yield outputs in various formats (ipynb, PDF, html, etc.).
# How-to
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* `Docs` - Normally related documentations, references, and perhaps
yielded reports will be put in this directory.
* An accelerator should be able to run interactively in an IDE that supports R markdown such as [R Tools for Visual Studio (RTVS)](https://docs.microsoft.com/en-us/visualstudio/rtvs/rmarkdown) or RStudio.
* An accelerator should be able to run interactively in an IDE that supports R markdown such as [R Tools for Visual Studio (RTVS)](https://docs.microsoft.com/en-us/visualstudio/rtvs/rmarkdown), RStudio, VS Code with AI extensions.
* Makefile is by default provided to generate documents of other formats, or alternatively rmarkdown::render can be used for the same purpose.
# Contributing