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accelearatoR will include Python collaterals.
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# Introduction
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**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.
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**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.
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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.).
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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.).
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# How-to
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* `Docs` - Normally related documentations, references, and perhaps
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yielded reports will be put in this directory.
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* 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.
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* 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.
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* Makefile is by default provided to generate documents of other formats, or alternatively rmarkdown::render can be used for the same purpose.
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# Contributing
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