<p><div><div><div><div data-width="100" data-show-faces="false" data-send="true" data-layout="button_count" data-height="21" data-href="https://www.r-bloggers.com/the-team-data-science-process/"></div></div><div><a data-url="https://www.r-bloggers.com/the-team-data-science-process/" data-counturl="https://www.r-bloggers.com/the-team-data-science-process/" data-count="horizontal" data-text="The Team Data Science Process" class="twitter-share-button" data-via="rbloggers" href="https://twitter.com/share"></a></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2016/10/the-team-data-science-process.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>As more and more organizations are setting up teams of data scientists to make sense of the massive amounts of data they collect, the need grows for a standardized process for managing the work of those teams. To help with this, the data science team at Microsoft has drawn on their experience with large-scale data science projects to develop the <a target="_blank" href="https://github.com/Azure/Microsoft-TDSP">Team Data Science Process.</a> The process is built around this data science lifecycle:</p>
<p><a target="_blank" href="http://revolution-computing.typepad.com/.a/6a010534b1db25970b01bb0945bf4d970d-pi"><img title="Dsprocess" alt="Dsprocess" src="http://revolution-computing.typepad.com/.a/6a010534b1db25970b01bb0945bf4d970d-800wi" border="0"></a></p>
<p>The Team Data Science Process proposes a standardised directory structure for managing the data, code and documents for a data science project, and provides for tracking of those artifacts using a version control system such as Git. It also proposes a shared distributed analytics infrastucture to provide the computational and storage resources that the data scientist tools rely on. It also provides two open-source utilities to support data scientists:</p>
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<li><a target="_blank" href="https://github.com/Azure/Azure-TDSP-Utilities/tree/master/DataScienceUtilities/DataReport-Utils">IDEAR: the interactive Data Exploration Analytics and Reporting (IDEAR)</a>, an interactive data exploration tool based on R and RStudio</li>
<li>The <a target="_blank" href="https://github.com/Azure/Azure-TDSP-Utilities/tree/master/DataScienceUtilities/Modeling">Automated Modeling and Reporting</a> tool, which automates and standardizes the process of creating reports describing the results of statistical models created with R's <a target="_blank" href="https://mran.microsoft.com/package/caret/">caret package</a>.</li>
</ul>
<p>You can find more background on the team data science process <a target="_blank" href="https://blogs.technet.microsoft.com/machinelearning/2016/10/11/introducing-the-team-data-science-process-from-microsoft/">in this blog post</a>, and you can also watch this presentation from the developers of the process <a target="_blank" href="https://channel9.msdn.com/Events/Machine-Learning-and-Data-Sciences-Conference/Data-Science-Summit-2016/MSDSS23">from the Data Science Summit</a>, embedded below.</p>
<p><iframe height="281" allowfullscreen="" width="500" src="https://channel9.msdn.com/Events/Machine-Learning-and-Data-Sciences-Conference/Data-Science-Summit-2016/MSDSS23/player"></iframe></p>
<p>You can download the various artifacts of the Team Data Science Process (and even suggest your improvements via a pull request) at the Github repository linked below.</p>
<p>Github (Azure): <a target="_blank" href="https://github.com/Azure/Microsoft-TDSP">Team Data Science Process from Microsoft</a></p>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://blog.revolutionanalytics.com/2016/10/the-team-data-science-process.html"> Revolutions</a></strong>.</div>
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