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(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2016/06/the-user-2016-tutorials.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="http://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>by Joseph Rickert</p>
<p>Over the years I have seen several excellent tutorials at useR!conferences that were not only very satisfying &quot;you had to be there&quot; experiences but were also backed up with meticulously prepared materials of lasting value. This year, quite a few  <a target="_blank" href="http://user2016.org/#tutorials">useR!20i6 tutorials </a>measure up to this level of quality. My take on why things turned out this way is that <a target="_blank" href="https://github.com/">GitHub</a>, <a target="_blank" href="https://en.wikipedia.org/wiki/Markdown">Markdown</a>, and <a target="_blank" href="http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html">Jupyter notebooks</a> have been universally adopted as workshop / tutorial creation tools, and that having the right tools encourages creativity and draws out one's best efforts.</p>
<p>Jenny Bryan's tutorial <span><a target="_blank" href="http://schedule.user2016.org/event/7Bad/using-git-and-github-with-r-rstudio-and-r-markdown-part-1#">Using Git and GitHub with R, Rstudio, and R Markdown</a> and the tutorial by Andrie de Vries and Micheleen Harris: <span><a target="_blank" href="http://schedule.user2016.org/event/7BbB/using-r-with-jupyter-notebooks-for-reproducible-research-part-1#">Using R with Jupyter notebooks for reproducible research</a> are two superb, Escheresque self-referencing examples of what I am talking about. Bryan's tutorial which uses GitHub and <a target="_blank" href="http://happygitwithr.com/workshops.html#user-2016-stanford">R Markdown</a> to teach GitHub and R Markdown is an impressive introduction to these two essential resources. And, the <a target="_blank" href="https://github.com/michhar/useR2016-tutorial-jupyter">tutorial</a> by de Vries and Harris makes very effective use of GitHub and Jupyter Notebooks. Moreover, this tutorial sets the gold standard for how to set up a system for interactive user participation. Harris and de Vries staged their tutorial on <a target="_blank" href="https://azure.microsoft.com/en-us/documentation/articles/machine-learning-data-science-provision-vm/">Microsoft's Azure Data Science VM</a>. The Linux version of this VM comes provisioned with <a target="_blank" href="http://jupyterhub.readthedocs.io/en/latest/getting-started.html">JupyterHub</a>, a set of processes that enables a multi-user Jupyter Notebook server. Once the VM is loaded with the training materials, its only a matter of giving students a username and password to grant them immediate access to the interactive workshop materials. Have a look at <a target="_blank" href="https://github.com/michhar/useR2016-tutorial-jupyter/blob/master/06_Jupyter%20notebooks%20on%20Azure.ipynb">notebook 06 </a>to see how to set all of this up.</span></span></p>
<p><span><span>After seeing this, and comparing it to other tutorials where instructors wasted the better part of an hour trying to get students up and running with local copies of their course materials I can't see why everyone wouldn't opt for a cloud solution to this problem. When word gets out, the Data Science VM is going to be the standard for delivering technical workshops.</span></span></p>
<div>Unfortunately, I couldn't get around to see all of the tutorials, but two more that I can heartily recommend are <span><span><a target="_blank" href="http://schedule.user2016.org/event/7BaZ/more-than-words-text-and-context-language-analytics-in-finance-with-r-part-1#">MoRe than woRds, Text and Context: Language Analytics in Finance with R</a>, the introduction to text mining by Sanjiv Das and Karthik Mokashi and <a target="_blank" href="http://schedule.user2016.org/event/7BaX/machine-learning-algorithmic-deep-dive-part-1#">Machine Learning Algorithmic Deep Dive</a> by Erin Ledell. Sanjiv Das is an inspired educator and I have never seen a presentation either at <a target="_blank" href="http://algo.scu.edu/~sanjivdas/RiskNetworks_slides_RFinance_2015_05.pdf">R/Finance</a>, useR! or even <a target="_blank" href="http://files.meetup.com/1225993/A%20Brief%20History%20of%20the%20Short%20History%20of%20Text%20Mining%20%28in%20Finance%29.pdf">BARUG</a>, the Bay Area useR Group where he wasn't on his game and super prepared. The <a target="_blank" href="http://algo.scu.edu/~sanjivdas/Temp/user2016/">tutorial</a> he and Karthik gave this year at useR!2016 is a self-contained course in text mining.</span></span></div>
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<div>Erin Ledell also came prepared with more <a target="_blank" href="https://github.com/ledell/useR-machine-learning-tutorial">tutorial material</a> then she could ever present in three hours. But because of her thoughtful use of GitHub, Markdown and notebooks we have a machine learning resource that is well worth studying. Just being introduced to this incredible visualization of decision trees by Tony Chu and Stephanie Yee made my day.</div>
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<p><iframe height="281" allowfullscreen="" width="500" src="http://www.r2d3.us/visual-intro-to-machine-learning-part-1/"></iframe></p>
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<p>Ledell is also a gifted teacher who anticipates where here audience may have have difficulties. Her historical approach to understanding <a target="_blank" href="https://github.com/ledell/useR-machine-learning-tutorial/blob/master/gradient-boosting-machines.ipynb">gradient boosting machines</a> provides an opportunity to clarify the differences between various versions of the boosting algorithms. Sometimes understanding how something came to be is halfway towards understanding how it works.</p>
<p>The bar for presenting lectures, tutorials and workshops has been set pretty high. Anyone who is serious about delivering a high quality education probably needs to develop some skills with GitHub, Markdown and Notebooks. Studying the tutorial materials from useR! 2016 is a good place to start.</p>
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