The useR! 2016 Tutorials

by Joseph Rickert

Over the years I have seen several excellent tutorials at useR!conferences that were not only very satisfying "you had to be there" experiences but were also backed up with meticulously prepared materials of lasting value. This year, quite a few  useR!20i6 tutorials measure up to this level of quality. My take on why things turned out this way is that GitHub, Markdown, and Jupyter notebooks have been universally adopted as workshop / tutorial creation tools, and that having the right tools encourages creativity and draws out one's best efforts.

Jenny Bryan's tutorial Using Git and GitHub with R, Rstudio, and R Markdown and the tutorial by Andrie de Vries and Micheleen Harris: Using R with Jupyter notebooks for reproducible research are two superb, Escheresque self-referencing examples of what I am talking about. Bryan's tutorial which uses GitHub and R Markdown to teach GitHub and R Markdown is an impressive introduction to these two essential resources. And, the tutorial 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 Microsoft's Azure Data Science VM. The Linux version of this VM comes provisioned with JupyterHub, 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 notebook 06 to see how to set all of this up.

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.

Unfortunately, I couldn't get around to see all of the tutorials, but two more that I can heartily recommend are MoRe than woRds, Text and Context: Language Analytics in Finance with R, the introduction to text mining by Sanjiv Das and Karthik Mokashi and Machine Learning Algorithmic Deep Dive by Erin Ledell. Sanjiv Das is an inspired educator and I have never seen a presentation either at R/Finance, useR! or even BARUG, the Bay Area useR Group where he wasn't on his game and super prepared. The tutorial he and Karthik gave this year at useR!2016 is a self-contained course in text mining.

 

Erin Ledell also came prepared with more tutorial material 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.

 

 

Ledell is also a gifted teacher who anticipates where her audience may have difficulties. Her historical approach to understanding gradient boosting machines 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.

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.

Comments

[Mikec2A]

Thanks for the heads up on these neat resources. BTW clicking the "Using Git..." and "Using R with Jupyter..." links doesn't work in Chrome for some reason. Had to right click and open in a new tab.

[Rick]

Erin's tutorial was terrible, seriously.

[Mark]

I agree regarding Erin's tutorial, it was more a shallow float than a deep dive.

[Joseph B Rickert]

Hello Rick and Mark,
Thank you for taking the time to comment on my post about the tutorials. Constructive criticism makes us all better.

I would like to say though that it appears as if you both consider "the tutorial" to be limited to what happened during the 3 hour session. My view is that the actual delivery is only part of "the tutorial". Sure it would have been better not to have spent time on the setup, and Erin did not have nearly enough time to cover all of the material that she prepared: https://github.com/ledell/useR-machine-learning-tutorial

But, she certainly pointed out where to "dive". I think that the "deep dive" part comes from working through those materials afterwards.

Maybe you think that the prepared material itself is not deep enough? If so, I would find it helpful if you could briefly describe what is missing.

[Marketing Analyst]

Hello,
It is a great summary and I liked the way they explained the ML approach.
I will definitely move to their site to dig deeper
Chers,
Chill