Rapid prototyping: Decision trees with iPython and SKLearn.
Details
I'll get this started by demo-ing a rapid model prototyping environment.
The free Anaconda (http://continuum.io/downloads) distribution of Python is all that's needed to begin prototyping predictive models. This workshop will supply several sample datasets and walk through the theory behind and use of sklearn (http://scikit-learn.org/stable/)'s decision tree (http://scikit-learn.org/stable/modules/tree.html) as a classifier. I'll be using the iPython Notebook (http://ipython.org/notebook.html) (comes installed with Anaconda) on my end, though you're obviously welcome to use a different python development environment should you like. If we have extra time at the end, we can also dive into random forests (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html).
What you need to do before: Install Anaconda (http://continuum.io/downloads) on your device of choice and bring said device with you to the workshop. We'll take care of everything else once we're there. If you want to save some bandwidth on site, you're also encouraged to download the datasets ahead of time (which I'll be announcing a week before the event).
Come, network, learn something new, teach someone else something new, and let's start this thing off right.
Location: Snapflow. 600 NW 14th Ave #200
I'm looking for presenters for future workshops. You have some work you want to share with the community at the experiential level? Is there a favorite modeling technique? Interesting case study? A killer visualization everyone just has to know about? Have you single-handedly found the best tech stack to do data science? Great! Contact me. We have lots of data science talent here in Portland, and I want all of us to grow faster because of it!