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<!DOCTYPE html>
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<h1 class="page-header">Our 3 Tracks
<small>by <a href="https://www.linkedin.com/profile/view?id=AAkAAA7Q8DwBrkZlkyyL8E0Am0N5javfER1dPCU&authType=NAME_SEARCH&authToken=XjId&locale=en_US&trk=tyah&trkInfo=clickedVertical%3Amynetwork%2CclickedEntityId%3A248574012%2CauthType%3ANAME_SEARCH%2Cidx%3A1-1-1%2CtarId%3A1443142348053%2Ctas%3Afri">Friederike Schüür</a>
and <a href="https://www.linkedin.com/in/ivavuk">Iva Vukićević</a>
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<p><i class="fa fa-clock-o"></i>Posted on October 1, 2015</p>
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<p class="lead">
We ‹3 hackathons! People coming together using their skills for a good cause? That’s awesome! Right?
</p>
<p class="lead">
Well, yes! Yes, it is. But implementation can ruin, or save, the best of ideas - and Hackathons are a great idea.
So let’s talk about implementation.
</p>
<p>
We’ve been to hackathons and one problem that we’ve seen is that some data skills, like loading and cleaning that
data, are required early on in the pipeline. While people are scrubbing the data, others with different skills,
ones more suited to analyzing or modeling, will have to wait.
</p>
<p>
Now, that can be frustrating. Who likes to show up to an event early (imagine your precious Saturday) pumped up and
ready to get going only to find out that you can’t contribute for at least a couple hours?
</p>
<p>
Right, that’s no fun! This is why we decided introduce STRUCTURE.
</p>
<p>
Yes, yes, we know... the beauty of hackathons is their egalitarian nature. Their spontaneity. People hacking away
doing cool stuff. That’s what we love about hackathons, too, and that’s why we’re introducing just the right kind
and the right amount of structure (so we hope). Our aim is to help everyone be productive right from the first
minute of the hackathon while ensuring we’re all working, roughly, towards a common goal.
</p>
<p>
Now, how exactly are we going to doing that?
</p>
<p>
First of all, we ask people to apply. To be clear, we’re not planning to let only the best people through. This isn’t
a job application. We want to make sure people attending the event have the minimal skills required to be productive
whilst ensuring a good mix of different types of skills so that people can have fun learning from each other. That’s
why we ask you to fill in the application from. So please, bear with us!
</p>
<p>
Second, we’d like to introduce to you our three data tracks:
<ul>
<li>Clean the Clutter</li>
<li>Describe the Disorder</li>
<li>Model the Mess</li>
</ul>
People within a track will share the same set of skills (or are interested in getting better in working that set of
skills).
</p>
<p>
People in <b>“Clean the Clutter”</b> will focus on getting and cleaning the data, a crucial step in the data
processing pipeline. We’ve got a couple of really interesting but hard to work with datasets. People in “Clean
the Clutter” will help us all get access to the data, from scraping to setting up databases to query from. Their
objective will be to unify our sources and build clean datasets for everyone’s use. Outputs from our wranglers
will be available for download after the event. We’ll need resourceful people, come join “Clean the Clutter”.
</p>
<p>
People in <b>“Describe the Disorder”</b> will work on getting some really good descriptive statistics of the data
sets we’ll be working with. A good understanding of our data will be crucial; good descriptive stats are the first
and crucial building block in getting to that understanding. The descriptive stats groups will begin their day with
some pre-cleaned data (different from what’s being cleaned by “Clean the Clutter”) and dig into it in search for
those actionable insights. Join “Describe the Disorder” to get intimately familiar with our healthcare data sets.
</p>
<p>
Finally, people in <b>“Model the Mess”</b> will construct machine learning type models to learn what can predict
health care utilization and price, for example. They'll work on similar problems as the "Clean the Clutter" and
"Describe the Disorder" tracks, just from a different perspective using slightly different tools. And that’s really
the beauty of it all, people working together towards a common goal using the skills they have.
</p>
<p>
Each track will have 2-3 groups of 4-5 people. Plenty of people to learn from, small enough for each one of you to
have an impact.
</p>
<p>
Now, should you only consider a track if you already have all the skills you need to contribute?
</p>
<p>
Well, if you’re an expert in cleaning, describing, or modeling, we’d love to see you at the event. But, if you’re
interested in learning about cleaning, describing, or modeling, that’s great, too. We ask for some basic knowledge
of python (or R), but otherwise we’d love to foster curiosity and provide an environment for you to learn in. So,
if you spend your days setting up databases and you’d want to give visualization a try, consider joining the
descriptive stats track! If, on the other hand, you already know something about predictive modelling but want
to apply your skills to healthcare data, join our modeling team!
</p>
<p>
Towards the end of our hack day, we will have a poster/whiteboard session for everybody to present their results.
The goal is to learn from each other to understand what’s wrong with healthcare in the US and how we can contribute
to fixing it. There’ll be no prizes for the best project. Why compete? This issue is too important. Let’s work together.
</p>
<p>
We hope you <a href="application.html">Join Us</a>!
</p>
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