Data Heroes: Mike Stringer

November 25, 2012

Next up in our series of amazing data do-gooders that we’ve met in our travels comes Mike Stringer. Mike is one of the managing partners at Datascope Analytics in Chicago, where he solves the toughest data problems for the trickiest clients. We met Mike when we came out for our Chicago DataDive and asked around for who would want to help wrangle non-profit data problems. Mike charged to the front of the line and stepped up to lead The Red Cross of Chicago through their problem of hunting down the neighborhoods most at risk of fire. We’ve been thrilled to get to work with Mike and are always delighted to see him sharing his insane intelligence with the Strata crowds or just taking off the tie to take down some tough datasets. Without further ado, meet our Data Hero of the month, Mike Stringer!

What’s your day job?

I use data as a resource to solve problems and design products at Datascope Analytics in Chicago.

Tell us about your work with DataKind.

We worked with the Chicago Red Cross. One of their challenges is to understand the areas (neighborhoods, schools, zip codes) where they can focus their fire prevention efforts to have the maximum impact. Using data about where they have responded to fires over the past couple of years, our team built an interactive map that would visualize the geographic areas with the highest number of responses. The map helped them understand the extent to which disasters are concentrated in those specific areas to help guide their prevention efforts. Now, in addition to exploring further projects, members of the team are working to help the Red Cross standardize their data format and building programs that will help them map their response data as it occurs.

What inspires you to use your data skills for good in your spare time?

Everyday I witness the unique ability for data (“big,” “small,” or anywhere in-between) to be used as a valuable resource for solving important problems. Getting involved in helping non-profits use data as a resource to solve important societal problems feels good! Maybe even more gratifying is that it helps build and train a community that is passionate and capable of cultivating and communicating a fact-based view of their surroundings.

What is one of the most surprising things you’ve learned or seen in working with data?

I think I started out thinking that working with data only required “wicked data analysis skillz” like network analysis, machine learning, statistics, and simulation. However, I constantly re-learn the importance of finding important problems to solve and communicating, often visually, the complex conclusions that arise while using data as a resource. With all the hoopla about big data, it’s surprising that the “soft side” is the step that falls short most often while working with data.

What’s the most interesting or visually striking data project you’ve seen recently?

I’m admittedly biased, but the e-discovery project that I’m working on at Datascope Analytics is extremely interesting. Also, I love the data artistry at Stamen, in particular their watercolor maps.

What does someone getting started with data science need to learn?

I think data science is evolving so rapidly that everyone is learning: a good start is to just dive in. Start by thinking of an interesting and important problem that data could help to solve. Then, scrounge up some data by hacking together a python script or using wget in Linux, analyze the data in python or R, and finally develop some interactive visualizations using d3 or Raphael. It will take some time to learn those tools, but less time than most people realize. Maybe after that go through Sivia’s “Data Analysis: A Bayesian Tutorial” and you will have a strong foundation to keep learning!