At The Data Incubator we run a free eight-week Data Science Fellowship Program to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring Data Scientists. Dan was a Fellow in our Summer 2015 cohort in Washinton, DC, who landed a job with one of our hiring partners, Freddie Mac.
Tell us about your background. How did it set you up to be a great Data Scientist?
I have a Ph.D. in pure mathematics, and my dissertation was in number theory. After graduating I was a visiting assistant professor at a large university and later at a liberal arts college. Doing mathematics taught me to think deeply about hard problems, but I was lacking some of the skills I would need to be a data scientist. To prepare for The Data Incubator and a career as a data scientist, I looked for opportunities to improve my statistics and coding skills. For example I helped my department develop a statistics curriculum using R, and I supervised undergraduate research in number theory using C and Python.
What do you think you got out of The Data Incubator?
The Data Incubator is incredibly good at recruiting highly motivated people who are very talented to be Fellows, and then introducing those Fellows to employers who want to hire a data scientist. This happens through Partner Panels, Happy Hours, Pitch Nights, and the growing network of alumni. I learned a great deal during the program, and much of that happened by interacting with the other Fellows. I also got a lot of great feedback from the other Fellows about my capstone project. The Data Incubator gave me support for job placement by helping me write a great resume, giving me advice about contacting employers, and doing interview preparation.
What advice would you give to someone who is applying for The Data Incubator, particularly someone with your background?
Everything will be much easier if you spend some time learning to code well. Create a great capstone project. It’s a good way to showcase your skills to employers. Look at the blog posts “Data Sources for Cool Data Science Projects” (here and here), and start working on a project that would be interesting to business people. I tried to pick a project that I thought I could complete even though I didn’t acquire some of the skills I needed until I was a Fellow. [Editor’s Note: For more information about how to prepare for The Data Incubator, check out this post.]
What is your favorite thing you learned at The Data Incubator?
I really liked learning about various machine learning algorithms. I knew almost nothing about machine learning when I started at The Data Incubator, and I find it to be a mathematically rich subject. The biggest thing that I got out of The Data Incubator is the ability to take on a data science project and learn whatever techniques I need in order to solve it.
Could you tell us about your Data Incubator Capstone project?
For my capstone project I analyzed data from the online lender Lending Club. Potential borrowers create a profile on the Lending Club webpage, and individual or institutional lenders can choose which loans to fund. I created a model to predict which loans were most likely to go into default. You can read more about it here: (http://dfile.github.io/).
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