Before joining SPICED, Kent had just finished up a graduate program at Princeton having worked for five years in finance and economic research. Here on the blog, he tells us all about his decision to learn Data Science, and his experience at SPICED.
What were you up to before SPICED?
Just before coming to SPICED I finished up a graduate program at Princeton, and before that I spent about five years in finance and economic research.
When did you think specifically about learning Data Science?
I spent a year in between my two grad school years working on a research project, and during that time I heard about some interesting projects that had been done with Python in my field. So I started to learn Python and then R for the things that I was already doing. Exposure to these communities naturally led me to Data Science, since the tech stack is largely Python/R, but also the statistics and econometrics foundation I got in grad school was very transferable.
When did you move to Berlin, and why did you choose this city?
My research project was that year in between grad school was actually at a German research organisation here in Berlin. So, prior to SPICED, I’d already spent a year or so in Berlin before. I’m from Philadelphia in the US, which is a blue-collar, gritty city where people don’t mince words. So I felt immediately at home in Berlin.
What makes Berlin special for you?
A lot of things. But mostly the summertime. It’s the reward for sticking it out through the dark eternal winter. The parks and lakes, the cafe’s and bars, the whole city seems to transform — more so than in any place I’ve ever been.
Before starting SPICED, what were your biggest fears?
That it would either be too advanced or not advanced enough. I felt that it would be too difficult to bring six people together with totally different backgrounds and make the curriculum work in such an intense learning environment.
How did you overcome them?
Well they were overcome for me. I kept up my end and worked hard, as did my classmates, and Kristian made sure that no one was left behind. A few of us were more advanced or had more experience than the others. But given the pace and how everyone helps each other, everyone pretty much is at the same level by week 2.
What have you learnt about yourself during your time at SPICED?
I learned that I’m actually a creative problem solver, if not the fastest problem solver. In terms of programming, once I got my feet under me and was comfortable enough with the tech stack, I was able to come up with some creative solutions to the projects, and a neat application for my final project. The intense smash-the-glass method of the boot camp helped me get to the next level skill-wise, such that I spent less time thinking about syntax and error messages and focus more on the solutions.
What was the biggest challenge for you in learning Data Science?
Just doing it. Sometimes when I come up against a new challenge or new technology, I’ll futz around, read a tutorial, watch a youtube video, write a few lines of code, and just kind of circle the drain. Its weird but I was almost afraid of that frustrated feeling you get when you can’t get around an issue. Having my classmates there, having weekly projects, and just spending an intense amount of time doing this stuff over 12 weeks helped me get over it and just dive in and hack through those error messages.
Now you’ve finished at SPICED, what are you up to?
I’m a Data Scientist at the Deutsche Bundesbank, the central bank of Germany. I work on a project with the ECB collecting security-by-security data from nearly all euro countries and making sure that data is high-quality for our users, and exploring machine learning methods to help us get it all done faster and more accurately.
What advice do you have for people who are thinking about studying Data Science at SPICED?
I would say that its important to try everything. There will be days that you learn one technology or package in the morning and another in the afternoon. Sometimes you’ll want to just keep grinding on that code chunk from yesterday instead of working with the new material from today. But from my perspective, one of the most valuable things for me was to have tried out and gotten a least a bit of hands-on experience with a bunch of packages and technologies so that I know the ecosystem. I would also say listen to the TalkPython podcast, its great for getting to know the ecosystem a bit better as well. Once you know that, and you have a solid programming foundation, you have the tools you need to figure out and solve most of what you need.
