Kovas Boguta has previously worked at Wolfram Research and Wolfram Alpha in various research roles.  He then went on to found Infoharmoni, a social media analytics company.  Currently, he is the Chief Analytics Engineer at Weebly.

Hi Kovas! What is your educational background and how has it compared you for your role in data analytics and science?  What skills did you not develop in school that you find important in your work?

BS in Math, with a minor in CS from the University of Chicago. One of the great benefits of formal education in math and science is that it teaches you to debug your thought process, and refines your BS detector. Being able to read research papers certainly helps too. Unfortunately none of my classes forced me to become fluent in the linux toolchain, so I had to pick up those skills much more inefficiently later.

What are the biggest challenges in data science and analytics?  What are  the most important things to ‘get right’.  What are the best technologies available to solve these problems?

There are so many!

On the technical side of things, our tools are pretty bad at supporting the scientific method. Source control, IDEs, and most everything else is geared towards engineering, rather than doing  experiments. IDEs have plenty of support for unit tests for instance, but almost none for visualization. Environments like Mathematica and IPython support the scientific workflow much better, but are still far from a panacea.

Communicating the results of experiments is also a huge challenge. Pie charts don’t convey a lot of nuance, but that’s what typical analytics consumers expect. A compromise solution is to have more sophisticated presentations, but with the most important features prominently highlighted and labeled, and then re-emphasized in textual form.

Ultimately the only way to gain intuition around data is to work with it yourself. And we need to find better ways to empower the rest of  the population to do exactly that.

What’s your definition of data analytics and science?

Data science is really the science of computation. If we understand
the computation, then we can predict it, or program it. That is where
the value comes from.

What advice can you give someone with little experience in analytics to  pursue a career in the field?

The best advice I ever got was from Stephen Wolfram, which was “Ask the simplest, most obvious questions first”. This is a very scalable bit of advice. It applies no matter how sophisticated or experienced you are.

And it blends nicely with Paul Graham’s advice of “Make something people want.”

So I would start by answering simple questions that someone cares about, or should care about. That someone might be you, such as about your personal finances or health data. Or it could be your organization, a customer, or a community you are in. Answer some  simple but useful questions for someone. If its useful, they will come back with more questions, and you are rolling.

In terms of employment, research and academia is a great place to start. Particularly if you can attach to a lab doing research that has commercial potential, like robotics, computer vision, ecological or social modeling, etc. Another a good choice is to go work for people building cutting edge analytics tools. Or, you can start with a growth area, like personal health analytics, or political analytics.

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