Christyn is currently a quantitative analyst at Youtube.  Previously she worked at Slide, a social gaming startup where she also performed quantitative analysis on product features.  She has also worked as a statistician at Stanford University School of Medicine’s Center for Clinical Research and INC Research (pharmaceutical CRO).

Hi Christyn!  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?

I received a PhD in Research, Evaluation and Measurement from the University of Pennsylvania as well as an MS in Statistics.  My undergraduate degree was in Psychology at Loyola University.  At the time, my education felt like a hodgepodge of my interests and I wasn’t sure how to fit them together into a career. After a bit of trial an error, I found that my background in and love for psychology combined with my desire and instinct to quantify things made me well suited for social gaming. My psychology background gave me the skills to understand why people were using our games, what made them come back, what could we do to change their behavior, etc. My background in statistics and experimental design taught me how to study, test, quantify and interpret their behavior. Both disciplines help me to ask the right questions, find the best approaches and understand the answers.

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?

One of the most important things to get right is what you do with the results of an analysis. Practice explaining statistical topics to non-quantitative people. Choose your words well and err on the side of over-explaining. Misinterpretation can spread like wildfire and it’s best to avoid it at all costs. Also, remember that it’s not enough to just state what the results are. You also need to need to consider and address what it means, the implications and what should you’re audience *do* as a result. Actionable insight are key.

What’s your definition of data analytics and science?

It’s the art of drawing insight from numerical chaos.

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

You need to be on top of your game in three major areas. First, you need to know statistics and research design. Second, you need to know how to work with and communicate to non-quantitative people. Third, you need programming/technical skills. In most places, knowledge of MySQL and a statistical package, like R, is enough. But it’s also imperative that you understand where the data is coming from, where it’s being warehoused, the most efficient way to access it, etc. I never learned any scripting languages and, while it’s not necessary for my job, a basic knowledge of it would make a lot of things easier. Luckily, all three areas can be self taught with books, websites and free educational programs like Coursera ( ). So if you don’t have any experience in one of the areas or just need a refresher, get to it.

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