Last week I had the opportunity to host a panel discussion at New York University on the topic of data science as a career. Held at the NYU Center for Data Science, the panel also included Todd Lowenberg, group head of advanced analytics at MasterCard Advisors, and Kirk Borne, principal data scientist at Booz Allen Hamilton.
In front of a full house of students, we discussed everything from how we each got into the data science field to our “crystal ball” predictions of what the students might expect as they begin their own careers. It was a fantastic experience to get to speak to so many students on the cusp of the professional data science world, and to introduce them to what we’re doing at Intel to advance data analytics. I think it was an eye opener for the audience to hear that Intel is not just a chip maker; Intel is on the inside, but it’s also on the outside enabling companies around the world to use data analytics to become more agile, competitive and intelligent.
Part of what makes a discussion about fields such as data science so successful is personal stories about the inspiring moments we data scientists have had throughout our careers. Kirk shared an uplifting experience that brought him out of the world of astrophysics and into the world of data science. While at NASA, he worked with an IBM internship program that was teaching data analytics to inner city high school students. How, he asked, did they get these kids interested in such an advanced concept when so few of them regularly went to class?
They were interested in learning all about data because the program related it to something they cared about – sports analytics. These kids never knew that using math could influence the play of their favorite athletes, and they were hooked on learning more. The graduation rate at their high schools sat at around 47% - but after the internship, the graduation rate among these students soared to 93%. Kirk knew then that we were on to something special with data analytics, and has spent his life dedicated to the field ever since.
Todd surprised the panel by introducing MasterCard as a technology company. Which, when you think about it, isn’t a surprise at all. With millions of customers each having a vastly different spending pattern, the data sets available for analysis are some of the most interesting and unique available – a veritable “kid in a candy store” situation for any data scientist.
Todd also outlined one of the most important concepts of the discussion – data science as a team sport. It’s true that having advanced knowledge of mathematics and programming is fantastic background for a data scientist. But, in any company, you won’t find just one data scientist doing it all – just like Michael Jordan couldn’t have scored so many points without Scotty Pippen at his side, data scientists all bring their own skills to the table that together build an ideal team.
In fact, we’re looking for all kinds of skills and backgrounds as we look to build out our team at Intel – from programmers to those with creativity, curiosity, and great communications skills. It’s rare to find a “data unicorn” that can do it all, and we’re not spending our time recruiting for such a talent. We build out teams to reflect a variety of backgrounds and experience, which brings greater insight to our data analytics work. In this spirit, we had a very diverse group in the room, with students majoring in physical science, math and statistics, and computer science. This is incredibly encouraging, since diverse backgrounds build a better data science team.
After our discussion, we had the opportunity to learn all about what was top of mind for the students. A theme that kept popping up in my conversations went something like this – “I’m really good at computer science, so how do I show my mettle as a data scientist?” My advice to them was to get their hands on a data set – whether it’s from Kaggle, DataKind, or the government – and build up a data analytics environment. Calculate something on it, whether it’s a correlation or Tableau visualization, and tell a story with that data. It’s great practice, and will show anyone interested in the field what data science is really like. It will also show future employers that you’ve done work in the field, and that you understand how to deal with messy data and think about these types of problems.
I hope to be able to host more of these university discussions in the future. It’s been some time since I left the world of academia, and it’s invigorating for me to spend time with students, learn about what they’re working on, what’s challenging them, and help guide them on their path to data science. With the huge shortage of data scientists we’re faced with today, it’s fantastic to see so many great minds ready and willing to jump into the field. And maybe if I’m lucky, I’ll have a few of them on my team.