DJ Patil is arguably the most well-known data scientist in the world. He’s the Chief Data Scientist for the White House, he built the first data science team at LinkedIn, and along with Jeff Hammerbacher, is credited by Forbes as coining the term “data scientist.”
So, how did Patil climb to the top of the data science ranks? How does his journey compare to the current data scientist landscape? And what are the takeaways for would-be chief data scientists? Well, we’ve got some data on that.
The educational backgrounds of data scientists
It may come as a surprise to learn that Patil’s journey started in community college. After high school he attended De Anza Junior College, where according to a comment on his LinkedIn profile, he realized he “was pretty good at math.” In his article, How I Became Chief Data Scientist, he writes about how one of the most valuable things he took away from community college is his love for mathematics.
All through high school, I was a mediocre math student (and that’s putting it kindly). But then I took a calculus class — and it rocked my world. The lecturer really took the time to explain deep concepts and helped me see the intrinsic beauty. Today, when I explain some of the concepts I learned back then, I still call upon the way it was explained to me all those years ago.
Patil’s background in mathematics is fairly common among data scientists with graduate degrees, ranking in the top five:
But beyond a pure Mathematics degree, it’s interesting to note that nine out of the top 10 backgrounds require a foundation in mathematical intuition; Business Administration/Management being the only outlier — though an MBA is hardly a “math-free” degree. And the list as a whole is very STEM-centric.
The above analysis focuses only on the backgrounds of data scientists who have a graduate level degree because, like Patil, the majority of data scientists have a graduate degree.
Data science degrees: Bachelors, Masters, Ph.D.
While Patil’s education started in a community college, he went on to get a BA in Mathematics from University of California, San Diego., then a Ph.D. in Applied Mathematics from the University of Maryland. His trajectory is common of many data scientists. In our research, we found over 79% of data scientists earn a graduate degree.
42% of current data scientists list a master’s degree as the highest degree attained, while 38% earned a Ph.D. like Patil. However, we also found some underlying trends that will likely impact how this breakdown looks in the coming years.
When we looked at the ratio of Ph.D.s to master’s degrees broken out by levels of experience — junior data scientist, senior data scientist, and chief data scientist — we found that the ratio of Ph.D.s to master’s degrees for chief data scientists is actually less than that of senior data scientists.
While Ph.D.s aren’t the most popular degree among chief data scientists today, they will be very soon. Highly-educated senior data scientists will advance into the role, and Patil’s path will become much more common for junior data scientists looking to advance their career. So, is that the best path for would-be data scientists to take?
How to become a chief data scientist
While it does seem like more and more data scientists are pursuing a Ph.D., having a Ph.D. is not necessarily required to be successful. The general consensus is that no one needs a Ph.D. to do the work of data science. Instead, it’s likely that what we’re seeing here is the migration of Ph.D.s into data science.
The rising demand for data scientists has opened up a new career path for many Ph.D.s, and they’re taking it. How to make the transition from Ph.D. to data scientist is a hot topic and data scientists turned Ph.D.s from Twitter, Quora, and Birchbox have all shared detailed advice on how and why they made the jump.
As senior data scientists move into the role of chief data scientist over the coming years, a Ph.D. may be the norm, but there’s no reason to expect it will stay that way. If the title data scientist retains its popularity, it’s likely we’ll see more people pursue educational paths tailored to that career, rather than pursue Ph.D.s only to make the jump into data science.
Because ultimately, what makes someone like Patil so successful as a data scientist isn’t the degrees. It’s the thing spark he found during his calculus class at community college — a love of numbers, the draw of mathematical concepts, and the curiosity that drives him to always learn more.