At the time of writing, I’m a 52 year-old working in the fields of mathematics and data science. In mathematics, that makes me well-seasoned (and probably well-tenured, if I had chosen to continue in academia). In data science, some would consider me a dinosaur. In fact, many older people considering a career in data science might be put off by the thought that data science is tough to break into at a later age. But is that statement true? Should the over 50 crowd put down their textbooks and pick up their gardening tools?
Is Math a Young Person’s Game? Maybe
As far as the mathematics portion of my career, I didn’t become a mathematician until I was in my mid-thirties. Before that I dabbled with whatever venture brought in a few bob to feed the kids: computer operator, Ebay entrepreneur, aviation electrician. I was 36 when I decided to go back to school to get my master’s. If Alfred Adler is to be believed, my “mathematical life” had already long passed by the time I graduated.
Work rarely improves after the age of twenty-five or thirty. If little has been accomplished by then, little will ever be accomplished.
That belief–that mathematics is a young person’s game–is often banded about although there are many examples of mathematicians making extraordinary contributions as older ages. For example, famous mathematician Sir Michael Atiyah, born in 1929, is working on the Riemann Hypothesis and came very close to solving it last year when he was a spry octogenarian. I could throw in many more examples, such as this article which has a plethora of statements on how women mathematicians are at their prime in their 30s, 40s, and 50s.
The belief that math is a young person’s game may or not be true, but let’s assume for a moment that is true. That if you’re an older student in math, you’re probably not going to rise to the top of your field. Does that belief also extend to Data Science?
When is a Data Scientist “Past their Prime”?
Seeing as data science algorithms weren’t developed until the late 80s, and assuming you got in on the ground floor, the most experience anyone could possibly have as a data scientist is about 30 years. That gives us very few data points on which to base an argument either way, so let’s do what a statistician does when few data points are available, and revert to expert opinion.
David A. Vogan Jr., the chairman of M.I.T.’s math department (as cited in Lila Guterman’s article ), says experience matters in all sciences (other than mathematics, where he believed that experience tends to not be a good thing). “In a lot of the sciences, there’s a tremendous value that comes from experience and building up familiarity with thousands and thousands of complicated special cases.”
Or, there’s this 1946 article which reports the median age when scientists (of any kind) do their best work is 43. That is, half of people on the list did their best work after the age of 43. The list was made up of over 4,000 scientists, some famous (some not so famous). Yes, it’s an older article, but when you take into consideration that the average age of a scientist is rising, it’s still very relevant.
Here’s HuffPost’s Formula for Scientific Excellence: “Scientists are likely to do their best work during the time that they’re most productive, and young people generally tend to be more productive. But if a scientist is more productive in the later years of her career, then she’s most likely to have her best work then.”
How Old is Too Old to Be a Data Scientist?
How old is “too old” to be a data scientist? Assuming you have the skill set, there isn’t an age limit—even if you’re starting from scratch with a degree.
As an example, the age range at the Berkeley School of Information reports that the age range of students in their online data science program is 21 to 67. The average age is 35, which means there are a lot of students in the upper age group. For NCSU’s Master of Science in Analytics, the oldest student is 50.
The Reality Check
That said, it’s time for a reality check. There are several questions you have to ask yourself before you go for that graduate degree. Probably the first is what exactly do you hope to accomplish? If you enter the regular workforce, in a regular job at age 50, that leaves you little time to “rise the ranks.” So if you’re hoping to work your way from intern to CEO in that time frame, it’s probably not going to be possible. On the other hand, if you want to branch out as a freelancer, develop some new algorithms or crunch some data of your own, then the possibilities are definitely there.
Secondly, your financial return on investment is going to be lower than if you were in your 20s. An MS in DS at Northwestern will cost you $54k. Stanford will set you back $15k per quarter (as an undergraduate), and the (relatively cheap) Georgia Tech degree will cost $10k per undergraduate semester–if you’re in state. These costs don’t include room and board, books, or those extra baby sitting fees (or if you’re older, lost time with the grandkids!). Are you going to be able to recoup your investment in your remaining working life? You be the judge.
Alfred Adler. “Mathematics and Creativity.” The New Yorker Magazine, February 19, 1972.