“I keep saying that the sexy job in the next 10 years will be statisticians,” said Hal Varian, chief economist at Google. “And I’m not kidding.”
It’s true, nothing is hotter right now than statistics. Reading about the new importance of statistics made me wonder—how important is statistics in IT? How important is math in general? Does the average programmer benefit from knowing calculus?
According to Python expert Al Sweigart, the average programmer needs very little math to get by. He lists a few exceptions, and his exceptions are interesting. Games involving 3D require some trigonometry and linear algebra. Games that involve physics, like Angry Birds, require some knowledge of physics equations (who knew?) For cryptography, some knowledge of prime numbers comes in handy. Sweigart says there are libraries with all these functions, so even if you’re a games programmer you can use the libraries rather than learning the math.
The importance of math in programming is an evergreen topic on programmer forums. The consensus—if it can be called a consensus—seems to be that the kind of logical thinking that math requires is critical in programming too, but that learning any particular math discipline probably isn’t necessary. But if you want to be a Mozart among programmers, math probably isn’t optional.
For the Mozarts of the computing world, the white-hot field right now is data science, and data science definitely requires math. The problem, for anyone interested in becoming a data scientist, is that no one seems to know exactly how to get there. Harvard Business Review described data science as “The Sexiest Job of the 21st Century,” but at the time the article appeared, in October of 2012, there was no university in the U.S. offering a degree in this sexiest of jobs. The term “data scientist” was coined as recently as 2008, and just what constitutes training for a data scientist still seems to be something of a black box.
Even if the attributes of data scientists aren’t set in stone, one thing is certain—no math field is too esoteric for data science. Data scientists come from backgrounds like astrophysics and systems biology, and an article in InformationWeek speculates that marine biologists and baseball statisticians would make great data scientists: all fields that hardly go easy on the math.
How do you go from being an astrophysicist to being a data scientist? Currently there are only a few bridges between a doctoral degree and a data scientist career, such as the Insight Data Fellows Program. Douglas Mason, who now works as a data scientist at Twitter, describes some the essentials for making the transition in an Insight blog, and a lot of the essentials involve math. Statistics, including regression types. Recursive programming. Calculating expectation values in combinatorics problems (I didn’t have even the vaguest idea what this meant before I googled it, and I can’t say I understand it much better now).
A few firms, such as Mu Sigma, are offering data scientists as consultants, but data scientists are still difficult—and expensive—to find. For any geniuses out there who love computer science and love math, data science is career field worth checking out, though how to study for it is still anyone’s guess.
What do you think? What role should math play in a career in IT?
Lani Carroll lives in Colorado Springs with her bees, chickens, and horses. She can be found at her Google+ Profile.