Are you good at math? Like, really good at math? Do you also know Python and, oh yeah, have deep knowledge of a particular industry?
On the off chance that you possess this agglomeration of skills, you might have what it takes to be a data scientist. If so, these are good times. LinkedIn just voted «statistical analysis and data mining» the top skill that got people hired in 2014.
Glassdoor reports that the average salary for a data scientist is $118,709 versus $64,537 for a programmer. A McKinsey study predicts that by 2018, the U.S. could face a shortage of 140,000 to 190,000 «people with deep analytic skills» as well as 1.5 million «managers and analysts with the know-how to use the analysis of big data to make effective decisions.»
The field is so hot right now that Roy Lowrance, the managing director of New York University’s new Center for Data Science program says he thinks it has peaked. «It’s probably in a bubble,» he says. «Anything that gets hot like this can only cool off.» Still, NYU is looking to expand its data science program from 40 students to 60 over the next few years. The current school year won’t be over for another five months and 50% to 75% of its students already have firm job offers.
Why the explosion? Linda Burtch, managing director of Burtch Works, a Chicago-based executive recruiting firm, notes that while tech firms like Google, Amazon, Netflix and Uber have data science groups, the use of such professions is now starting to filter down to non-tech companies like Neiman Marcus, Walmart, Clorox and Gap. «All these are companies looking to hire data scientists,» she says.
The hope is that such professional will unearth new information that will prompt new streams or revenue or let a company streamline its business. Pratt & Whitney, the aerospace manufacturer, now can predict with 97% accuracy when an aircraft engine will need to have maintenance, conceivably helping it run its operations much more efficiently, says Anjul Bhambhri, VP of Big Data at IBM.
Though IBM just released its freemium, cloud-based Watson Analytics program this month, most often data scientists have to create homegrown software programs to analyze unstructured data, which is one reason that programming skills are required.
Lowrance says there are basically three skills that a data scientist needs to possess: math/statistics, computer literacy and knowledge of a particular business domain (like autos, for example.) NYU’s program teaches those so that each area of expertise builds on the other. When you graduate, you’re sort of a jack-of-all-trades for data crunching. «When working on data science projects in coursework they have to do all the jobs,» he says.
Not everyone has to go through a college course to become a data scientist, though. A company called Metis, for instance, started offering a 12-week data science boot camp in September. The program, in New York, costs $14,000 and admission is highly competitive. Metis Cofounder Jason Moss says that about half the students come in with a Master’s or PhD.
Just a couple of weeks after the first boot camp ended in early December, Moss said six of the class’s 15 students had job offers.
«I don’t think it’s a replacement for college,» Moss says of his program. «I think college is about more than the fastest path to getting a job. I also don’t believe that you have to have gone to college to be successful as a data scientist,» he says. «There’s a personality type – innately curious, has grit, wants to figure things out — that does well.»
Anmol Rajpurohit, an independent data scientist and consultant, says being a fast learner is most important attribute for this line of work. «Generic programming skills are a lot more important than being the expert of any particular programming language,» he says. «Living in an age of rapid technology advancement, we see languages quickly becoming obsolete and new languages quickly getting popular. Thus, a fast learner will go a lot farther than an expert.»