Data scientists use technology to glean insights from large amounts of data they collect. It’s a field that requires statistics, quantitative reasoning and computer programming skills. On top of all that, you need to be a good communicator so you can report your research findings and explain how they address a larger question you’re trying to answer.

“A data scientist really is a scientist at heart,” says Scott Beliveau, chief of the enterprise advanced analytics branch within the U.S. Patent and Trademark Office’s Office of the Chief Technology Officer. “But rather than using chemicals or other things, a data scientist uses data – numbers, zeros, sometimes it’s textual information – to try and solve and answer problems.”

While data science is still a new career field, employers are increasingly recognizing the value of professionals with this expertise. Today, you’ll find data scientists working at a range of organizations, including tech startups, government agencies, large companies and research institutions.

Chris Holdgraf, director of the nonprofit International Interactive Computing Collaboration, which helps researchers and educators run data science infrastructure in the cloud, started learning about data science as a graduate student at University of California, Berkeley. With the help of data whizzes at the university, he taught himself coding, analytics and other skills that he thought would help him conduct neuroscience research.

“By the end of my Ph.D., I had inadvertently built a pretty nice set of (data science) skills,” he says.

While serving as a fellow at the university’s Berkeley Institute for Data Science, Holdgraf collected data from electrodes placed directly onto the surface of patients’ brains to learn more about how they hear. Feeding the data into computer programs he created, Holdgraf ran experiments, made observations and drew conclusions about the brain’s response to different types of sounds. Then, he wrote papers about the results.

“Being a data scientist gives me the flexibility to work with people with many different kinds of backgrounds and to work on problems that are meaningful to lots of different communities, which I really enjoy,” he says. The Bureau of Labor Statistics projects 35.2% employment growth for data scientists between 2022 and 2032. In that period, an estimated 59,400 jobs should open up.

Data scientists may come to the field from a variety of disciplines, including engineering, math, computer science, business, and natural or social sciences. Recently, colleges have started to offer data science certificates and degree programs, and there are also boot camps and online programs that teach data science skills.

Because the title “data scientist” can refer to many types of jobs in a range of industries, the skills you need may vary.

“The most common denominators,” Holdgraf says, “are courses in statistics and computational analysis – including things like machine learning, for example – but that have a very strong focus on the kind of pragmatic, hands-on and practical, getting-stuff-done version of those concepts.”

Indeed, aspiring data scientists can’t be afraid of numbers, though, “you don’t need to be a mathematician” and must be comfortable working with a computer, Holdgraf says.

Communication skills are also vital, he says. “You can run all the analyses that you want to, but in the end, it’s only going to be useful if you can turn that into something that’s going to be useful to another human being.”