The field may be relatively new and occasionally vaguely defined, but one thing’s for sure — it’s also growing.
That’s becaues, in the digital age, the amount of data available is only going to continue to grow exponentially. And we’ll need data scientists to set up the systems required to glean insight from all that information.
“In a lot of these areas, we’re still like a baby in the crib,” Data Science Association founder and president Michael Walker told Business Insider. “We’re still crawling. Maybe we’re standing up. I don’t even think we’re walking or running yet.”
Business Insider spoke with several people working in the field of data science to get a sense of what it’s like to have the best job in America right now.
Here’s what the data scientists had to say:
Looking at Glassdoor's rankings, it's not hard to see why data scientists came out on top.
The occupation boasts a $US110,000 median base salary. And it's in high demand -- with Glassdoor listing thousands of job openings at a time.
Other factors that went into Glassdoor's methodology to find the best jobs in America include overall job score and job satisfaction scores. Data scientists earned 4.4 out of 5 on job satisfaction and 4.8 out of 5 on overall job score.
'I enjoy seeing the final product. The result of our work and the result of our algorithms are often things that are really, really cool,' Lotem Peled, head data scientist at sales intelligence platform Gong.io, told Business Insider.
Data science may be popular at the moment, but many of the data scientists Business Insider spoke with said the field is still somewhat vaguely defined.
'Data science is an activity more than it is a job title,' Kevin Safford, lead data scientist at data management platform Umbel, told Business Insider. 'To accomplish this activity, you usually need a team of people with a range of different backgrounds and expertise. No one person is going to be expert at all the underlying skills necessary for a successful data science initiative.'
Data scientists tend to work with data analytics programs and algorithms to extract meaning from data, often using high performance computers to do so.
'What a real data scientist does is take data -- it can be large, small, from a variety of sources -- and interpret it for your client or your employer,' Walker said.
But don't mistake data scientists for similarly named occupations, like data analysts and business analysts.
'I think a common component to understanding what data science is and how it's distinct from other similar kinds of professions is really, when thinking about it, to highlight and underline the word science,' Safford said. 'It's about applying the principles about the scientific method toward solving business problems.'
There's really no typical day in data science.
'You have to find your own style,' Peled said. 'You can be a person who drills down on a single project until you solve it, and only then you go back up for air. Or you can be the kind of person who runs from project to project and does things more horizontally.'
Many of the data scientists Business Insider spoke with mentioned that their drive to solve problems often allows them to blur the lines between work and rest.
'I don't think I'm ever off,' Ryan McCready, data science at web design company Venngage, told Business Insider. 'It's no big deal to get pinged by one of my coworkers late at night and then jump into a piece of data. It's like, 'Oh, this is very cool, I'll jump into it.''
One major task for all data scientists involves taking the time to find solid data sets and formulate appropriate scientific questions.
'We do spend a lot of time making sure we have good data -- well-prepared data -- so we get very good results from our algorithms,' Adam Estrada, director of analytics solutions at space imagery vendor DigitalGlobe, told Business Insider.
A love of maths and science and a knack for solving problems are must-haves for data scientists. But what specific technical skills are in-demand in the field?
Glassdoor recently looked at 10,000 data scientist job postings from January to July 2017 to find out the most frequently listed data science skills. It found employers want candidates well-versed in Python, R, SQL, Hadoop, Java, SAS, Spark, Matlab, Hive, and Tableau.
Walker said other abilities, like sharp critical thinking and a recognition of internal biases, are also important for prospective data scientists to hone.
Estrada added that the work tends to be quite collaborative and a great fit for people who tend to fit the personality type of 'extroverted introvert.'
Because the field is still in the process of becoming established, it's not surprising there are some misunderstandings out there about data science.
'I think a lot of people think data science is 90% modelling, 10% data mining and cleaning,' Vivian Zhang, founder and CTO of the NYC Data Science Academy, told Business Insider. 'But actually it's the reverse.'
People interested in entering the field may also be surprised by the amount of coding the job involves, according to Peled and Estrada.
'You have to be a really good programmer,' Peled said. 'I think another misconception is people think they don't have to know the maths behind the algorithms.'
Non-data-scientists can harbour some pretty over-the-top expectations about the field.
'It's not magic and you can't just have some kind of black box algorithm that applies arbitrarily good answers to arbitrarily weird questions,' Safford said. 'It's very much a rigorous process of hypothesis, data exploration, analysis, and then whatever the result is, that's the result.'
Walker said it's important people realise that, while data science can provide a competitive edge in the business world, it's unrealistic to expect instant results.
'The road there is like the road to climbing Everest,' he said. 'It's tough. If you want to climb Mount Everest, you can do it. But you're going to need to start training every day.'
Data science programs may be hard to come by at some universities, so Peled said it's important to be intentional about the maths, science, and computer science classes you take.
Many data scientists hold master's degrees and PhDs in some scientific field. Safford and Walker said data scientists should have graduate-level experience, although they added there are always exceptions to the rule.
'Data science is a wonderful field, but you can't just take a few-month bootcamp and expect to come out and be a data scientist,' Walker said. 'Maybe you'll be a good analyst. It takes graduate-school-level training.'
Safford added it can be hard to find 'entry-level' data science positions straight out of school.
You can boost your chances by gaining experience as quickly as possible and showcasing your work online.
'You've got numbers out there about everything,' McCready said. 'Figure out what your passion is. Start making a name for yourself as early as you can.'
Estrada said forums like GitHub are a great way to showcase your coding abilities. Safford added that blogging about your personal data projects is also a great way of demonstrating to potential employers that you can tell insightful stories about the data you're working with.
'There's really no better way to learn something than to solve a problem,' Estrada said. 'Posting your code up in an open forum where other people can see it really helps you gain traction, not only for yourself but for other people who are interested in working with you and hiring you.'
So what's next for data science?
While it's certainly attracting some positive press at the moment, the data scientists Business Insider spoke with mostly agreed that the interest is far from peaked.
'I think data science is booming, but it's still in its infant stage,' Zhang said. 'Everyone is saying, 'Oh, data science is so hot.' But it's just the beginning. It's like in 1997, when everyone was talking about software engineering.'
Walker said it's crucial for data scientists to proactively professionalize the field in order to prevent untrained data workers from potentially cause problems by misinterpreting data.
He also said steps must be taken for data scientists to collectively strive to focus on positive, human-centric goals.
'We're creating a better future,' he said. 'That's what I'm so excited about. But I'm also a realist and I realise it could go the other way. But I think if we do this right, we're going to significantly improve the lives of the majority of people on Earth.'
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