If you want a high-paying, dynamic job in an in-demand and rapidly growing field, data science may be worth looking into. According to the U.S. Bureau of Labor Statistics, the job outlook for data scientists is projected to grow 22% from 2020 to 2030, much faster than the average for all occupations. Median pay for the position was $126,830 per year in 2020. People usually enter the field by obtaining a master’s degree in data science.
Who should pursue a master’s degree in data science?BY Rich GrisetJanuary 19, 2022, 8:06 AM
So, what employment opportunities are available for someone with this degree?
“Excuse my New York City vernacular here—anything you damn want. That’s the beauty of a data science degree,” says Eric Bradlow, vice dean of analytics and a professor of statistics and data science, marketing, economics, education, and statistics at the Wharton School of the University of Pennsylvania.
If you’re contemplating a master’s degree in data science, here’s what you need to know:
The difference between data science and data analytics
People often conflate data science with data analytics. While both fields work with data, data analysts look at trends in large data sets to create charts and visual presentations to help organizations make strategic decisions.
Data scientists, on the other hand, design and build new processes for data modeling by using algorithms, prototypes, predictive models, and custom analysis. By asking questions and creating algorithms and statistical models, data scientists work to estimate the unknown, using multiple tools simultaneously to arrange undefined sets of data and build their own automation systems and frameworks. People with data science degrees often branch out into technical roles, including data engineer and data architect.
“Data science has a more technical focus,” says Arthur Spirling, deputy director of New York University’s Center for Data Science and a professor of politics and data science. “Data science is going to train you in cutting edge statistics, machine learning, and programming, and to do those things the second you are out of the program.”
Who is a good candidate for a master’s in data science?
Roughly half of the master’s degree students in NYU’s data science program come straight from obtaining their undergraduate degree; the other half are seeking a career switch or career advancement, according to Spirling. Typically, master’s in data science students have an undergraduate degree in a STEM field, have done some serious computer programming, and have taken one or two computer science courses, as well as rigorous statistics courses.
Still, Spirling says it’s common for people to pursue a master’s in data science who have a non-STEM undergrad degree. Usually, they’ve done corresponding work, such as holding a job where they use general-purpose programming language Python or a domain-specific language like SQL every day.
“We would like, ideally, at least one undergraduate course that involved programming,” Spirling says. “If they don’t have that, it can be acceptable for them to have some evidence of programming in their day-to-day life.”
What career prospects are available for this master’s degree?
According to Bradlow, the sky’s the limit when it comes to data science employment opportunities.
“Everybody needs people that know data science,” Bradlow says, adding that because data science is a core discipline, graduates can work in tech, entertainment, pharmaceuticals, telecom, and as a consultant. “It’s a great set of skills to have as an entrepreneur for startups. Literally, it’s the one job today that’s industry agnostic. It’s recession proof.”
And there are even different career paths within the same company. Graduates with a master’s of data science degree may have to choose whether they want to use their new skills to work with data directly, or become a c-suite executive who understands data, Bradlow says.
What are some misnomers about a master’s of data science degree?
Some people believe that data science is either all computer science or all statistics, when it’s really “a more holistic understanding of problems that you are likely to encounter,” Spirling says.
“Data science is as much an art as a science. A lot of what you’re trying to develop are good intuitions about problems,” Spirling says, adding that data scientists have to understand these problems beyond the raw numbers, such as the social situations, ethical considerations, and market forces that may be at play in a data set.
“At NYU, we have a very rigorous approach to data science, and so you have to be ready to work hard in your master’s,” S.pirling notes. “It’s a serious, graduate-level undertaking, and in that sense many of our courses are closer to Ph.D. level than they are to undergraduate level.”
What’s more, some people don’t realize just how many applications data science can have, including for sports and gambling, according to Bradlow.
“It’s so much fun, because it’s a license to learn about any subject,” he says. “You’re dangerous enough with data that you can really read almost any article in any field and understand what they’re trying to assess.”
Spirling agrees, adding that data science is a diverse discipline that attracts people who believe in helping broader society progress.
“There has never been a better time to get into data science than now,” Spirling says. “Data science is much broader than the core skills that we teach, or that students think they’ll gain. It’s a much larger understanding of the place of data and methods in society.”