Why employers are looking for applicants with a master’s in data science
Applying for a job can feel a little like you’re stepping onto a path that leads nowhere. This is especially true for those pursuing data science roles. You could have a bachelor’s in statistics, solid programming skills, and relevant work experience, but still struggle to land interviews. Why is this?
Many employers have a strict list of qualifications candidates have to meet before they’ll even be considered. You might be surprised to hear that a master’s degree in data science or a related field is often a requirement.
According to a recent report from Burning Glass Technologies, 39 percent of data scientist and advanced analyst roles require candidates have a master’s degree or higher. One survey from Burtch Works Executive Recruiting even suggests as many as 88 percent of data scientists have at least a master’s degree.
But why does the degree matter so much? We reached out to an expert to learn a little more about the value in completing a data science education. It turns out a master’s, particularly one in data science, offers much more than a line on your resume.
3 reasons employers look for candidates with a master’s in data science
Though employers filling big data roles are often willing to consider candidates who have an M.S. in several fields, such as engineering or computer programming, data science is particularly appealing. Let’s take a closer look at why this particular degree stands out.
1. Candidates without an M.S. in data science typically lack certain skills
Data scientists need to draw on a pretty wide variety of skills to be successful in their roles. The exact breakdown varies depending on the industry, but Dr. Daniel Wu, Assistant Professor of Computer and Information Sciences and Program Coordinator for the Master of Science in Data Science at Cabrini University, says the most obvious competencies are statistical skills as well as programming and engineering skills. Hiring managers are clearly struggling to find applicants who check all the boxes, and the same Burning Glass Technologies report indicates these positions remain unfilled for six days longer than the market average.
But technical competencies are actually just one part of the picture. There’s also a third piece ‒ communication skills.
“That third one is non-technical, and a lot of people actually have that part omitted,” Dr. Wu says.
The reason data scientists need communication skills is that their jobs require conveying information to colleagues. You need to spell out what the data means and how you can use your insights to work toward solving a problem. Though job seekers can often build up their resumes with certificates or single courses that focus on some of the skills they need, it’s likely they’ll still have gaps.
“Finding a good candidate with strengths in all those three areas is very hard,” Dr. Wu says. “Hopefully, a master’s can help students develop all these areas.”
2. Graduate programs teach students how to connect everything they’ve learned
More than anything, a data science graduate program teaches students how to use everything they’ve learned in conjunction with everything they already know. There’s a great deal of uncertainty in data science. You need to be able to wield multiple tools in your toolbox at the same time in order to eventually come up with something concrete.
Dr. Wu says artificial intelligence (A.I.) is a good example of an area that presents challenges since it’s still so new. Candidates with a formal data science education may be better positioned to implement A.I. in business environments.
“A master’s program can give you a more systematic training in how to apply theoretical aspects to practical use,” Dr. Wu explains.
So, how does a master’s program help you understand how everything fits together? It progressively builds your skills. A good data science program will require that you draw on what you learned during your first term when you start your second term, and so on. Developing that holistic knowledge becomes incredibly important when you’re on the job.
“You could derive a lot of different conclusions from the same set of data,” Dr. Wu notes. This is why well-trained data scientists are in high demand. They can navigate the gray areas. “You need to try to reduce all that uncertainty and then arrive at something that could really help businesses,” he says.
3. Candidates with a master’s already have relevant experience
Employers also like to see some experience, even for more junior roles. Completing an internship or capstone project as part of a graduate program demonstrates that you’ve done relevant work already. Dr. Wu explains that his vision for the M.S. in Data Science capstone project at Cabrini University involves working with local businesses to identify real-world problems that students can help work toward solving.
“That approach, I feel, is a distinct path for the student to get training that can be applicable right after graduation,” he explains.
Gaining an understanding of how to take on complex problems while still in school means you could gain the attention of employers soon after graduation as well. And nearly every industry is eager to add data scientists to their team. With a master’s in data science, you can pursue a career in anything from information technology to health care.
Stand out in the job market
A master’s in data science is clearly something hiring managers want to see on your resume. Obtaining your M.S. shows potential employers that you have ample training in each of the skills you’ll need to succeed in data science or a related profession – and the desire to be at the front of the field. It could be the one extra credential that gives you an edge.
Learn more about how a Master of Science in Data Science can set you on the path to becoming a key player in a growing field.