Do I need a data science degree? 5 ways a master’s in data science can change your career
Problem-solving has always been one of your strengths. Connecting dots and recognizing anomalies that might confuse others both come naturally. You actually enjoy working through complex challenges, which is why you’re thinking about pursuing a career in data science.
Though you already have an undergraduate degree, you know you need a little more education to stand out as an applicant. There are a few routes you could pursue to help bolster your resume. Should you pick up a few certificates to build your skills? Or is it better to pursue a master’s in data science?
The decision is ultimately yours, but know that occasional courses and “degree boosters” can only get you so far. An M.S. might be more beneficial than you realize. Keep reading to learn more about how a master’s in data science can set you on an upward career trajectory.
5 ways a master’s in data science can transform your career
1. You’ll learn hard skills in multiple disciplines
Read any data scientist job description, and you’ll see a long list of required skills. You need solid foundations in data collection and analysis, computer programming, and more. 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 these competencies typically fall into two categories: statistical skills on the one side and engineering and programming skills on the other.
The benefit of pursuing a graduate program in data science is that you get a multi-disciplinary education. Individual degrees in computer science, engineering, and statistics can certainly be helpful, but they could leave you with educational gaps. The same is true for the narrow focus of one-off certificates.
2. Master’s programs emphasize essential non-technical competencies
As important as it is for professionals in data-focused careers to have a solid foundation in programming languages, data manipulation, and statistical modeling, they also rely on numerous soft skills. An inquisitive mind is one example. And it isn’t just helpful – it’s essential.
“One of the non-technical parts is the curiosity,” Dr. Wu explains. “How does the data turn into a solution?” In some cases, you might not even know exactly which question you’re answering until you get started.
Being able to effectively communicate is another key part of any data science role. It doesn’t do any good if you discover something that could take a business to the next level and no one else understands how to proceed.
“You have to be a good storyteller to whoever’s using your data, whoever’s using your insight created from that data,” Dr. Wu says.
3. You can start building a robust professional network from day one
One of the biggest advantages to a formal education is the ability to start building professional relationships. In a graduate program, your classmates aren’t just classmates, and your teachers aren’t just teachers – they’re all part of your growing professional network. You never know what type of internship and career opportunities could come your way from those interactions.
While the number will differ across professions and geographic regions, there’s evidence to suggest that around 70 percent of people secure employment through their professional networks. Some jobs are never even posted, because someone recommended internally gets the gig first.
4. A master’s degree can open doors in nearly any industry
Many professionals advocate for learning on the job, arguing you gain the most knowledge by actually getting your hands dirty. This isn’t practical for those interested in data science roles. It’s difficult to figure out how everything connects without the guidance and structure of a formal program. Candidates without a graduate degree who actually meet employer expectations are rare.
“Finding a good candidate with strengths in all those areas is very hard to find,” Dr. Wu acknowledges. “Some people call it a unicorn.”
It’s also important to note that nearly every industry is looking to hire employees who can work with big data. A report from the US Bureau of Labor Statistics (BLS) highlights some examples, including finance, healthcare, and social networking. There’s no sign of the trend slowing, either.
“I think we’ll see more and more machine learning used by regular businesses,” Dr. Wu hypothesizes. “All these companies are looking for some type of data scientist who can write code that serves the business's purpose.”
5. You’ll be ready to hit the ground running soon after graduating
Graduate programs clearly aim to train students in all the core areas needed to pursue a career in data science. But do new graduates really stand a chance against candidates who already have a few years of experience?
Dr. Wu thinks recent grads could actually have a leg up. Quality programs will incorporate some sort of capstone project that requires students to apply their theoretical knowledge in ways that are applicable to businesses.
“They could have some advantages over people already working in the industry, but who might be limited in certain areas,” Dr. Wu points out.
Analyze your options
You can see that there are a lot of perks to obtaining a master’s degree in data science. While a quick coding course or data science certification may be appealing, it may leave you with an educational gap. A quality graduate program will be able to help you develop the robust skillset you need to pursue an exciting career as a data scientist.