How to decide if a data science degree is worth it, and choose the right program

Data scientists are in demand, but a master's degree in the field may not open as many doors as you think.

Is a data science degree worth it? Data scientists are in demand, but a master's degree in the field may not open as many doors as you think.

Few careers have experienced the same amount of hype as data scientist in recent years. Named the best job in America by Glassdoor for the last four consecutive years, data scientists are promised a plethora of job openings and high starting salaries, leading to increased interest in the field from both those entering college and those already in the workforce.

With soaring demand for data scientists in the enterprise, a number of master's programs in data science, business analytics, information systems, and other related fields are springing up across the US, promising to teach the skills needed to glean business insights from data and to help fill talent gaps.

"Data science is something that is not just a temporary blip, but something that is going to be needed a lot and for a long time," said Andrea Danyluk, a professor of computer science at Williams College and co-chair of the Association for Computing Machinery's taskforce on data science. "Colleges and universities are beginning to understand the need for data scientists, and are beginning to develop programs."

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Many of these new programs are still in the process of creating the proper curriculum that blends math, statistics, and computer science—and there is a large range in terms of program quality, depth, and breadth of knowledge offered, Danyluk said. They also often require an investment of at least one to two years for a master's program, and can cost thousands of dollars.

Should you get a data science degree?

The best path to becoming a data scientist depends on an individual's background, Danyluk said. "The good news is that there are more academic programs developing now that are allowing people to fill in the gaps in terms of process and programs," she added.

Many people currently working in data science come from backgrounds in math, statistics, or computer science. The term "data scientist" is relatively new, and has led to some of the hiring shortages, said Meta S. Brown, business analytics consultant and author of Data Mining for Dummies.

"We've pushed the idea that data scientists are somehow different from any analyst who ever existed before," Brown said. "A lot of organizations are imagining that there's something that is unrealistic that can solve all their problems."

Many data science master's programs teach students the value of analytics, and help them become more educated in the subject, Brown said. However, some of these programs, particularly the newest ones, may risk overpromising and under-delivering on future employment, she added.

SEE: Big data policy (Tech Pro Research)

"My concern about the explosion in data science programs is that some of those graduates will get jobs they like very much that pay them very well," Brown said. "But I don't believe that all graduates of all programs are going to be so lucky. The only way that an individual can know what the prospects are for a particular graduate program is by just speaking to hiring managers and discussing not only the program, but their own background."

Before entering a master's program, potential data scientists should ensure that they are actually interested in what the work would entail, Brown said. "Many of the people who say they want to go into this haven't had enough involvement with it to know that," she added. A student may want to take a statistics class as a starting point, while a professional may want to find an opportunity to work with data in their current job to gain both exposure and experience, Brown recommended.

"If you love it and have the money and the time to get another degree, I never stand in the way of a person wanting to be educated," Brown said. "I just think it's very important to be realistic about what you're investing, and what you're getting in return."

In particular, those who want to go into data science primarily for the high salary should find a way to ease in outside of a traditional program, Brown said.

"There are so many talented people out there who have valuable experience. The most valuable thing that they can do for society is not throw that away and be a data scientist," Brown said. "It is to bring these meaningful elements from statistics and modern technology into their profession. And I think that many people will get good professional returns in that."

How to choose a data science program

Those interested in moving forward in applying to a data science degree program should consider the following questions:

  • What pathway do you want to take?

If you do want to move forward in applying to a data science degree program, you must first decide which pathway you want: Data science, business analytics, or something else, said Kristen Sosulski, clinical associate professor of information, operations, and management sciences in the Leonard N. Stern School of Business at New York University, and author of Data Visualization Made Simple.

"The power of a master's or another degree in business analytics is you're setting pathways for yourself to be a leader in that field," Sosulski said. "That's very powerful for someone returning to school looking to make a clear change."

  • What is the school's reputation?

If a data science or related program is new, potential students should consider the reputation of the institution as a whole, particularly its math and computer science programs, Danyluk said.

"Data scientist as a position requires a lot of breadth and depth in a number of important areas," Danyluk said. "It carries with it a huge responsibility. So the more background and the deeper the background somebody has, the better."

  • How comprehensive is the curriculum?

If you are able to get a full undergraduate curriculum in data science that includes computational, statistical, and professional practice aspects, including ethics and legal requirements, that will probably be more comprehensive than a certificate or even a master's program, Danyluk said.

The curriculum should also focus on teaching you how to communicate data findings with the business, as this is a key element of the job that hiring managers seek out, she added.

  • What have past graduates gone on to do?

Assuming at least one class has already graduated from the program, or from related programs, find out what kinds of jobs graduates now hold, and how many are working in the field, Danyluk said.

  • Are there networking opportunities?

When selecting a program, ensure that there are networking opportunities and a strong alumni network built into the program and continuing after graduation, as this will ultimately help you get a job in the field, Sosulski said.

  • Are there opportunities to do projects and build your portfolio as part of the program?

"You would never want to walk away with just being able to show a few homework assignments," Sosulski said. "You want to make sure there's a robust capstone project at the end."

Hiring managers will be looking for a rich set of skills like Python, R, and SQL. "Being able to communicate the results of your findings to an audience of non-data scientists is a critical skill," Sosulski said. "You want to make sure that comes across in your resume and also those examples that you show us are your portfolio."

  • Do employers value the program and the skills it teaches?

People exploring programs should reach out to potential employers to find out whether they value the program, Brown said.

"If you hope to have a career in data science, the first thing you need to do is make sure you actually understand what that is, what it is you want to do for your work, and why it's important to you," Brown said. "And then you need to get out and talk to real employers. You need to hear from real hiring managers what they are and are not going to pay for.

Ultimately, the potential future data scientist is in the driver's seat when it comes to choosing the best education, Sosulski said.

"There are a plethora of opportunities for you to develop your skills in data science at varying levels," Sosulski said. However, that doesn't mean that it's always worth it to go with the least expensive option, she added. "You want to make sure that you are using your time wisely and out of every input or out of every course you take, you want to make sure that you're getting something that then you can add to your portfolio or your resume in terms of a skill, a certificate, a badge—something that propels you forward," she added.

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