Is a Master’s Degree in Data Science Worth It?

Written by Coursera • Updated on

While it's not always necessary to hold a master's in data science, earning an advanced degree does feature several benefits. Learn more about whether it's right for you.

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Data science is a field where advanced knowledge and additional skills training can yield positive returns. While earning a master’s degree in data science comes with certain costs—in terms of both tuition and time—it can be a worthwhile investment when you’re interested in furthering your abilities to work with and parse data. 

In this article, we’ll go over the benefits and costs of earning your master’s degree in data science so you can determine whether it’s the best choice for you. 

Master’s in data science: Pivoting vs. advancing 

Your goal in earning a master’s in data science can be a strong indicator of whether a graduate education is the best course of action. Think about what you hope to get from a master’s program: Are you aiming to change careers or advance in your current one? 

When you’re interested in pivoting to a career in data science, earning a master’s can be useful. Many programs emphasize foundational knowledge, making it possible for students from many different backgrounds to learn about data science. However, some programs may have specific prerequisites you will need to fulfill first. 

On the other hand, if you’ve been working in data science for some time and are now interested in advancing your career, it may be best to find a master’s program that offers an opportunity to concentrate on an area, such as machine learning or bioinformatics. In other words, you may get more out of a program that provides niched concentrations or specializations.

Learn more about whether a master’s degree is worth it for your needs.

6 benefits of earning your master’s degree in data science

Many benefits come with earning your master’s degree in data science:

1. Advanced roles

Becoming a data scientist typically requires a bachelor’s degree. Still, given the demand for this work across sectors, you may find that earning your master’s degree qualifies you for advanced roles that require more in-depth knowledge. 

2. Higher salaries

While earning a master’s degree, in general, has been shown to increase your earning power—a median of $240 more per week in the US compared to bachelor’s degree holders—data science as a field tends to pay more [1]. That means earning a graduate degree may lead to higher salaries because you qualify for senior-level or managerial roles.  

3. In demand

The demand for data scientists is extremely high, according to the US Bureau of Labor Statistics. Data scientist openings are expected to grow 36 percent between 2021 and 2031 [2].

In fact, on Glassdoor’s list of 50 Best Jobs in America, which ranks occupations based on job openings, median base salary, and job satisfaction, two data roles occupy the top ten: data scientist ranks third, while data engineer ranks seventh [3].

4. Resume credential 

A graduate degree can be a strong way to stand out as a job candidate. Fifty-four percent of data scientists hold a bachelor’s degree, while 34 percent hold a master’s, according to Zippia [4]. Not only does a master’s degree add a notable credential to your resume, but it shows the time you’ve committed to furthering your knowledge. 

5. Specialized knowledge

Master’s degree programs tend to be more rigorous—and more focused—than bachelor’s degree programs. Whereas bachelor’s degrees take between four and five years, with half of your coursework dedicated to gaining a general knowledge of many subjects, a master’s degree takes two years and concentrates exclusively on your area. What’s more, you may have the opportunity to specialize in an aspect of data science, such as bioinformatics or big data. 

Earn your Master of Applied Data Science from the University of Michigan in 12 to 36 months, depending on how much time you can dedicate to the program each week.

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6. Network 

Enrolling in a master’s degree program can also be a great way to expand your network, both through the peers you meet and the faculty you work with. 

Learn more: Is a Master’s Degree Worth It?

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Master’s degree in data science costs

The average total cost of a master’s degree is $62,650, according to the Education Data Initiative, though degrees can range anywhere from $30,000 and $120,000 [5].

It takes around two years to earn a master’s degree when you’re able to attend full-time, though a number of online master’s degrees in data science are optimized to take less time (around one year) when you’re able to commit a certain amount of hours per week on your studies. 

What can you do with a master’s in data science? 

With your master’s degree in data science, you can explore a number of data science jobs or continue your educational pursuits by applying for a PhD in data science. Let’s review each option. 

Master’s in data science jobs

The roles below illustrate some of the jobs you can pursue once you’ve earned your master’s degree in data science. Learn more about the kinds of salaries you may qualify for once you earn your master’s degree in data science:

  • Senior data scientist: Working as a data scientist typically requires a bachelor’s degree, but you may qualify for more senior-level roles with a master’s. Data scientists typically design algorithms to collect and interpret data. 

  • Data engineer: A data engineer designs and builds systems to handle a lot of data so that data scientists and data analysts can work with it. 

  • Data architect: A data architect drafts frameworks businesses can use to handle data, often with the goal of making sure it meets any compliance requirements. 

  • Machine learning engineer:Machine learning engineers often sit on data science teams. They design, build, and maintain machine learning algorithms and systems. 

  • Statistician: Statisticians can work for public or private organizations and often look for trends in data by collecting and interpreting it. 

  • SQL developer: This role sits in between software development and database engineering. SQL developers often work to create or maintain SQL-specific databases. 

Learn more: How to Become a Data Scientist

PhD in data science

A PhD in data science is a terminal degree—or the highest academic degree you can earn in a field. Many data science professionals go on to earn their PhD in order to pursue cutting-edge research or teach at the university level. These degrees tend to take around five years to complete, though it can be longer depending on your area of research. 

Other types of education 

Whether you’re interested in beginning or advancing your career in data science, you may find that completing projects, taking individual courses, and other self-guided learning can help you achieve your goals. 

  • Certificates: Earn a professional certificate or a graduate certificate in data science or an area of data science to strengthen your skill set. Certificates typically take one year or less to earn and focus on practical knowledge and skills development. 

  • Courses: You can take massive open online courses (MOOCs), many for free, to acquire or improve your knowledge in data science. 

  • Projects: Find projects to build your skill in a particular area and demonstrate your knowledge. You can find ideas on YouTube or enroll in a Guided Project on Coursera.

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Explore further

Earn your master’s degree from a highly ranked university on Coursera. Explore the University of Michigan’s Master of Applied Data Science, University of Colorado Boulder’s Master of Data Science, Illinois’ Master of Computer Science in Data Science, or the University of London’s MSc in Applied Data Analytics. In many cases, you can enroll in a course before applying for the full program. 

Or check out professional and graduate certificates on Coursera, such as IBM’s Data Science Professional Certificate or the University of Chicago’s Machine Learning for Analytics.

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Article sources

1

US Bureau of Labor Statistics. “Education Pays, https://www.bls.gov/emp/chart-unemployment-earnings-education.htm.” Accessed January 19, 2023. 

Written by Coursera • Updated on

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