Data Science

Data Science Master’s vs. Certificate Programs

Data Science Master’s vs. Certificate Programs
While earning a data science master’s may not be the sole path to a successful career in this field, these studies strongly indicate that possessing this advanced degree is definitely advantageous—and expected and/or required in many upper-level data science positions. Image from Pexels
Lucien Formichella profile
Lucien Formichella August 20, 2021

If you're a data science professional considering earning your master’s, you may be tempted to look at cheaper and faster certificate programs. But how do they really stack up against master’s programs and will they help advance your career?

Data Science Programs You Should Consider

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If you’re interested in earning a data science master’s, you’re already aware that this is a rapidly expanding field with numerous job opportunities and a median annual salary of $ 111,490. Yet, even though you know that obtaining this degree is an excellent way to advance your data science career, you may be hesitant about the cost, time, and effort involved—and are considering a faster and cheaper certificate program.

This article on data science master’s vs. certificate programs examines the pros and cons of each option and covers subjects like:

  • Which is better: a data science master’s or certificate (or both)?
  • What is a data science master’s degree?
  • Best programs to earn a data science master’s?

Which is better: a data science master’s or certificate (or both)?

A recent survey by Burtch Works found that 50% of the data science professionals had earned their master’s, and a study conducted by Stitch several years ago revealed that around 40% of data science professionals in both senior and chief leadership roles held master’s degrees. While earning a data science master’s may not be the sole path to a successful career in this field, these studies strongly indicate that possessing this advanced degree is definitely advantageous—and expected and/or required in many upper-level data science positions.

If you’re applying for an entry-level data science job, earning a certificate can be a good way to demonstrate to a prospective employer that you have some data science skills. Likewise, if you’re in a lower-level data science role and want to acquire a specific new skill that will help you become better at your job and be seen as a more valuable asset to your employer, a data science professional certificate is a good move. However, these are short-term career strategies (with short-term payoffs).

Below, you’ll find the pros and cons of both data science master’s and certificate programs:

Pros of a data science master’s degree

  • They are all-encompassing: A data science master’s will provide you an education that is both broad and deep, as well as prepare you for a rewarding career in this field.
  • They are a known quantity: If you earned your degree from a top data science program, like those offered by the Stevens Institute of Technology, Tufts University, or the University of Virginia, an employer knows that you have been equipped with the knowledge and expertise to do your job effectively and well.
  • They offer guided learning: You will have many opportunities to collaborate with professors and fellow students in your data science courses, especially during thesis, capstone, and group projects.
  • They can connect you to great jobs: Top data science programs often provide excellent networking opportunities that can help you secure your ideal data science position.

Cons of a data science master’s degree

  • They are expensive: Master’s programs typically cost over $40,000, with tuition at top schools considerably higher (though the cost of this degree will be offset by the higher future earnings that you’ll command for the rest of your career).
  • They are time-consuming: It generally takes around two years to complete a full-time master’s program—and longer if you’re enrolled in a part-time course of study (though the investment of a few year’s time now will pay off for decades).
  • They are comprehensive: If you’re only looking to acquire skills in one limited area, you don’t want to spend the time and money enrolling in a master’s program.

Pros of a data science certificate program

  • They are inexpensive: Most programs cost just a few thousand dollars to complete—though they can run more than $10,000.
    They are quick: Though some certificates can take more than a year, most take just a few months to complete.
  • They sometimes count towards a master’s: Credits earned through some certificate programs can transfer if you decide to pursue your data science master’s.
  • They offer targeted learning: Certificates allow you to focus on one specific subject, like big data or machine learning.
  • They can be offered by top programs: Many top schools offer data science certificate programs, including Harvard University, Northwestern University, and University of Michigan.

Cons of a data science certificate program

  • They aren’t equal to a master’s: It’s apples and oranges. Certificate programs are nowhere near as comprehensive and rigorous as a master’s, so their importance and value to employers (and your career) is significantly less.
  • They will leave gaps in your knowledge: Even if you earn multiple certificates in data science, you will never achieve the breadth and depth of learning found in the curriculum of a degree program.
    They offer less support: Since certificates are fast, one-off affairs, the number of resources provided to students outside the class is minimal.
  • They can be riskier: The quality-control of some certificate programs is sometimes questionable, so their perceived value among both participants and hiring managers can be low.

“I’m Interested in Data Science!”

University and Program Name Learn More

What is a data science master’s degree?

Simply put, data science is a computer science discipline that focuses on data. Data science professionals utilize advanced data analysis tools, such as machine learning, data visualization, artificial intelligence, and high-level statistical analysis to collect and interpret data sets.

Are there prerequisites for applying to a data science master’s?

If you’re applying to a data science master’s program, it’s beneficial to have at least two years of relevant work experience in data management, business analytics, or business intelligence. While there are programs that accept inexperienced applicants—those with a bachelor’s degree in an unrelated subject looking for a career change—it’s likely you’ll be required to complete bridge coursework, in addition to your regular course of study.

A basic understanding of one or more of the top computer programming languages (Python programming, R programming, SQL, or Java) will bolster your application, and knowing how to manage data structures is helpful. In addition to specific programming skills, many data science program applicants have worked as data analysts and possess the skills and experience necessary for that role.

Overall, applying to a data science program is similar to other master’s programs, including submitting personal statements, letters of recommendation, and undergraduate transcripts. You’ll also likely send in GRE scores, though many programs no longer require them, especially those offered online. Even highly regarded schools like Tufts University are loosening their standardized test requirements in favor of a more holistic approach to admissions, favoring related work experience over test scores.

What do you learn in a data science master’s degree program?

There’s no universal curriculum for a data science master’s. The University of Virginia “draws from multiple disciplines to give students a comprehensive and holistic approach to data science.” Common elective and required courses include:

  • Applied statistics
  • Cloud computing
  • Data administration
  • Data mining
  • Data visualization
  • Data warehousing
  • Linear algebra
  • Natural language processing
  • Network design
  • Neural networks
  • Predictive modeling (including regression analysis and forecasting)
  • Project management
  • Software engineering

Not all relevant data science programs offer each of these courses, and not all of them are titled Master of Science in Data Science. Other related degree titles include:

  • Master of Applied Data Science
  • Master of Computer Science in Data Science
  • Master of Professional Studies in Data Analytics
  • Master of Science in Data Analytics
  • Master of Science in Data Science and Analytics

Many, but not all, programs allow for specialization. Common specializations include:

Artificial intelligence (AI)

Data scientists utilize AI, which is capable of processing enormous amounts of data, to improve efficiency, spot trends, and decipher data.

Big data/data analytics

It’s hard to be a data scientist and not specialize in big data or data analytics. Big data courses teach data wrangling techniques to handle the massive and ever-increasing volume of data.


Bioinformatics is the intersection of healthcare and data. According to the National Human Genome Research Institute, it “is a subdiscipline of biology and computer science concerned with the acquisition, storage, analysis, and dissemination of biological data, most often DNA and amino acid sequences.”

Computational finance

Computational finance “uses the tools of mathematics, statistics, and computing to solve problems in finance,” according to Carnegie Mellon.

Cyber security

A large and growing discipline in its own right, cyber security involves applying computer science principles to keep governments, companies, and individuals safe. You may be able to specialize in this field through a data science program, but there also are several excellent cyber security master’s programs to choose from.

Data engineering

Data engineering is the movement, storage, exploration, and transformation of data. Data engineers need excellent warehousing skills and work to provide data scientists with good, viable information.

Machine learning

Data scientists who specialize in machine learning are in high demand, as this is one of the most technically demanding roles in data science. Southern Methodist University students who complete the specialization “learn to utilize advanced computational algorithms needed to build platforms that expand the boundaries of machine cognitive function to provide solutions, advance automation and evolve processes.”

Data science master’s career options (who’s hiring?)

LinkedIn consistently ranks data science as one of the fastest-growing fields. Hiring in this profession has increased by 46 percent since 2019. Data scientists are needed by tech giants like Microsoft and IBM to government agencies to healthcare organizations. Wherever there is data—and data is everywhere—data scientists are required.

The top jobs that you can find with a master’s in data science include:

  • AI researcher
  • Applications architect
  • Big data solutions architect
  • Data engineer
  • Data strategist
  • Data warehouse engineer
  • Enterprise architect
  • Machine learning engineer

Best programs to earn a data science master’s

The top data science master’s programs include:

  • Carnegie Mellon University’s School of Computer Science
  • Columbia University’s Data Science Institute
  • New York University’s Center for Data Science
  • Stevens Institute of Technology
  • Tufts University
  • University of Rochester’s Goergen Institute for Data Science
  • University of Virginia’s School of Data Science
  • University of Washington’s College of Science & Engineering

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About the Editor

Tom Meltzer spent over 20 years writing and teaching for The Princeton Review, where he was lead author of the company's popular guide to colleges, before joining Noodle.

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