Data Science: Grad School or Certification?

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Table of Contents

  1. Introduction
  2. Graduate School
  3. Certification
  4. Summary
  5. References


This article is intended for people who already have an undergraduate degree looking to switch to data science, or people who are interested in knowing the benefits of each. With data science, machine learning, artificial intelligence, and deep learning emerging as popular jobs in nearly any industry, graduate programs have emerged just as much to keep up with the demand. However, there is another option that may be more enticing for a variety of reasons, which is a certificate in data science. Keep on reading below if you would like to know the difference between each, what to expect for each, and the benefits of both graduate school and certification in data science.

Graduate School

Photo by Priscilla Du Preez on Unsplash [2].

Graduate school can come in a variety of forms, whether that be in-person teaching to fully online, or a mixture of both. This option requires more commitment, but in turn, might inspire you to then finish and reap its many benefits.

Here are benefits of choosing graduate school for your data science education:

  • You can meet your classmates in person, in most programs (even online programs offer a few in-person meetings).
  • You can offer capstone/final projects that are much more involved, where you can also present at summits or conferences in person, ultimately allowing you to network with reputable companies.
  • They’re expensive — while this is a disadvantage, as we will include below, it can also be looked at as an advantage, because you are making this large, monetary commitment that will, to be frank, force you to study so you are not wasteful.
  • Built-in trust — graduate schools in data science often stem from reputable universities that have existed for a long time, so you can trust their program structure is competitive and beneficial.
  • To some companies, having a graduate degree in data science is preferred over certifications.

Here are some disadvantages of choosing graduate school:

  • It’s more expensive than most certifications.
  • It takes much longer than most certifications.
  • It can be overwhelming to have multiple courses per week on top of your current 9–5 job.
  • The curriculum can sometimes lag behind data science trends.
  • It can be too general sometimes.
  • Sometimes you have to wait for the program to start before you want to.

As you can see, there are many benefits to obtaining your data science degree from a graduate school. While there are some disadvantages, it is ultimately up to you to decide if they outweigh the pros or not.


Photo by Avel Chuklanov on Unsplash [3].

Certification is a little tricky. It can mean many certifications, or one, and is often certified by the company that made the certification. However, there are still countless reputable certifications out there, and over time, they are seen as ways to land a job in data science.

Here are benefits of choosing certifcation for your data science education:

  • It can be much quicker than grad school, perhaps weeks/months versus years.
  • It tends to be much cheaper.
  • Because the programs are shorter, the curriculum can be changed with the times, meaning, unlike grad schools, certifications can quickly redesign their courses for trending data science packages and machine learning algorithms like CatBoost.
  • It offers more specialization, for example, you can choose a certificate in only gradient boosting algorithms versus in graduate school, it will tend to simply have a general course over algorithms.
  • They are more flexible, you can build your own program, choosing multiple certifications with courses you really enjoy.
  • You can skip courses that you do not need — that some graduate schools include and require like intro to SQL, intro to coding, intro to statistics, if you know these already, you may be wasting a whole entire semester of your time.

Here are some disadvantages of choosing certfications school:

  • They can sometimes be less reputable than graduate schools.
  • It’s hard to trust if the certificate is actually teaching you the correct information, there are much more certificates than grad schools in data science, so it can be overwhelming to know which one(s) to choose to enroll in.
  • It can be more difficult to know when you have reached ‘full education’, like where in grad school there is a clear end to your main studies (of course, you can always learn, but for your initial exposure to data science to when you land a job, it is stressful to not know when you are ready with certifications).

After writing these advantages, I surprised myself with how much certification has to offer. Of course, there are still some disadvantages. Once again, ultimately, it is up to you to weigh the pros and cons of each, and each specific graduate school program and certificate(s).


I hope this article sheds some light on what makes these two education options different, as well as the advantages and disadvantages of both. Both are amazing options, and as long as you are learning and taking the time to build up your knowledge and experience in data science, you will succeed.

To summarize, here are the general benefits of each option:

Graduate School: reputable, networking, and trustworthyCertifications: flexible, quicker, and cheaper 

I hope you found my article both interesting and useful. Please feel free to comment down below if you agree or disagree with these pros and cons of each respective education option in data science. What other advantages and disadvantages can you think of? In my experience, I chose the graduate school route, I also tried the certification route, and while I still learned, I found it hard to focus and keep myself accountable on completing X amount of certifications to reach the same level of graduate-level education. But, that is me, and you may be very well different — so I hope that what I discussed above can give you some information for your decision.

Please feel free to check out my profile and other articles, as well as reach out to me on LinkedIn.


[1] Photo by Vasily Koloda on Unsplash, (2018)

[2] Photo by Priscilla Du Preez on Unsplash, (2018)

[3] Photo by Avel Chuklanov on Unsplash, (2019)


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