When is a master’s degree the right way into Data Science?

You are a student finishing their bachelor’s degree and unsure of what to do next. Or perhaps you are a professional considering a change of industry. You may even be a PhD that wants to transition into the private sector. Whatever your origin, what concerns you now is how to accomplish this change. And you wonder: should I study a data science master’s? Will it be worth it?

Since you want to get a data science job, enrolling a master’s program sounds like a logic step. A master’s will teach you the knowledge needed to do the job as well as the credentials to get it. However it is also costly. Tuition fees may vary from a few thousand dollars to several tens of thousands. Additionally you will have to invest a year or two into it, potentially going jobless during the time.

Since it’s a very important decision, you consider the alternatives. Is there a better way to learn data science? Maybe self-learning and some online courses? Maybe on another data-related position you can learn it on the job and easily transition later?

In the rest of the article I will compare the master’s and the self-learner routes and highlight when one is better than the other.

Let me tell you a couple of stories

With one year left to finish my degree (Math+Civil engineering) I took interest in data science. That year I only had to do my final thesis so I had a lot of free time. I used that as an opportunity to get into data science by:

  1. Doing a machine learning project as my thesis with the help of some online courses and books
  2. Joining a local analytics consulting firm for an internship, where I learned about SQL and databases

That turned me into a great candidate for starting data science positions and I got my first full time job right after finishing my thesis.

My wife Anna enrolled a master’s in statistics and operations research right after finishing her bachelor’s in mathematics. After finishing her master’s she has held a couple of data scientist jobs and is now a biostatistician. Doing a master’s was a great decision in her case. It led to job offers and meeting great friends.

As illustrated by these examples, both ways can work. Which one is best will depend on your personal situation and preferences.

Why should you study a master’s in data science?

Certification. A master’s degree is a recognizable badge and will make it easier for you to get interviews. Recruiters and HR professionals value it highly. Data scientists don’t value it as much, according to a recent Kaggle survey only 20-30% of data scientist hold a master’s degree. If you decide to skip the master’s, there are some ways to get proof of your skill such as:

  • Data science competitions (Kaggle, local hackathons …)
  • LinkedIn skill assessments
  • Personal projects and OS contributions
  • Experience on adjacent fields (data analyst, data engineer).

Peers. Another advantage of a master’s degree is that you will have a class of like-minded people to study with. During the degree you can have fun together. Afterwards you will be a valuable network of professionals that can help each other. Self-learners won’t have the same camaraderie as classmates. However, you can join online communities as well as local meetups and study groups to network and socialize.

Convenience. The final major advantage of a master’s degree is that it’s simply easier to follow from start to finish. Most people find it a lot easier to commit to a habit once they’ve paid for it or given it some formal structure. Finding the discipline and motivation for consistent self-study is hard. If you struggle with it you can try some of this tricks:

  • Learn with a friend
  • Allocate a certain time of the week to it
  • Find a way to track your progress and give you a sense of accomplishment

What are the advantages of self-learning?

Flexibility. Self-study lets you advance at your own pace, from wherever you are and skip subjects you find boring or uninteresting. This was very important in my case as I have always struggled with things I find boring. Some master’s programs aren’t as rigid as they used to be but still nowhere close to self-learning.

Cost. And an obvious one. The cost of learning data science on your own will be close to zero. You may spend some money on a couple books and online courses. Master’s degrees are very expensive unless education is heavily subsidized where you live.

Quality of education. I know this may come as a shock to some. Self-learning will let you pick and choose the best materials from different sources. On the other hand, a master’s will have you committed to a single program. If you are unsure about the best books and courses, worry not. Online forums like reddit and stack overflow will answer your questions and blogs like this will try to point you in the right direction. Moreover, bloggers and Kaggle winners regularly share their experience and tips, something that many teachers won’t do. So even if you decide a master’s is the better option for you, it’s good to stay online.

And finally, let’s talk about salary. The Kaggle study found no significant salary differences between those who had a master’s and those who didn’t. So we can call this even.


As with most thing in life, wheter or not studying a DS master’s is a good idea, will depend on your situation and preferences.

Study a master’s if you really value the certification, having classmates or aren’t confident on your discipline to do it solo.

Self-learn if you value the flexibility or money is an issue.

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