Is getting a Master’s in Data Science a million dollar mistake?

Colleges are businesses, and businesses are looking to make easy money. Data science is a field that presents opportunities to do that in a couple different ways. One, colleges are aware that it is incredibly easy to get a student loan–so they continually raise tuition knowing that students can just borrow as much as they need. And two, the explosion of high-paying data science and analytics jobs coupled with the newness and uncertainty of the industry has made these data science academic programs an easy sell to prospective students.

Okay, but how is getting a Master’s in Data Science a million dollar mistake? Tuition can cost upwards of 20, 30, 40k+ over the course of a Master’s program. What if you instead invested the money that would otherwise have gone to pay your tuition?

You’re probably shaking your head at this point. But hear me out. Consider two 25 year old young professionals interested in getting into data science and analytics. Let’s call them Larry and David.  

Larry opts for a Master’s in Data Science (or a Master’s in Analytics/Applied Statistics/Data/Information or whatever title sounds the most impressive to a university administrator). Larry pays his tuition (or if he enjoys financial torture, takes out a student loan).


David instead continues at his same job. If possible, he takes on any data-related or analytics-related responsibilities at his work. In his free time, he learns SQL and Python or R, brushes up on some statistics, works on data-related personal projects to build up his portfolio, takes an online class or two, and even offers up his services for free or at a discounted rate to companies looking for data science and analytics freelancers. David decides to invest the money he would have spent on a Master’s degree in the S&P 500.

18 months pass.

Both Larry and David have accumulated (roughly) the same amount of knowledge and skills. The difference is that Larry paid A LOT more money. When David retires, his investment (assuming a standard rate of return) would net him… you guessed it, over one million dollars.

Now you might be saying to yourself, but you’re not taking into account the return on investment from Larry’s education?! Won’t companies value Larry’s fancy Master’s degree over David’s self study?

What does a bean mean?!

Let’s break down each concern:

You’re not taking into account the return on investment from Larry’s education?!

You can acquire the same skills and experience from a Master’s degree with self study. Typically, these programs offer courses in data cleaning, data visualization, data analysis, machine learning, programming, and statistics. But, these academic-based technical skills will only get you so far.

A combination of self study, personal data science projects, and doing whatever you can to find any data science freelance or volunteer opportunities gives you as good (if not better) experience than a Master’s degree.

Won’t companies value Larry’s fancy Master’s degree over David’s self study?

Short answer: no, they won’t. The data science and analytics field is still new and evolving. There is not one background or one path that you have to follow to get your first data science job.

As an aspiring data scientist, it is your job to demonstrate your value to companies. It might be the path of least resistance to just pay for a new degree–but it is a bad strategy.

Master’s programs love to boast about the job offers their students get–but what prospective students forget about is that a lot of those students could have gotten those jobs anyway. Controlling for a student’s ability and motivation, the value-add of a Master’s degree is basically zero. The content and resources that can be found in a Master’s program are available online for free (or for significantly cheaper than the cost of tuition). So, Master’s students are paying for two things: convenience and a line on their resume. I can think of a million reasons why that isn’t worth it.


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