I'm using my circumstances as an example.

I don't have a strong Bachelors (it's in Math this past year) nor three strong letters of recommendation. I am 25 years old in the USA and have not held a full time or part time job yet. I have one volunteer unpaid internship in Hospital Information Technology (mostly blue collar work).

I want to go to a good school in Computer Science to study machine learning, but do not think I can currently afford it nor get accepted. I want to learn some significant mathematic theory in addition to the courses/electives offered in a C.S. masters.

How do I decide from a perspective (either mine or an employer's) whether it is productive to pursue two Master's Degrees back to back?

I am considering starting a Master's in Applied Mathematics focused on Finance/Business at an international university in a year with very low or free tuition (a country like Germany, France, Belgium), even if I have to learn the language.

One year into the degree, I would apply for Computer Science programs in the USA and Canada to study Machine Learning with a focus on Data Science after graduating.

Pros I have read about: I would have the ability to work part time throughout both degrees to pay living expenses. I am currently debt free. I would have multiple opportunities for internships and research. The programming done for my research would be part of my portfolio if I make it test-driven and production quality. I would learn two important and related skills for Industry. I would have the opportunity to travel. I would be more likely to get a fellowship/scholarship or TA/RA position that waives tuition for the second masters. At the right school, I could try very hard to have a significant part of my second degree be a collaboration with a company / organization to work on an important real world problem in industry together.

Cons I have read about: If I don't plan it right, this could be unbelievably expensive. Employers would view me as over qualified for certain positions. Employers may view me as a professional student and see red flags. It may look like I failed/dropped out of a PhD program. I would enter the job market significantly later than my peers (30 years old) causing red flags. I can only spend $40,000-60,000 USD total for both degrees, excluding living expenses, over the four years. Some employers may think that I will only be happy in a job that uses both of my specializations and I will get bored / move-on otherwise.

How do I figure out what employers value, and how do I decide what I value from these pros/cons?

How does one determine pros/cons they have over looked? I assume it is worth it to ask hiring managers on LinkedIn how they view the situations and credentials one would have in this scenario?

Edited in from answer by OP

What I decided to do was do the applied mathematics Master Degree with a focus on Business and Finance then pursue the PhD in CS.

The cons are just too big and give too big of an opportunity cost.

closed as off-topic by gnat, Jim G., Dukeling, scaaahu, gazzz0x2z Jan 14 at 15:05

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  • "Questions asking for advice on a specific choice, such as what job to take or what skills to learn, are difficult to answer objectively and are rarely useful for anyone else. Instead of asking which decision to make, try asking how to make the decision, or for more specific details about one element of the decision. (More information)" – Jim G., Dukeling, scaaahu, gazzz0x2z
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  • Do you think you are actually capable of going through this is the first question you should ask yourself in my opinion. I wouldn't worry on what employers will say until I am really sure I can do it. Do this simple test - pick the books (maybe online) for the first or second year and try to study them all, in X hours / day, where X is what you got left from 24 - attending university - work - sleep. So 24 - (4 to 6) - (8 to 10) - (at least 6). You will see you don't have much time left to prepare for exams, etc. – Pavel Donchev Jan 12 at 3:35
  • 1
    Have you tried Coursera.com the Data Science section? $60/m flat fee and all courses are then available, graded and certified. – Sandra K Jan 12 at 10:18

Outlaying the money, learning a new language and spending the years just isn't worth the gamble that it will get you a better job when you're just starting. It wouldn't make much difference at all to 99% of starting jobs.

I advise looking into it later in your career.


Whilst it is dependent on the role and career you would be looking at, most jobs wouldn't require two masters. From my experience many employers would value the time you would take studying the second masters as time spent in employment instead, even if not directly related to the role they are employing for. I know that when I was looking for my first job, my masters didn't really add much to my worth in many employers' eyes and was often told that they would prefer someone with at least some work experience.

If you are thinking of doing back to back masters, I would suggest looking at possibly getting some work experience whilst doing this as this can really help when looking for your first job. Even having just a simple part time job can help show skills that your education can't and also helps remove the 'eternal student' view that some employers might have.


I would look into the online MS programs by prestigious universities. Georgia Tech has a well known one and UT Austin is launching one this year also. Those tend to be <10k total so would be easier to handle financially.

Your background does not matter much especially for data science. People from all sorts of backgrounds get into data science just fine provided they can demonstrate the required skills. I have seen accounting, psychology, civil engineering grads in various positions or even an ex-priest leading a data science team. Going from that, you can look into getting an internship somewhere until you get a degree and then you should have more options.

As for whether if it is worth doing two Master's back to back, getting two in similar areas may not provide much in extra benefits, especially compared to what you are putting in. Sure, you will be exposed to more material by having math and cs masters at the same time but for your credentials it will be a very marginal benefit over having one and self-studying another.

That being said having masters in different but complementary areas can open up new career options e.g. MBA alongside MSCS, or finance alongside applied math etc. It really depends on how you want to shape your career.


With a background in Mathematics, an interest in Machine Learning and Data Science and limited funds, I recommend you pursue a Masters degree or bootcamp in Data Science. I would lean more towards a bootcamp because you lack 3 letters of recommendation necessary for most traditional graduate programs.

Masters in Data Science programs in general have less strict requirements on computer science fundamentals. You have to check with the graduate program if you have enough computer science background to pursue a Masters of Computer Science program.


You write a lot about your future education but not a lot about your plans for the career afterwards. You should think carefully about where you see yourself 2-3 years from now, and let that guide your educational decisions.

The one concrete thing to say about the machine learning field is that it’s in flux. During the few months I was working on my bachelor thesis on CNN’s, there were tons of new papers published. Two of those literally changed the direction of my thesis. This implies that hanging out in school too long is a bad strategy. The courses you took a few years ago may well go stale by the time you graduate.

Specific skills like R and the like can be acquired cheaply online. Use college for the hard stuff and the networking.

Since you already have a math background I recommend to find one master with a good mix of advanced math, especially statistics, and courses in the latest machine learning technologies. Such courses will already include enough CS to get you going. Then find a good thesis project which brings you into the area you want to work with after graduating. Find schools with the right connections to secure a good thesis, or network on your own. That will put you in a good position for a junior ML role, or a phd should you become interested later.

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