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8 Things You Must Consider Before Committing to a Data Science Master’s Degree

Hold fire with your Pinterest board: is going back to uni the right move for you?

Image by Alexander Schimmeck on Unsplash
Image by Alexander Schimmeck on Unsplash

In recent years, the number of Data Science-related master’s programs has exploded. According to FindAMasters.com [1], universities around the world currently offer a total of more than 3,500 master’s degrees related to Data Science and AI.

Clearly, there is a lot of demand, and universities are experts at tapping it. Hardly a day goes by where I’m not targeted by the internet gods with some slick, well-marketed college ad banging on about how "data is the new oil".

If you’re considering a master’s in this field, it can be really tough to rationally evaluate the pros and cons of enrolling. Obviously the universities are going to say it’s a good idea: they want your money! But is it a good idea? And is it a good idea for you specifically?

I’m writing this article because I want to try and help you make a well-informed decision and consider all the important questions. I myself do have a master’s in Data Science, and while I think this was a worthwhile investment of time and money for me, I also believe that a master’s won’t be right for everyone. My hope is that these 8 considerations help you work out whether it’s right for you.

Why do YOU want to do a master’s degree?

First, you need to work out what’s motivating you to consider a Data Science master’s degree.

Yep, I said YOU: not Bob the sophomore from Colorado who wrote a post in r/datascience; not Anirudh from Bangalore who wrote on Quora about making a career transition from software engineering. YOU. Everyone will have different reasons, so what’s yours?

The reason I start with this is because your motivations will massively affect the advice that you should listen to. If you don’t have a clear idea of what’s motivating you, you won’t be able to filter out the irrelevant advice and will end up reading a lot of garbage that doesn’t address your specific concerns.

From what I’ve seen, the most common motivations are:

  • Career-related: For example, to gain credibility in the eyes of (prospective) employers and increase your chances of getting a job or a promotion.
  • Learning-related: You want to learn as much as possible, as fast as possible. Maybe you’ve tried learning in evenings and weekends, and you’re getting frustrated with how slow progress can be. For you, the main motivation is being able to go "all in" and learn at speed.
  • Intellectual curiosity: Perhaps you’ve already got a reasonable grounding in AI, but there’s a specific area or topic you really want to explore. For you, the main pull of a master’s is the chance to take a step back from life and dedicate time to exploring your interests.
  • Accountability: You know that self-driving your learning isn’t always that consistent, so want something tangible to aim for (like a master’s degree), which will keep you accountable and consistent in your learning.
  • Imposter syndrome: You’ve already got a job in the industry, but don’t have a formal background in Data Science and want the validation of an official qualification.

Do any of these feelings resonate with you? Have similar thoughts crossed your mind? Until you have a clear idea of what’s driving you, it’ll be really difficult to fairly evaluate the pros and cons of enrolling.

For me, I was motivated by a mix of career- and learning-related reasons. Before deciding to enrol, I spoke to many senior Data Scientists who had all told me that getting a master’s degree was objectively not necessary for pursuing a career in Data Science. I also already had a good undergraduate degree (so I didn’t need to show employers that I was academically capable), and I saw that there were enough online Data Science short courses to enable me to self-teach all the content I’d learn in a master’s. Nevertheless, as someone coming from a completely non-AI background (my previous jobs had been in Sales and Marketing), I knew that I would benefit from an intensive period of study, and felt like coming to Oxford was a bit of a once-in-a-lifetime opportunity.

Is it necessary?

Once you’ve identified why you’re considering a master’s degree, you need to ask yourself: is a master’s actually necessary __ for meeting my goals?

For example, let’s say your primary motivation for doing a master’s is to gain credibility in the field and increase your chances of getting a job/promotion. Can you actually say for certain that a master’s will help with this? I know it could (in theory), but will it?

In all likelihood, the answer isn’t as straightforward as you might like: it depends. If you’ve already got a degree in a STEM-related field, for instance, getting that master’s might not do much to boost your job chances. This is especially true if you’ve already got another higher-level qualification like a master’s or a PhD. That qualification already demonstrates your aptitude with numbers, and another degree might not add much to your profile in the eyes of prospective employers. Rather, the limiting factor in your career development might simply be your lack of commercial experience in data/analytics. In that case, you’re better off trying to do things that plug the "experience" gaps in your CV, for example getting internships or lower-level jobs.

My point is that you shouldn’t assume that getting a master’s will significantly help you in your journey. The simple way to find out how necessary it is is to speak to people in the company/industry you’re specifically interested in. Try it: reach out to some senior/principal Data Scientists at companies you’d like to work at, and ask whether they’d consider a master’s degree a prerequisite for working there.

As I discussed previously, in my case I think getting a master’s was helpful, but I wouldn’t say it was necessary. Is it for you?

Is it the most efficient way to achieve your goals?

The next question to ask yourself is whether the degree will be an efficient use of your time. At first, this might seem like a strange question. Surely, we think, full-time study is the best way to learn lots quickly? The thing is, however, while a master’s seems like an obvious way to achieve that goal, it’s not the only way, and might not even be the most efficient. During my master’s, for instance, it was compulsory to take papers in many subjects which were not directly relevant to Data Science (e.g., papers on survey design, the social science of the internet, etc.). Don’t get me wrong: these papers were very interesting and provided great fodder for my portfolio, and I loved the broad academic experience which is hard to get outside of a university. But if you have very specific aims (e.g., become an expert in NLP), then doing a generalist/broad qualification like a master’s might not be a very efficient way of investing your time.

What’s the opportunity cost?

Consider the following question: if you didn’t do a master’s degree, what could you do instead? And, crucially, could that alternative actually be more helpful in terms of enabling you to reach your long-term goals in the field?

Image by Raquel Martínez on Unsplash
Image by Raquel Martínez on Unsplash

Considering the opportunity cost is a very helpful heuristic for evaluating the pros and cons of different opportunities. Ironically, for me an example of this came when I was finishing my master’s, when one of our professors was encouraging us to think about extending our studies into PhDs. His argument went something like this: employers rate PhDs much more highly than master’s degrees, so if we wanted to get top jobs, we should strongly consider extending our studies for a few more years and working towards PhDs.

The thing is, however, this isn’t really a fair comparison, because if a PhD takes 4 years to complete, then really we should be comparing a PhD to a masters + 3 years of work experience in Data Science. And all of a sudden it seems much less obvious that a PhD is a good idea, given that 3 years of work experience adds an awful lot to a candidate’s profile in the world of AI.

Don’t get me wrong; a PhD might still be a prerequisite for certain jobs. The point I’m making is that you need to look at all the options and compare them fairly. A simple way to do this is to think of your time as a resource that you can invest. Simply ask yourself: how can I invest this resource to drive maximum return on investment?

Can you afford it?

Don’t assume that you’ll "make back" the cost of the master’s through getting a higher-paying job afterwards. I know plenty of people who’ve done Data Science-related masters and are still looking for jobs. Despite what you may read online, it’s not a guaranteed that you’ll find a well-paying job, so don’t assume that any money spent on a master’s will be reimbursed via a job (at least in the short term).

Image by Towfiqu barbhuiya on Unsplash
Image by Towfiqu barbhuiya on Unsplash

If money is an issue, there are lots of ways of working around this. You could study part-time (i.e., alongside a job), apply to scholarships, enrol in a funded PhD instead, or get employee to sponsor your studies; I know several people who’ve made this work.

In my case, I took a few different approaches. First, I applied to as many scholarships as I could, and got a partial scholarship that covered a substantial portion of my fees. I also worked for a couple of years before doing my master’s degree, so that I had time to save up for it. Because of the large financial outlay, I was also laser-focused on getting a job, and made sure to apply to jobs early in the year and try and minimise the amount of time I’d spend "jobless" after finishing the degree.

Is it a good course?

Don’t assume that just because it’s a reputable university, the course will be good. Pay close attention to the specific modules and who will be teaching you. Find out about student satisfaction scores for the course and look at graduate outcomes. If you can’t find any official statistics about these things, see if there’s an open day or online taster event you can attend. When I was considering my master’s course, I reached out to the course coordinator with some questions. Others I know reached out to current students and alumni on LinkedIn to get their opinions. It doesn’t matter loads who you ask, but the more specific your questions, the better.

Will the course suit you?

Data Science courses vary wildly in their content and style, particularly with regard to coding prerequisites. While some are pitched at newbie coders, others expect a much higher level of prior coding experience.

The important thing is to find a course that’s right for your level. For example, the master’s I did at Oxford tries (and in my opinion, succeeds!) to accommodate people from all types of academic backgrounds, but this meant that the first weeks were a bit of a "coding crash course", and if you’re already got loads of coding experience then this would probably be very boring and a bit of a waste of time.

Other things to consider are whether the course is a taught or research masters, whether it offers the chance to do work placements, whether it’s online/remote, and whether it’s full-time or part-time. All of these factors will affect how suitable it is for you. In my case, I had a bit of coding experience, but really wanted something that would be comprehensive and cover all the bases of a general Data Science education. Given that I had a social sciences and business background, I was also keen to pick a course that focused on the application of AI to problems in economics and business, rather than a purely theoretical course which downplayed the commercial and societal aspects of AI.

Are you certain you actually want to do Data Science?

Image by Vladislav Babienko on Unsplash
Image by Vladislav Babienko on Unsplash

If you’re on the fence about whether you want to work in DS/AI, a master’s degree is NOT the best way to find out. Sure, it is one way. But it’s also pretty much the most expensive and time consuming way.

I reckon that a lot of people do master’s degrees as a way of putting off decisions about what to do with their lives. Don’t do this! Do a gap year instead, and get some practical work experience in the field to see if you enjoy the day-to-day work.

Ultimately, Data Science isn’t for everyone, and it’s important to consider that data science might not be the right path for you. You might be more suited to an adjacent field like product management, software engineering, design, human-computer interaction, research, statistics, data visualisation, analytics translation, or something like that. There are tons of related Careers out there, and you shouldn’t write off the less-well-known or less-hyped options just because you don’t know much about them. I tried a few different jobs before settling on my master’s, and I think they really helped me know for definite that AI was the area I wanted to go into. Plus, once you come out the other end of the master’s, having some prior commercial experience will help propel you into that next job.

Conclusion

I really hope this article has brought some clarity to your thinking. If you liked it, it would mean a lot if you followed me, as it helps support my writing. Let me know in the comments if you have any feedback!

Sources

[1] FindAMasters.com https://www.findamasters.com/masters-degrees/?Keywords=data+science [Accessed 12 April 2023]


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