When NOT to get a Master’s in Data Science.
From a university faculty member with real-world work experience. Full time school is not for everyone.
I am currently a faculty member at Vanderbilt University’s Owen Graduate School of Business and affiliated faculty at the Data Science Institute. I just wrote about why you should consider a Master’s degree, so now I’m writing the companion piece.
A bit more about me, then: before my Ph.D. and faculty job, I worked at Accenture for five years and TIAA-CREF for three, both essentially doing software development. I never had taken a programming class in college (still haven’t) and so all my tech work in both places was self-taught or corporate training, mostly in Java. So I am well-acquainted with the mid-twenties search for “what do I want to be when I grow up” and then “how do I get there?” More about me here.
When you should skip the degree
If you are currently employed and have the following benefits, you should very likely stay where you are and upskill in place:
- A company you enjoy working for and colleagues you enjoy working with.
- Interesting work that challenges you.
- Colleagues who are either doing what you want to do in data science or want to walk the same path towards data science with you.
- A willingness by your employer to invest in you (usually through corporate training partnerships or some reimbursements for your own self-learning).
This is perhaps a no-brainer. If you have all of the above, you are probably not even reading this article. If you have the above benefits and want to give a shout out to your employer and/or group, say so in the comments! Great companies deserve to be recognized. Honestly, that is what I felt like I had at Accenture, which is why I never got my MBA. I felt like I had lots of interesting work to do and plenty of training opportunities (all internal, but lots of self-study modules on a wide array of topics and I learned a ton). I was working with smart people that I genuinely liked.
I highly recommend Accenture as an employer! I ultimately left consulting because of the intense travel lifestyle. It isn’t for everyone long-term, but it is a great job for anyone interested in learning and hard work. (Note: my tenure ended in 2003, so I can’t 100% speak for how they operate now, but I assume it is the same or similar).
Now, drop one of the four.
Let’s start examining the permutations away from this ideal situation, because most people are not in this ideal situation.
A company that does not have much in the way of training
This is #4 above. So, you have #1–#3 in your current job. A great company, interesting work, and colleagues to learn data science with. But no funding or help with training.
This may surprise you, but in this scenario, I generally recommend you stay where you are. Most people underestimate #1 (a company you like and people you enjoy working with). I strongly believe that who you are working with is more important than precisely what you are doing. If you genuinely like the company and people, have some like-minded peers and interesting work, you should stay.
Then, you should find some of those like minded people and start a self-study group. Why? It is hard to self-study alone. Most people can’t do it. If your company doesn’t have the funds for training, perhaps they can at least give you permission to do some of your school work on company property or perhaps even during your work day. Watch videos and do readings on your own, then schedule regular meetings at work with colleagues to work on assignments so you can keep each other accountable. Even if you just sit there in silence working, others’ presence is helpful. You’ll probably end up helping each other (check the course honor code to be sure of what is allowed here).
These meetings are where you’ll further develop your own network (with those co-workers) and learn from them (peer learning). In your job, you’re already likely doing teamwork and managing deadlines. Your biggest risk is that when the time crunch happens (it will), your studies will fall to a lower priority. That is why you need your group to hold you accountable to at least start coming back if you need a work-induced hiatus to do your real job.
Don’t underestimate how hard it is to keep going with self-study. Set yourself up to succeed by finding a group.
If you can, advocate with your employer to add some training options. There are lots of great companies to partner with on this, and that will move the company in to the “no-brainer” category in terms of retaining top employees. Also, see if they could do tuition reimbursements for part time degrees.
No colleagues to work with
This is dropping #3 above, so you have #1, #2, and #4. A great company and co-workers, interesting work, and funds/programs you can use, but no other people interested in walking the data science road with you.
This is a likely scenario if you are at a company that is very, very early in the data science lifecycle. They perhaps want to do something with analytics, but most of the data is emailed Excel spreadsheets. Or perhaps they have a database and you are welcome to access parts of it, but they aren’t sure what they want to do. You like the company and the people and want to help, but staring at all there might be to do without a lot of help feels daunting. I bet we would all be surprised at how many companies are still basically at this stage, even with all the data science hype. As a consultant, I learned that the reality behind the curtain is always less impressive than what it looks like in the first impression. I am certain there are a lot of firms who are doing data science with Excel spreadsheets.
This is harder than the above choice, but I still think it is worth trying to make it work. If the company has training resources, and they are willing to invest in you, then you should try to make the most of them. However, as I noted above, you’ll likely struggle if you feel like you are doing this alone. Give it a try, see if you find others along the way and do your best. Anything you learn is a benefit and will help you, even if you don’t accomplish all you want. Maybe you’ll find others in your training course/program that you connect with. Once you begin, maybe others at your company will want to join you. Again, try to connect with someone to work together because going it along is hard.
I’ll again reiterate how important relationships are at work. So even if you feel like you are by yourself working on all this, it is worth holding on to those people. Remember:
Who you work with is more important than what you are doing.
Who knows, if you’re the first one here, you may end up as the Head of Data Science :). I know someone who took a variation on this path: they went back to school part time (in town, paid for by the company, evenings and weekends) and got a master’s and then helped the company build out their data science capability. By doing this, they built up a great data science network outside of their firm that has continued to help them.
Great company, great people, willing to invest…but you’re bored.
This is also a harder scenario. Even if you like the people and have great support, if you’re uninspired by what the firm is doing and what you are doing day to day, you’ll probably find it hard to keep going.
The most likely scenario here is that you are in an industry that just is a poor fit. Maybe you’re in finance, and it just isn’t your thing. There is nothing wrong with that — lots of undegrads think finance is awesome only to get there and…meh (I say this as a finance professor…). It pays pretty well (most of the time) but I’ll tell you — I see a lot of former finance folks in data science, and not doing finance. It might not be a great fit.
In this case, you should really consider going back to school. This is harder because as I said, you need to value relationships. However, in this situation, you are likely in an industry you don’t want to be in 5 or even 10 years from now. The best way to reset your path is to go back to school. You’ll greatly improve your skills, meet a new network of people, and start clean with your job search.
Remember: job hunting is a full time job. Trying to network your way out of an entire industry is an even harder task. So, if you don’t see yourself in your current industry (much less this firm) in 5–10 years, you’ll be looking for a job soon regardless of what you choose regarding education.
In the meantime, if they offer some great training, you should get on it. Nothing is wasted, so if you can take some courses while submitting applications (or looking for a job), you should do so. Everything you do later will be easier if you can start the process now.
You are not big on the company or the people, but the work is pretty cool and the training opportunities are nice.
Our final permutation is dropping #1, where you don’t really feel like you fit in at the company and/or are not sure about your professional colleagues and those relationships.
Going back to my main theme, relationships matter, your days here should be numbered. However, there may be some value in sticking it out, working on some interesting projects and building out some skills. If you can do this for a few years, you can go a long way towards really upskilling yourself and setting yourself up well for the future.
This “stick it out” approach works best if the primary issue is the company, and not the industry. To continue my finance example above, perhaps you like finance, you just are not sure this company is for you. As I noted above, if you are looking to move out of the industry entirely, you should consider going back to school because that reset is a great way to career switch. However, take advantage as much as you can of the training that is available in the meantime because nothing is wasted. Even taking a few intro courses will ease your transition. Then, finding your way to a new company will be easier than trying to get into a new industry.
Final thoughts
You can probably work out other sub-scenarios from the above description, so I won’t go through all the combinations. For example, if you only have #1 and #2 (great company, great people, interesting work but no real training support or others to work with) then that is a combo of my first two scenarios and you should consider staying, but it will be even harder to study on your own time and on your own dime.
Another item to keep in mind is that most employers that provide solid training or skill development usually have some sort of “lock up” associated with it. They don’t want to train you up so you can quit. As I noted in my last post, quality teaching is costly, and if your firm is going to pay for it, they are going to want to benefit from your skills. You shouldn’t begrudge them this, rather simply include it in your planning because it is fair.
I’ll also say this: as a faculty member at a university, I’m glad that there is such a diversity of educational options out there. I don’t see it as competition, but rather complementary. The university system is not for everyone — not everyone can quit their job and go back to school. Not everyone can afford tuition (always ask about financial aid, though!). I’ve benefited from many of these non-university sources myself — as I said, I’ve never actually taken a university course on programming, yet I have used probably ten computer languages over my career so far. Also, books! I did an entire project in Perl once using just the O’Reilly books as my instructor (anyone remember the “Llama” and “Alpaca” books?).
People matter
I’ll conclude with this again. People matter. Who you work with matters. It is really easy to get caught up wanting to work on the latest and greatest technology or be in the coolest workplace or city. Working by yourself on cool technology is quite lonely for most people. Going to work at a cool office location where you don’t feel like you quite fit is isolating. Think very carefully about what you want to do if you really like your company and professional colleagues. Don’t take that for granted.
