
Two years working in the professional world out of undergraduate was all it took before I realized I wanted to shift towards a more data-centric career. I was working as an IT Risk Consultant, which was a great job in terms of prospects and growth, but it wasn’t where my long-term passions resided.
The role I was in was somewhat technical, but I wasn’t getting my hands dirty with as much of the data and programming that I would have liked. So I made the tough decision to seek other opportunities that would allow me to build out a strong technical foundation in data science and analytics. And let me tell you – I looked at everything at the time in terms of channels to build those skills including data science boot camps, master’s programs, and free online courses (MOOCs). Ultimately, I went with enrolling in an accredited Master’s program which I thought was best for my situation and goals, but I spent a lot of time weighing my different options during that decision process.
To understand my decision making, I’ll share the steps I took and considerations I made. The hope is not to prescribe a certain path for those thinking about pursuing a career in data, but rather share my experience and what can supplement or help you arrive at that decision yourself.
Don’t Reinvent the Wheel – Seek Those Who Have Experienced It
First thing is first – find out as much as you can about the potential bootcamps and master degrees from others. Making a decision is easy when you don’t have as many options or information to sift through, but that doesn’t always mean you’re making the best decision.
Finding information online via a program’s website is a no-brainer but your research shouldn’t stop there. If a bootcamp or university is having an open house or information session, try to attend it and have a couple of questions ready to ask. The best type of questions are ones that will give you new information or allow you to hear personal perspectives that you couldn’t already find by reading a brochure or website. For example, I attended numerous bootcamp open house sessions in New York City where current cohort members presented their final projects to prospective employers, networks, and prospective candidates like myself. It gave me a feel for the kind of work the students completed and their backgrounds. It also allowed me to directly connect and ask them specific questions at the end like:
"What kind of job opportunities are you seeking and how has the program specifically helped you get in touch with companies hiring?"
Now – not everyone can easily make it to an in-person event like this (especially during a global pandemic) but luckily we live in a increasingly digital and virtual world. Try to attend any virtual meetings that are offered or at a minimum – reach out to former alumni or current students from the programs you’re interested in on LinkedIn. Specifically, I made it a daily goal to reach out to at least 3 people on LinkedIn from a particular program I was looking at who currently were working at companies or roles that I was interested in. I even had a daily event scheduled on my calendar to keep me on top of it. Reaching out to strangers online can seem a bit cold and insincere at first and the majority of the time you will get rejected, but you’ll be surprised by the amount of people that are willing to share their experience or connect if asked appropriately.
Here’s an actual message I sent to someone on LinkedIn:

Fortunately, that message led to a direct conversation with the alumni member where I explained my background and interests for considering the program in question and learned about their experience. At the end, that person strongly advised against the program for me. Many times, negative news like this, can actually help a lot more than positive news that supports your confirmation bias.
Compare Options and Find Those Moats
The increasing popularity in the past 10 years for Data Science and data focused careers has brought with it a rise in the number of new academic programs and bootcamps catering to this domain. That, in effect, has also made it increasingly difficult to distinguish between various programs in terms of the benefits that they offer. However, after looking at enough of them, there are some common themes in terms of advantages and disadvantages that each one offers:

This list is not exhaustive and there are many other benefits that exist for each of them that I’ve come across, but it really depends on the specific program you’re looking at. For instance, many bootcamps seem to be great for professional candidates who already either have industry experience and a strong technical background as an engineer or have a relevant PhD and are looking to supplement their skills to be marketable for a specific career. However, there are bootcamps where you come out of them with a beginner-level skillset and they can be suited for really anyone, regardless of your previous experience.
That being said, what I did (and what I advise) is I took a look at all the pros/cons, identified which benefits were most valuable to me, and then carefully compared both paths to understand which ones really had benefits that stood out and could not be achieved easily or at all if I took the other path. What I mean by this is what was the moat of each one. Moat is a term popularized by the highly successful investor Warren Buffet, which refers to a distinct competitive advantage a company (or data science institution in this case) has over its competitors.
As an example, while bootcamps could offer more practical hands on experience in terms of projects and working alongside industry experts – could I not also develop this capability with a bit more effort and work on my part while enrolled in a Master’s program? If so, I didn’t really view that as being a moat exclusive to bootcamps. I could just as well get practical real-life data science applications under my belt by choosing a particular project for my capstone, partnering with a professor on research applications, or completing an internship.
Evaluate What’s Best For You
Once you have a good understanding of the distinct advantages and drawbacks, identify what’s best for you in terms of your top priorities. For me, my top priorities, in no particular order, were:
A) Getting real and applied industry experience
B) Being able to work simultaneously and maintain financial support
C) Challenge myself both mathematically and technically
For A – bootcamps were highly incentivized to get me a job at the end but none of that was guaranteed plus the job comes at the conclusion of my experience and I may or may not have liked it. On the other hand, the idea of being able to test-drive a particular role with an internship at a graduate program before hand was highly appealable. For B – graduate school was a no-brainer as most bootcamps I looked at had an intense daily schedule, offering little or no time to work or hold other real responsibilities. And for C, it really just came down to academic programs offering a more theoretical and statistical curriculum, an area I was weak in going into the journey.
Final Rounds of Decisions
Once you’ve decided what channel you want to take, all that is left to do is find the specific institutions that interest you the most and apply. Costs, timeline, curriculum offerings, prestige, and location are all important factors to consider. Just remember – there is no one way for becoming a data scientist, machine learning engineer, or "insert your desired title" you seek at the end of the journey. Hopefully this might help you better find the best way for you.
References
[1] Gallant, Chris. (2019 July 7). What Is an Economic Moat? Received from http: http://www.investopedia.com/articles/investing/022415/ten-worst-mistakes-beginner-investors-make.asp