It seems as though data science is the hot new thing these days. Data science jobs are posted constantly on sites like LinkedIn, while search volumes for ‘data science’ have been trending upwards for years.
For all this the task of becoming a data scientist, and the intermediate step of choosing whether and which bootcamp to attend, can be daunting. In this article, we’re going to walk you through the thinking required to make the best decision for you. We also include a discussion on the most highly-ranked bootcamps of 2019.
“Best” Depends on What You’re Looking For
In the journey toward becoming a data scientist, deciding whether to attend a bootcamp and which one to attend is an extremely individualized undertaking. It depends on a variety of internal, external, and historical factors. Let’s walk through a few of the things you need to keep in mind.
Assessing your finances is an extremely important step in deciding whether or not you can afford to attend a bootcamp. Bootcamps–coding, data science, or any other kind–are pricey. There are a variety of loan and finance options, but remember that even a high-performing bootcamp graduate might not get a job immediately after graduation. You might need to dip into savings while you’re scouting for the right employment opportunity, and it’s worth considering this up front.
A related question is what the optimal study schedule is like, given where you’re at in your current job and what your responsibilities are. I opted to attend the full-time Galvanize program in Denver because I wanted to pursue my new career as aggressively as possible, but I could only do this because I had just enough in the bank to make it a viable strategy and I wanted the extra motivation that comes from spending nine hours a day surrounded by others pushing in the same direction.
If this won’t work for you, there are many bootcamps built specifically with flexibility in mind. We discuss some of them in the next section.
How Do You Like to Learn?
An import factor is how you learn best. Are you invigorated by lectures and team exercises, or do you prefer to focus in a corner with your headphones in until the sun comes up? Do you think you’d benefit from a consistent, structured march through the terrain of modern data science, or are you one of those people who just can’t move on from an idea until they’ve understood it from every angle?
A related dimension is the kind of preparation runway you require. If you studied math or computer science in college, you likely have a lot of the prerequisites in place. If so, you could probably consider applying to a data science bootcamp immediately. If not, you may be more comfortable with one of the self-directed bootcamps. These are better suited to folks needing to periodically shore up or fill out their background knowledge.
If you’ve never heard of a “t-test” or a “for loop,” I recommend gaining some basic familiarity with the relevant concepts and skills before you commit to an ambitious course of study.
It’s worth pointing out, however, that you can absolutely be a beginner and still succeed. Prior to attending Galvanize, I’d never had a college-level class in software engineering, computer science, or mathematics.
Making it through was a struggle, but I did make it through.
What you’re looking to do is take all of the above, use it to develop an understanding of your most likely pathway to success, and then build on it to make your final decision. I can’t tell you what to do, but I do hope this has pointed you to the kinds of questions you need to be asking.
A Tour of the Best Data Science Bootcamps
There are many, many data science bootcamps to choose from. I have personal experience with two: in addition to attending Galvanize, I have a close friend who chose to do Thinkful, a distance-learning program structured very differently from Galvanize. We compared notes before, during, and after. By a happy coincidence, these two programs represent the major approaches to data science education. I’m going to discuss them in detail and then briefly treat the other top choices.
The Galvanize Data Science Immersive is a three-month, full-time program aimed at taking you from beginner to job-ready. In theory, ‘full-time’ means 9 a.m. to 5 p.m., but in practice most students put in hours on nights and weekends. Flatiron follows the same template but is 15 weeks long.
Thinkful pairs you with a dedicated tutor, and you work your way through their curriculum at whatever pace you’re comfortable with. It’s popular with folks who plan to work part-time during their re-skilling, as well as with those who want plenty of time to dig into a subject.
Lambda School is an interesting mix of Galvanize and Thinkful. It’s similar to Galvanize in terms of daily and weekly schedules, but at six months, it’s twice as long, and all the classes are online, so you can attend from anywhere.
Springboard is in the Thinkful mold, in that you’ll be working online with mentors and student guides, not in classrooms (though there are forums you can use to chat with students on the same career track as you).
Insight targets post-doctoral students transitioning into industry from academia. It’s seven weeks long (one of the shorter bootcamps), and it really strives to have you learning more from industry professionals and program alumni than from lectures or books.
The program closest to my experience is the one offered by Metis, which is a twelve-week, in-person bootcamp with a similar ratio of class lectures and project-based learning.
Deciding on a Data Science Bootcamp
This has really been just a sketch that points the way towards an answer. Reflect seriously before you take the plunge, as completing a data science bootcamp is going to require a lot from you.
What are your experiences with data science bootcamps? Leave us a comment, and let’s start a dialogue.