Things to Keep in Mind When Choosing a Data Science Bootcamp

Over the past few years, the growth in data science (or related) bootcamps has been exponential.  For those of us interested in entering the advanced analytics field, the options we have for fast training can be overwhelming.  

I’m not going to get into discussing specific programs for the purpose of this article; rather, I’m doing to discuss some emerging trends to watch out for when deciding which (if any) data science bootcamp is right for you.

Data scientist salaries are projected to see an annual raise of 6.4

First, some background.  When these bootcamps first exploded onto the scene a few years ago, it was hard to find much negative press or non-successful stories of recent grads.  You had to look very carefully to find stories of real-life people graduating with significant debt loads and not-so-impressive employment outcomes.  

Now, in 2017, the landscape has shifted. With the proliferation of boot camps, not only has the overall press coverage of student outcomes drastically increased, but so has the number of students flowing through these programs, from a pure numbers standpoint.


More Candidates, More Openings

One could argue that at some point, the amount of data science/advanced analytics grads will saturate the market – others would argue we’re hit that point already, and maybe hit that point a long time ago.  

Yes it’s true that there is still a huge need for fundamentally talented data scientists at companies all over the world, but the key term here is ‘fundamentally talented’ – from recent press, it’s looking like a higher and higher proportion of boot camp graduates are coming out with substantially lacking skills, especially when it comes to fundamental concepts of computer science.


Not Quite Ready

Many others have discussed the specifics of what these shortfalls are and what they mean. The take home point is companies have noticed that a lot of these grads require a lot more initial hand-holding than the marketing materials for bootcamps would generally lead us to believe – even though bootcamp grads are generally well-versed in terminology and generally familiar with the most in-vogue technologies.

When it comes to solving real-world problems (especially ones that include implications for data architecture, some of these grads just aren’t quite ready yet.

So what does this mean for those of us who are interested in maybe choosing a data science bootcamp for ourselves?  Well, for starters, be aware that a 12-week course that essentially focuses on marketing yourself and having a portfolio that incorporates the latest shiny framework might not lead to the best short-term employment outcomes anymore.  

At the very least, spending some time with a fundamentals of computer science textbook should be a complementary aspect of your education, even if it’s not part of the core curriculum and you have to do this independently.


Splitting the Risk with The Program

Also, in pure economic terms, it might be worth placing more weight on the programs that don’t charge any upfront cost – rather, they make their money by taking a percentage of your post-graduation earnings for some length of time.  It’s true that you could end up paying way more for this education than if you just paid a normal tuition rate – however, what I like about the percentage situation is the program is not very vested in making sure you get hired into a good situation.  

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