Data Science Bootcamps : On Transparent Outcomes
Outcomes reports have not been widely embraced by the Data Science Bootcamp community compared to their programming bootcamp brethren. In fact, programming bootcamps are actually leading the vanguard on Outcomes Transparency. Hack Reactor and several other Programming Bootcamps founded The Council on Integrity in Results Reporting and you can see placement data some of the members have published. There is no reason why the Data Science Bootcamp community cannot replicate or adopt the CIRR standard. There is also a petition started by some CIRR members.
Being transparent about outcomes puts everything in the open.
It’s a very convoluted situation. Data Science Bootcamps are mostly judged by their placement numbers. If the numbers look good, some Bootcamps don’t publicize the detailed placement reports because it may be considered a trade secret. On the other end, if the numbers are bad then it’s not in their best interest to release and publicize them because students might decide to make a run for the fences. At the end of the day, the individuals who loose out are the students who didn’t do enough due diligence and research before attending a bootcamp.
We believe that there is some middle ground that benefits all parties involved — students, Bootcamps and potential employers alike.
A good amount of the material most Data Science Bootcamps teach today is becoming commoditized and you could choose to self-study and still not miss out on too much. A few of the bootcamps doing it right tend to spend a good amount of time on some aspects of the bootcamp experience you may not be able to easily replicate while self- studying. Some of these aspects include employer introductions, having an active alumni base, mentorships, positive reinforcement among others. These deliver a lot of value both in the short and long term.
Attending a Data Science Bootcamp is a high-risk, high-reward investment but some Data Science Bootcamps through their messaging tend to pass it off as low-risk, high-reward investment which is not exactly accurate.
We know for a fact that some Data Science Bootcamps remove individuals who aren’t able to find jobs after a certain amount of time from their rolls and don’t include them in their placement statistics. We also know for a fact that some Data Science Bootcamps hire some of their graduates as TA’s for the program and then consider them employed as Data Scientists. On the surface, there is actually nothing wrong with doing this and you can make a good argument for this but when Data Science Bootcamps release placement numbers without indicating how they came up with the numbers, you can see how the numbers could be made to look much better than they really are.
We feel some of the Data Science Bootcamps operating today have shoehorned themselves and essentially set the narrative that the only positive outcome from a bootcamp experience should be a Data Science job. This very lofty threshold sets them up to fail. The reality is that prospective students take these courses for a wide variety of reasons including transitioning to a Data Science related opportunity. Data Science Bootcamps need to capture these student intentions and be very transparent about them.
We believe the greatest challenge Data Science Bootcamps face going forward is how to ensure they continue to deliver value in very quantifiable and measurable ways (placements or positive outcomes) and also somehow figure out a way to stay relevant.
If you are a prospective Data Science Bootcamp student, you should definitely ask to see detailed placement statistics for some of the recent cohorts. This helps you understand the risks involved depending on what your goal is (it may not always be to transition into a Data Science job). This article might be helpful in performing your due diligence.
This is a repost from the original article with some modifications
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