I'm happy to answer any questions about this (I expect it to be controversial). When we started Triplebyte one year ago, I was pretty skeptical of bootcamps. Doing credential-blind interviews and seeing what some bootcamp grads can do, however, has won me over. Clearly there are a lot of bad bootcamp grads (and probably a lot of bad bootcamps). But the model is working really well at the top.
Can you discuss how you might test for bootcamps overfitting their curriculums to interviews? They definitely would seem financially incentivized to do just that. From a personal anecdote, someone I interviewed was able to code a solution for a problem, but then couldn't discuss how or why they did it that way at all, or solve a closely related problem, which made me strongly suspect that they just memorized an answer. How would you test for this?
I would also be curious to see if there was any way to breakdown bootcamp grads with previous programming experience vs. without and by what they studied in undergrad if they went to undergrad.
Interesting questions. Bootcamps are clearly incentivized to do this. However, they do not seem to be particularly good at it. Algorithms are over represented in interviews relative to most jobs, and yet (as our data shows) bootcamps are not very good at teaching this. Now, we are measuring algorithmic skill by asking candidates to actually implement non-trivial algorithms. We've observed that a lot of interviews involve what is essentially trivia about algorithms, and it's possible that the bootcamps are better at preparing students for this (we don't measure this skill so I am not sure). I think that this gets at the answer. If you make your interview go deep, it gets increasingly hard to specifically prepare for it, to that point where preparing is actually becoming a better programmer. Rather than a 30-minute question that covers knowledge of sorting algorithms (easily learnable), have your candidate spend an hour building a collision detection systems using a axis-sorted list of rectangles, and reason about maintaining this sort as objects move around. That's the theory. In practice there will always be some noise.
Do you have any date on job performance? It seems to me the complete lack of algorithms is a land mine for any project. I think it is a lot more likely a coder with little fundamentals will be able to grow or see the bigger picture.
I agree that not all jobs require this skills, but then suddenly you get a divided workforce with designers and implementers.
> It seems to me the complete lack of algorithms is a land mine for any project.
If you hire someone and release them into the wild of your codebase, that's a failure on your part not theirs. New hires are an investment, they shouldn't be treated as an immediate need-based solution. My experience in two fields has shown me that new hires are a drain on resources for a fairly significant time period, regardless of their background. If you're not doing everything in your power to educate your new hires and get them up to speed, THAT'S your landmine. It's your job to get them to learn the fundamentals if they don't have it.
Eh, I disagree with the generalization that all new hires are a drain for a significant time period. In my experience, the rate at which they come up to speed is extremely varied.
> when I was a new grad I started on the codebase more or less right away
I did as well, but...
> Didn't even have code reviews back then.
Not so much this one.
> If you have to train them, then it seems the coding part should be cheaper then the algo part.
I do actually agree. But there tends to be other aspects of the job outside of just code and algorithms. Prior work experience is a shining star compared to a 22 year old in any field. I see my coworkers more than my family. Someone that has a full understanding of everything that goes along with that has a head start across the board. Someone changing careers in their late 20's also tends to put a hell of a lot more into improving themselves than just the basic progress-of-life learning. I just think it evens out as long as you're consciously hiring people for the right role.
I don't know, we pay people to do a job. Sure some coworkers become friends, but most don't. As long as people are somewhat easy to get along with I don't think we should focus too much on other stuff.
I wasn't implying that they should become friends. I meant that the working world doesn't tolerate the "I'm definitely right" mentality that comes with God's greatest gift, the 22 year old college grad. I'm joking about that last part, but there is a certain tact that only comes through time. A 25 year old is much more willing to say "I'm wrong, and I have no idea what I'm doing" than a 22 year old. This alone is a huge productivity boost. Both technical skills and soft skills can be learned, and I'd argue both are equally challenging.
Were you able to see any patterns in the backgrounds of the bootcamp students?
It doesn't seem too surprising that someone with a physics or finance or mechanical engineering background can learn enough programming skills in three months to be productive. But do students without as much prior experience in quantitative and analytical thinking have as much success?
We see about a 50/50 split between the two groups. (Of course a lot of the people without a technical background are still analytical thinkers. There's probably pretty heavy selection bias for that)
Do you think there's a correlation between "bad bootcamps" and "bad bootcamp grads"? I assume you can get good grads from bad bootcamps and vice-versa.
Our dataset is not large enough to really answer this. My opinion is that quality of the bootcamp (how selective they are, and how well they present and motivate students) has to matter.
