I'll speak up from the (seemingly underrepresented) data science side here, despite not making the 3+ years ago cutoff.
I attended a data science bootcamp almost two years ago now. It was 12 weeks long and ran 9-5 each day with a mixture of lectures and pair programming exercises in the morning, and time set aside in the afternoon for working on projects. My cohort was very diverse; there were kids just out of grad school, teachers, actuaries, data/business analysts, and even practicing software developers all taking the same course. I came into the bootcamp with a fair amount of background knowledge (Bachelors/Masters degrees in Math as well as a ~10 year history of teaching myself various computer science concepts and languages), and I have to say that this served me quite well. I didn't struggle to learn Python (the language of choice for this program) or grapple with what gradient descent was really doing, because these were already parts of the way that I understood the field. Instead, I used my 12 weeks to learn about Git/Github, get really good at actually working from the command line, learn about different "big data" techniques and database structures, and pursue a passion project.
That being said, throughout the bootcamp I was keenly aware of the fact that no one was going to "fail out". There were students that needed more direct guidance than others when difficult topics were broached, and there were students whose presentations revealed that their project hadn't worked as well as they'd hoped (this includes some of my own projects). On the one hand, it was good to have a community of people (students and instructors alike) who embraced these failures and helped you learn from them. On the other hand, it instilled some level of self-doubt: "Maybe I am wasting a solid amount of my life savings on an experience that will only teach me how bad I am at this." Or even, "I feel like I did well with this project, and I have some validation from my peers and mentors, but what would a future boss think of this work?"
As a practicing data scientist now, I feel like the bootcamp prepared me to both know how to ask the types of questions data scientists ask, and to know where to look for the answers I need. As far as I know, everyone in my cohort is employed as a data scientist now, save a couple individuals with visa issues (and these few are still actively working on personal projects). Those with prior exposure to the field were certainly able to get better jobs, and quicker.
I attended a data science bootcamp almost two years ago now. It was 12 weeks long and ran 9-5 each day with a mixture of lectures and pair programming exercises in the morning, and time set aside in the afternoon for working on projects. My cohort was very diverse; there were kids just out of grad school, teachers, actuaries, data/business analysts, and even practicing software developers all taking the same course. I came into the bootcamp with a fair amount of background knowledge (Bachelors/Masters degrees in Math as well as a ~10 year history of teaching myself various computer science concepts and languages), and I have to say that this served me quite well. I didn't struggle to learn Python (the language of choice for this program) or grapple with what gradient descent was really doing, because these were already parts of the way that I understood the field. Instead, I used my 12 weeks to learn about Git/Github, get really good at actually working from the command line, learn about different "big data" techniques and database structures, and pursue a passion project.
That being said, throughout the bootcamp I was keenly aware of the fact that no one was going to "fail out". There were students that needed more direct guidance than others when difficult topics were broached, and there were students whose presentations revealed that their project hadn't worked as well as they'd hoped (this includes some of my own projects). On the one hand, it was good to have a community of people (students and instructors alike) who embraced these failures and helped you learn from them. On the other hand, it instilled some level of self-doubt: "Maybe I am wasting a solid amount of my life savings on an experience that will only teach me how bad I am at this." Or even, "I feel like I did well with this project, and I have some validation from my peers and mentors, but what would a future boss think of this work?"
As a practicing data scientist now, I feel like the bootcamp prepared me to both know how to ask the types of questions data scientists ask, and to know where to look for the answers I need. As far as I know, everyone in my cohort is employed as a data scientist now, save a couple individuals with visa issues (and these few are still actively working on personal projects). Those with prior exposure to the field were certainly able to get better jobs, and quicker.
Ninja edit: italics