I can attest to this in the case of the Data Science course. My wife, a non-CS PhD, was interested in changing her career track. We were referred to General Assembly (the SF location) by one of my coworkers and the curriculum looked comprehensive enough, so she signed up.
About a month into the course, to our despair, we discovered that there was almost zero conceptual training imparted (I am a data scientist by profession and can speak to this). The instructors seemed to be working there as a stopgap measure while looking for other jobs, and the students were there for standard credential-seeking. The course material meanwhile was focused on answering "How" but not "Why", which is far more important when approaching data science problems.
To fix the situation, we quickly pieced together a curriculum based on Tibshirani & Hastie's machine learning video lectures[0] and picked a few Kaggle challenges for practice. My wife went through this material over a month, and came out with enough of a mental framework to be able to tackle book-length material such as ISLR[1].
Finally, there was almost zero career support provided apart from generic advice around how networking is important and how one should try to get outside their comfort zone.
Though all's well that ends well (my wife is working as a data scientist now at a seed-stage Valley startup), in retrospect we often talk about how we shouldn't have wasted money on this course. She didn't learn anything that she couldn't have picked up on her own, not even practical skills re: data analysis with Python/R, and her student cohort was quite disinterested in making connections.
Just to tack onto this, from what I understand, quality at each of the GA locations varied wildly. Part of that is it's on the instructors to come up with all the details of the curriculum so instruction varies from class to class depending on whether or not the instructor had taught it before, or if HQ decided to change the material in the course. My understanding as a former evening class instructor was the SF location was a shit show, whereas the Boston location where I taught had better outcomes.
About a month into the course, to our despair, we discovered that there was almost zero conceptual training imparted (I am a data scientist by profession and can speak to this). The instructors seemed to be working there as a stopgap measure while looking for other jobs, and the students were there for standard credential-seeking. The course material meanwhile was focused on answering "How" but not "Why", which is far more important when approaching data science problems.
To fix the situation, we quickly pieced together a curriculum based on Tibshirani & Hastie's machine learning video lectures[0] and picked a few Kaggle challenges for practice. My wife went through this material over a month, and came out with enough of a mental framework to be able to tackle book-length material such as ISLR[1].
Finally, there was almost zero career support provided apart from generic advice around how networking is important and how one should try to get outside their comfort zone.
Though all's well that ends well (my wife is working as a data scientist now at a seed-stage Valley startup), in retrospect we often talk about how we shouldn't have wasted money on this course. She didn't learn anything that she couldn't have picked up on her own, not even practical skills re: data analysis with Python/R, and her student cohort was quite disinterested in making connections.
Ce la vie.
[0] http://www.dataschool.io/15-hours-of-expert-machine-learning... [1] Introduction to Statistical Learning, http://www-bcf.usc.edu/~gareth/ISL/