[hintymad]

I feel that the challenge of Lambda school, as well as other vocational schools, is that their courses are too specific for too short a period with too easy assignments. Take this curriculum for example: https://lambdaschool.com/courses/data-science#curriculum. Stats fundamentals: 4 weeks. Predictive modeling: 4 weeks. Etc etc. I mean, really, 4 weeks for stats? Will the students have enough time to learn fundamentals on counting with such short time？Will they truly learn what a random variable is and why that matters? Will they learn what joint probability distribution is, what test of hypothesis is, and what pdf, cdf, and pmf are? Will they learn what an unbiased estimation is? All these concepts do not even scratch the surface of real data science work. Unfortunately one will not be able advance further without firmly grasping these concepts. And I'm just talking about problems at undergraduate-level. In addition, can we realistically ask the students to work on moderately challenging assignments given such a short time? If they can't, why would I, as a hiring manager, risk my team to hire a graduate from such schools? One may argue that a motivated and smart student can overcome such obstacles and get her foot in the door by attending such schools. But then the challenge is flipped: not many such students need to attend such schools.

[catwind7]

> One may argue that a motivated and smart student can overcome such obstacles and get her foot in the door by attending such schools. But then the challenge is flipped: not many such students need to attend such schools.

i guess it question is is there enough such students who can overcome these obstacles who are also willing to fork over X tuition? they may not need it per-se, but I think you may be discounting the value of the pre-existing networking leverage these schools have over individuals that may have no network in data science related work