That's true -- there's definitely a bit of knowledge needed to get started, but most of that can probably be taught in 2-3 classes. I think the first problem in learning data science is coming up with a "Foundations" curriculum:

- Learn a relevant programming language (R, Python) + tools (ipython, anaconda, etc)

- Basic linear algebra (nothing more complex than multiplying matrices) and calculus (what are derivatives and integrals)

- Intro to statistics (just to know the vocabulary -- covariance, correlation, standard error, etc.)

- Rough overview of Machine Learning / AI as a whole and where its used in the world today

After that comes the second problem: "What do I do next?"

- coming up with several interesting but manageably small projects

- getting data for these projects

- access to quality advanced resources that can be consumed as needed while working on the project. A 3-month long MOOC on Neural Networks is an impractical resource. A well-written blog post (with code) or youtube video is far better.

MOOCs + textbooks seem to do better for the first problem if you can sift through all the noise, but fail at the second.