I'm a data scientist at company that is big enough to have a six-person data science team. Our CTO bought the data science team all copies of this book (mine is sitting on my desk right now). The best thing about this book is the cover. Which is not to say that the book is terrible —the cover is a really pretty picture of a kiwi. The only chapter in this book that was really interesting was Chapter 5, "Information Platforms and the Rise of the Data Scientist", by Jeff Hammerbacher, who edited the book. The rest is pretty fluffy. Nice easy reading, but nothing really useful or all that interesting.

Mod up parent. I was going to point out the same thing. How the heck could this not be causation?

Very true. On the seeding issue, note that even for events with a substantial amount of randomness inherent, like key presses or other I/O events, the high-order bits are still quite predictable. For example, the intervals between keyboard events tend to follow a Pareto distribution (power law basically). That's why the kernel only uses the very lowest few bits of data from these sources to generate entropy.

I may be wrong, but I believe that the difference between /dev/random and /dev/urandom is that the former only provides true hardware-generated entropy and blocks when that's not available, whereas the latter uses true entropy and cryptographically strong PRNG to generate stream of data that has a variable level of true entropy mixed into it over time. Can anyone corroborate that and/or refute that? (References a plus!)

[B.v.L]