The author’s book titles sound interesting, but after reading the article I don’t think they’ll go on my reading list. The examples he gives seem weak, or obvious, or just plain uninteresting, and he doesn’t explain them well.
For example he cites a stock picker’s methodology. There are literally thousands of these books, so it’s not hard to find one that did poorly going forward. About half will probably do worse than average, nothing amazing here.
Google Flu was meant to be an experiment. They made a thing, and said “let’s see if it can predict the flu”. It didn’t. Experiment wasn’t a success, so what?
Even the Feynmann example I don’t get. It seems Feynmann was really telling the students, if you want to solve a problem do it the obvious way: to get the odds of a certain car plate existing in a parking lot go out and LOOK in the parking lot for that car plate. If it’s not there, the odds are zero. If it is there, the odds are 1. Instead the silly students started figuring out probabilities. What does computing a probability have to do with data mining?
Also a student put a fish in an MRI, disproving MRI machines I guess.
Maybe it’s his writing style but I left the article a cheerleader for big data mining. At least it comes up with interesting results, true or not!
