[dantkz]

You actually do not contradict the author that much. The thing is, the skills that are required are somewhat standard programming and graduate level maths, which narrows down the number of people to graduate level computer scientists. As he mentioned, the data science is NOT JUST application of someone's algorithms to the data, you need to have skills to preprocess the data, be able to use distributed systems for big data, actually know what is going on under the hood, etc. Also, I doubt that the netflix and kaggle competition winners were anyones from anywhere, they probably already had quite a bit of experience with ML.

[OverKAnalytics]

Agree, unless your arguing that graduate level math(s) are only 'truly understood' by individuals with post-graduate math(s) degrees. In my opinion, reading (and practicing) is a viable alternative to the same skills. Or, as more famously said by Matt Damon... (http://www.youtube.com/watch?v=ymsHLkB8u3s)

[icelancer]

Actually, the strongest candidates I've screened have been self-learners who list Coursera or their own OSS projects on their resume with little academic background. (Our best analyst is a guy who is qualified to repair VCRs with his trade skill diploma in rudimentary electronics.)

[marshallp]

The original gigaom article was about neophytes (people who only took coursera course) beating out people with much more experience. This has occurred many times on kaggle. All the preprocessing etc. you're talking is not a new thing, a lot of database developers have been doing that for decades (and that's in addition to my original claim that pretty much anyone with a science or engineering degree having those skills as well). As I said elsewhere, this data science is hard meme is just a ploy to inflate salaries (which every career does anyway, so there's nothing particularly sinister about it).