Sorry to say, but I disagree with the premise of this article. You still need R to answer many of the questions the author of the article is proposing to answer.
On top of that, having a degree in AI will prepare you for a lot of real-world problems because you've been lots of data sets. I'd prefer to have an PhD-level researcher who knows why my support vector machine is overtrained, than a guy with real-world experience who is likely to overtrain and bias the model.
Furthermore, the author's example of weight and height correlation isn't one of a bad model. It would be a perfectly fine model if that was all the data you had. If you ignore lots of input data to build a crappy model, you're going to have a bad time... But everyone with a PhD and now "real world experience" would know this.
I've seen quite a few startups with "normal" (say, 60 hours or less per week) hours. That being said, the people I know who put in 100+ hours per week on their startup also have been more successful (long-lived company, multiple millions in revenue per year, etc.).
I think it's possible to have a startup that promotes a sustainable lifestyle, but it's all about costs and benefits. If you work 100+ hours per week, you might miss out on seeing your kids grow up (or having any in the first place). Working ~50 hours instead might make your business grow more slowly but might allow you to enjoy other (more important, for some) aspects of life.
As a side note: some of the investors I've talked to also encourage and prefer a more sustainable lifestyle in a startup. That way you'll be alive in the long run. :)
I don't want to name any names or give explicit examples here, but happy to discuss you're making a difficult decision.
Would love to hear your thoughts on the topic. Either here or on the blog. Much of my work now focuses around lowering the barrier to entry for analytics (particularly in traditionally more qualitative fields), hence the post.
While this is more anecdotal, I know quite a few financial firms use neural networks for their stock trading algorithms. It's less about whether or not they're the right tool, and a matter of using every tool there is to see what happens to work best. Here's a very brief example: http://www.i2r.org/nnstocks.pdf
I'm from Canada and am very surprised at Canada's high rankings. Wondering if the authors took into account the relative differences between university / college systems across countries. Canada has a lot more "colleges" (i.e. technical schools) relative to universities -- our terminology is literally different here. Curious if it in any way affects the results.
There's also a related issue of brain drains / gains to related industries (e.g., banking or corporate analytics) within countries.
On top of that, having a degree in AI will prepare you for a lot of real-world problems because you've been lots of data sets. I'd prefer to have an PhD-level researcher who knows why my support vector machine is overtrained, than a guy with real-world experience who is likely to overtrain and bias the model.
Furthermore, the author's example of weight and height correlation isn't one of a bad model. It would be a perfectly fine model if that was all the data you had. If you ignore lots of input data to build a crappy model, you're going to have a bad time... But everyone with a PhD and now "real world experience" would know this.