Kaggle is a great idea; I actually entered the industry partly through a data mining contest (pre-kaggle). But I think the article overstates Kaggle's influence on industry, at least at this point in time.<p>>the Kaggle ranking has become an essential metric in the world of data science. Employers like American Express and the New York Times have begun listing a Kaggle rank as an essential qualification in their help wanted ads for data scientists<p>No, it hasn't, and the nytimes job posting they link to doesn't list it as an "essential" qualification (they didn't link to the amex posting). I know many people with experience in the data science space and very few of us have taken part in a Kaggle competition. It's not that there is anything wrong with Kaggle, but the pay is low for the required effort to differentiate yourself. Many modeling competitions I've seen require an inefficient use of time in the "diminishing returns" part of the process, which means winning requires a lot of free time. I worked with a guy who won a couple prominent data modeling competitions, and frankly I thought he was a mediocre data scientist (but a very hard worker).<p>I sometimes wonder who takes part in the competitions; and then I remember myself from six years ago, applying for jobs and looking for a way to make my resume stand out.
I think Kaggle is great, but I don't really see the model of "work full time for months competing with 80,000 other people in hopes that your data model will turn out 0.001% more accurate than everybody else's [which at some point becomes largely a function of the initial seed on your RNG when you fit the model] in order to get a single pay check" to be a workable concept for work.
A lot of commenters here seem to have missed this: "The really disruptive thing about Kaggle, though, comes through the company's new service, Kaggle Connect. Here, Kaggle acts as a match-maker, where customers with a specific problem can hire a specific data scientist well-suited to their problem; candidates are drawn the top tier of Kaggle participants: the top 1/2 of 1 percent, or about 500 data scientists."<p>The competitions are a good way to learn, practice, and get feedback on your methods. Kaggle Connect is where you can make a good living while doing a range of interesting work.<p>(I work for, and compete on Kaggle).
The article is sorely mistaken: a job posting that says "Kaggle participation will give you an advantage" does not mean Kaggle provides "essential metric[s]." That's just one example of the article overstating Kaggle's case.<p>Also love the irony of explaining the importance of a data science competition host by citing Tom Friedman, the King Of Generalizing Anecdotes.
It's interesting how differently the data science world has embraced this model vs. designers, of which a vocal set seem to scoff at any mention of 99designs. (I am currently competing in a Kaggle competition and the competitive factor is what makes it fun)<p>That said, I doubt more than a handful of people could make a living off competition winnings alone.
The cynic in me says, "How Kaggle wastes the time of many talented individuals while enabling corporations to give the finger to their staff."<p>I should start providing prizes for the best start-up. I'll give you a $20k prize and then turn around and sell it for $1M or more. You can put it on your resume. Win win.<p>EDIT: Sure, it's good for various things, but it is so detached from the reality that it's a bit out in the thicket. The cynic in me just doesn't get over the value handed over by competitors to the sponsors.
I cant see how Kaggle is good for the data scientist (but I can certainly see how it is good for the hiring companies). It seems like the design spec work. I find it horendous that people do work for a company and most of them never get paid for it. It is disgusting. The top #1 in Kaggle according to the article only got paid 6 times. Lets put it into perspective. Companies that wanted web developers stopped hiring them and started using a similar service, in which, everyone would create a version of the application, with only the frontend available for other contestants, in order to foster competition. Then the company chose the best one and only the winning team would get paid. This is capitalism at its worst, and long-term it is severely harmful. In a modern work-rights aware society this should be illegal, spec work that is... At least the communities should critic it in my opinion and not embrace it.
There was a similar website, where customers posted a bounty for the cheapest travel itinerary meeting certain conditions, and anyone could submit a plan meeting those conditions, to try and win the bounty. Does anyone remember what it was called?
Kaggle is pretty important. But concentrating only on accuracy tuning (ignoring data collection, data curation, and interviewing stakeholders to find real business needs) in machine learning is like celebrating only (premature) optimization in software engineering.<p>But don't get me wrong, lots of top notch results in the contests. It is just that it is testing only one facet of what is needed in a data scientist.
Big numbers of data crunchers in a big play. I wonder how many top scientists are themselves spurious, therefore, random leaders, since there are 85 thousands of them.
Is Kaggle really a science place or a gamble?
You know, we have seen things like that in any prediction market, e.g. financial.
I find irony in the cognitive dissonance betwixt these comments, and these: <a href="https://news.ycombinator.com/item?id=5540841" rel="nofollow">https://news.ycombinator.com/item?id=5540841</a>
I'm not anti-Kaggle, but this is not the future.<p>Credible people will do one or two competitions because they care about the problem, and because they want to establish themselves well enough to get better jobs, etc. If it works for them, great. If it doesn't, they'll get bored and quit. In that case, the best people leave and you have a ghetto.<p>Right now, Kaggle makes sense because "data science" is still an ill-defined field but a lot of people want to get into it, and no one knows what it means or takes to get in, so people will try things out to see what happens.<p>If Kaggle wants to stay in play for the long term, they'll need to get <i>really</i> good at connecting talented people with very high-quality jobs.<p>There is something that I don't think all of the hiring-related startups get yet: as things are, there's such a shortage of quality jobs. That's a 5-year existential threat to the whole business model. What happens when people realize that these sexy startup jobs are just corporate jobs with better marketing? What happens when the dream dies? Right now, high-quality jobs are too rare for the hiring startups (unless the genuinely change the economy) to prevent people from getting just as disillusioned with these new services as they are with headhunters. Now, that's not because there's an intrinsic limit on interesting work (see: Lump of Labor fallacy) but we'd have to overthrow the management of a whole industry to change that.<p>Now, data science. It is attractive right now because it carries with it a promise of what <i>software engineering</i> was supposed to deliver but, for most people, doesn't: interesting work, implicit respect, autonomy. I feel like data scientist in many companies means "software engineer who gets dibs on the most interesting projects". I'm afraid that title inflation into the data science field might dilute that, however.<p>What we really need is to fire 90+ percent of software managers and trust engineers to pick their own projects and call some shots. I don't know how to turn that into a specific startup idea, but it will solve a lot of problems.