Good tool set recommendations (+1 for mentioning SQL, immensely helpful), and I enjoy Kaggle. Not sure how critical top placement is, though. It seems like ge

Good tool set recommendations (+1 for mentioning SQL, immensely helpful), and I enjoy Kaggle. Not sure how critical top placement is, though. It seems like getting into the upper echelons of Kaggle is a matter of refining your model, and I do wonder how much value these refinements offer over a more basic and general approach in a real world scenario. To be clear, when I say I wonder, I'm not saying I'm rejecting the value, I really do mean it, I'm uncertain about the value. I think it's probably very scenario specific. Think of it this way - a predictive value of 90% vs 95% could be the difference between placing in the top 10% and the bottom third. Now, 5% isn't nothing, it could be very valuable. It really depends. But Kaggle is an environment where the question is already posed, the data has been collected, the test and train sets are already split apart for you, and winning model is the one that scores best on a hidden test set by a predefined goodness of fit score. In a real world scenario, suppose someone does a great job figuring out the question to ask, gathering the data, and determining the most effective way to act on the results, but uses a fairly basic, unrefined model. Someone else does a middling job on those things, but builds a very accurate model as measured by the data that has been collected. I'd say the first scenario is likely to be more valuable, but again, it depends of course. A couple other things, since I am a fan of Kaggle and do highly recommend it. First, these things aren't necessarily exclusive - you can have a particularly well conceived and refined model as well as a thorough and excellent businesss and data collection process (though you may have to decide where to put your time and resources). Also, refining a model with Kaggle can be an exceptional training opportunity to really understand what drives these things. So go for it! (I also find these things kinda fun).