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Comment by yankoff | Hacker News Reader

Hm, looks like this argument about Kaggle pops up all the time.

This is true, that getting and cleaning data is an important part of a workflow in data science and it's a necessary skill to have. But I don't believe anyone ever claimed Kaggle is enough to become a good data scientist. That's like studying algorithms is very useful, but who said it's enough to become good at software engineering? The skill consist of multiple parts which can be practiced separately or in combination in a real project where you do everything from scratch.

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It's not as straightforward as Kaggle representing one stage in a pipeline. Kaggle also fixes this stage in an unnatural way.

In real life, there is a feedback loop from modelling, to data processing. In Kaggle all you can do is improve your model. You can't go back and change how data was processed, or collect different sorts of data.

Also, some flows simply don't fit into Kaggle's train/test paradigm. E.g. suppose you have an online algorithm that continuously updates parameters. Or situations where the model is used to generate the train/test data.

Finally some situations may have stringent computational requirements, e.g. <100ms to classify a single instance.

Sure, everything "in real life" is more complicated. I think Kaggle to data science is as programming contests to Software Engineering. Will you say that doing programming challenges is "unnatural"? Is it useless or bad? Because you may also argue that you never have to solve problems for speed, you rarely see clearly-defined problems, you rarely get to apply complicated or rare algorithms in real life situation.

The thing is Kaggle helps developing certain skills. It doesn't mean it helps you developing all necessary skills for your "real life". Your real life and your job may require unique combination of skills and the only way to fully prepare for it is to actually do real project that are specific to your job.

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