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For Q1, check out these two stats.SE posts: Why does XGBoost have a learning rate? XGBoost Loss function Approximation With Taylor Expansion They bring up questions about the goodness of the second-order expansion, which is even more strict than asking about radius of convergence, and I think the discussion probably answers this reasonably well. I suspect ...


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I was educated as a mechanical engineer with minors in CS and math. In my personal experience, knowledge of math can be super useful in signal processing applications, and doing some creative/complex stuff with the data, but as many others have said, I think a good math "feel" is much more impactful than an explicit ability to do the math yourself.


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No, you don't need mathematics for data science in the same way that you need it for physics. As a data scientist, you won't be integrating a stress-energy tensor, or even solving a differential equation. What you do need is good quantitative reasoning and critical thinking skills. NotThatGuy's answer gives good examples of things you might think of as &...


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Statistical knowledge or statistical thinking is useful or necessary to: Understand, evaluate and pick appropriate metrics to use to evaluate the performance of models. You need to understand the real-world cost of prediction errors and how each metric relates to this. Explore and understand the data e.g. to help inform future models or other business ...


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Having a solid mathematical background is crucial for data science. Someone without solid mathematical background will always use the algorithms as black box models. Mathematical reasoning is needed when you debug your models but also when you want to come up with a creative solution to a problem at hand. Someone without mathematical background will have a ...


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