Thinking Outside Data Science’s Many Boxes

The two pillars of data science—statistics-backed analysis and code—come with a whole range of constraints. Structure your query the wrong way, and you might mess up an entire pipeline. Apply a formula incorrectly, and the results of your test might no longer reflect reality.

Working within constraints doesn’t have to feel rigid or limiting, though—on the contrary. A nimble data professional often needs to lean into their creativity while still working within a predefined set of parameters. This holds true …

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