This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field.
Anyone with a modicum of industry experience knows that there is an awful lot of bad code out there. It's not that it's just unsightly. Code that is not clean can quite easily move beyond being a f...
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