March 2021
Beginner to intermediate
498 pages
12h 3m
English
Content preview from Cleaning Data for Effective Data Science
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Preface
In order for something to become clean, something else must become dirty.
–Imbesi’s Law of the Conservation of Filth
Doing the Other 80% of the Work
It is something of a truism in data science, data analysis, or machine learning that most of the effort needed to achieve your actual purpose lies in cleaning your data. The subtitle of this work alludes to a commonly assigned percentage. A keynote speaker I listened to at a data science conference a few years ago made a joke—perhaps one already widely repeated by the time he told it—about talking with a colleague of his. The colleague complained of data cleaning taking up half of her time, in response to which the speaker expressed astonishment that it could be so little as 50%.
Without ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.