Andrew Escay

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Is garbage in always garbage out?

People who have been interested in any form of data science work have often heard these two sayings: you will spend more time cleaning your data than running analysis on it; and garbage in, garbage out. In any data science project, clean data is almost always a prerequisite for accurate outcomes. There are several reasons for this: our analysis can impact real lives on the ground if we’re working with community-facing information and we don’t want to make biased conclusions; ...

Author Andrew Escay
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