Data discovery and data preparation have always been among the most time-intensive steps of a research project and for good reason: If there are unaddressed errors in data, or if the wrong version of data is used, there will be errors in the resulting analysis or model. In some cases these errors could render the analysis or model completely useless. This is why thorough data preparation is essential for accurate analyses and accurate models. Data preparation will likely always be a major step in the data science process. However, data scientists can speed up the time spent on data prep tasks with a well-documented and curated data catalog, a repository of data cleaning functions, and of course, Python tools and libraries created especially for more efficient data prep. This guide takes a look at some tools and tips to make each step of the data preparation process more efficient.
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