Data management and cleaning are essential components of effective data processing and analysis. Data management refers to the overall process of collecting, storing, organizing, and analyzing data. Data cleaning is a specific aspect of data management that involves identifying and correcting or removing errors, inconsistencies, and inaccuracies from the data. This process ensures that the data is accurate, complete, and consistent, which is crucial for effective analysis and decision-making. Data cleaning involves a range of techniques, including outlier detection, missing value imputation, and data normalization. Effective data cleaning can help to reduce errors, increase the accuracy of analysis, and improve the overall quality of the data. It is a critical step in the data management process that ensures the reliability and usefulness of the data.
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