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Data Tips for Data Requesters The following table contains information about data quality or data nuances that affect data in the Arkansas APC. This information is provided at the Submitting Entity
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01
Identify the dirty data in your dataset by conducting a thorough data analysis.
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Determine the types of dirty data that exist, such as missing values, inconsistent formatting, and duplicates.
03
Develop a data cleaning plan to address each type of dirty data.
04
Use data cleaning techniques like imputation to fill in missing values.
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Standardize inconsistent formatting by applying data transformations or formatting rules.
06
Remove duplicates from the dataset using deduplication methods.
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Validate the cleaned data to ensure its accuracy and integrity.
08
Document the cleaning process and keep track of any changes made to the dataset.
09
Regularly monitor the data for new instances of dirty data and update the cleaning process accordingly.

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Data analysts and scientists who work with large datasets.
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Data engineers responsible for data quality and data integration tasks.
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Researchers and academics analyzing datasets for studies and experiments.
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Anyone who wants to improve the reliability and usefulness of their data.

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