Data Quality
Data Quality refers to the accuracy, completeness, consistency, reliability, and timeliness of data. It is the degree to which data meets the requirements of its intended use. In Data Science, data quality is crucial as it directly impacts the accuracy and reliability of the insights and decisions made from the data. Poor data quality can lead to incorrect conclusions, flawed models, and unreliable predictions. Data quality can be improved through various methods such as data profiling, data cleansing, and data validation. Data profiling involves analyzing the data to identify any inconsistencies or errors. Data cleansing involves correcting or removing any errors or inconsistencies found during data profiling. Data validation involves ensuring that the data meets the required standards and is fit for its intended use.
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