Data cleaning is important to ensure accurate analysis from "summary" of Data Science For Dummies by Lillian Pierson
Data cleaning is a crucial step in the data analysis process. It involves identifying and correcting errors in the data to ensure its accuracy and reliability. Without proper data cleaning, the analysis results may be skewed or misleading, leading to incorrect conclusions. One common issue in data cleaning is missing values. These missing values can significantly impact the analysis results if not handled properly. Data scientists need to decide whether to remove the missing values, impute them with estimated values, or use other techniques to handle them. Another challenge in data cleaning is dealing with outliers. Outliers are data points that are significantly different from the rest of the data. ...
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