What is the focus of the Data Preparation stage in data science?

The focus of the Data Preparation stage in data science is primarily on correcting invalid values and addressing outliers. This stage plays a crucial role in ensuring the quality and reliability of the data that will be used for analysis. During data preparation, practitioners identify and fix inconsistencies in the dataset, such as erroneous entries, and they also deal with outliers that can skew results and lead to misleading conclusions.

By addressing these issues, analysts can enhance the accuracy of their models and improve the overall insights gained from the data. This careful preparation helps in building robust predictive models, as it lays a solid foundation by providing clean, reliable data. This process includes techniques like imputation for missing values, normalization for scale differences, and potentially removing or transforming outliers to ensure that the final dataset is as representative as possible for analysis.