As data scientists and ML engineers, we often dedicate countless hours to fine-tuning hyperparameters of complex algorithms, particularly neural networks, in pursuit of optimal model performance. This talk will introduce a set of powerful open-source libraries, developed by the speaker, that enable a similar level of optimization for your data itself. Discover the capabilities of pandas-dq, featurewiz, Sulo model class, and lazytransform libraries as they empower you to enhance data quality, augment your data through feature engineering, and streamline feature selection for tabular datasets, including time series data.