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Simplified Data Quality Monitoring of Dynamic Longitudinal Data: A Functional Programming Approach


Data scientists are often tasked with being the first to detect issues of data quality that may have serious consequences for downstream consumers of their data. We will demonstrate how to simplify data quality monitoring through a functional programming approach that empowers the 5 key pillars of a user-friendly workflow: readability, compositionality, reproducibility, efficiency, and robustness.


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