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Data Scientists leverage common sense reasoning and domain knowledge tounderstand and enrich data for building predictive models. In recent years, wehave witnessed a surge in tools and techniques for {\em automated machinelearning}. While data scientists can employ various such tools to help withmodel building, many other aspects such as {\em feature engineering} thatrequire semantic understanding of concepts, remain manual to a large extent. Inthis paper we discuss important shortcomings of current automated data sciencesolutions and machine learning. We discuss how leveraging basic semanticreasoning on data in combination with novel tools for data science automationcan help with consistent and explainable data augmentation and transformation.Moreover, semantics can assist data scientists in a new manner by helping withchallenges related to {\em trust}, {\em bias}, and {\em explainability}.

A Survey on Semantics in Automated Data Science