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

Artificial Intelligence