  The recent efforts in automation of machine learning or data science has
achieved success in various tasks such as hyper-parameter optimization or model
selection. However, key areas such as utilizing domain knowledge and data
semantics are areas where we have seen little automation. Data Scientists have
long leveraged common sense reasoning and domain knowledge to understand and
enrich data for building predictive models. In this paper we discuss important
shortcomings of current data science and machine learning solutions. We then
envision how leveraging "semantic" understanding and reasoning on data in
combination with novel tools for data science automation can help with
consistent and explainable data augmentation and transformation. Additionally,
we discuss how semantics can assist data scientists in a new manner by helping
with challenges related to trust, bias, and explainability in machine learning.
Semantic annotation can also help better explore and organize large data
sources.
