  Given the complexity of typical data science projects and the associated
demand for human expertise, automation has the potential to transform the data
science process.
  Key insights:
  * Automation in data science aims to facilitate and transform the work of
data scientists, not to replace them.
  * Important parts of data science are already being automated, especially in
the modeling stages, where techniques such as automated machine learning
(AutoML) are gaining traction.
  * Other aspects are harder to automate, not only because of technological
challenges, but because open-ended and context-dependent tasks require human
interaction.
