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.Comment: 19 pages, 3 figures. v1 accepted for publication (April 2021) in
Communications of the AC