Automating Data Science: Prospects and Challenges
Automating Data Science: Prospects and Challenges (accepted by the Communications of the ACM)
arXiv paper abstract https://arxiv.org/abs/2105.05699v1
arXiv PDF paper https://arxiv.org/pdf/2105.05699v1.pdf
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.
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