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arXiv · 2105.05699

Automating Data Science: Prospects and Challenges

Tijl De Bie·Luc De Raedt·José Hernández-Orallo·Holger H. Hoos·Padhraic Smyth·Christopher K. I. Williams
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Abstract

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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Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams. 2021-05-12. Automating Data Science: Prospects and Challenges. https://doi.org/10.1145/3495256

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