Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3495256 | COMMUNICATIONS OF THE ACM |
DocType | Volume | Issue |
Journal | 65 | 3 |
ISSN | Citations | PageRank |
0001-0782 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tijl De Bie | 1 | 956 | 78.19 |
| Luc De Raedt | 2 | 5481 | 505.49 |
| Jose Hernandez-orallo | 3 | 995 | 100.10 |
| Holger H. Hoos | 4 | 5327 | 308.70 |
| Padhraic Smyth | 5 | 7148 | 1451.38 |
| Christopher K. I. Williams | 6 | 6807 | 631.16 |