Collaborative Data Science using Scalable Homoiconicity

Abstract

Motivation: Data science is increasingly collaborative. On the one hand, results need to be distributed, e.g., as interactive visualizations. On the other, collaboration in the data development process improves quality and timeliness. This can take many forms: partitioning a problem and working on aspects in parallel, exploring different solutions or reviewing someone else's work.