Human-Machine Collaboration for Democratizing Data Science
This aims to democratize data science for non-experts, though it appears incremental as it builds on existing spreadsheet and AI techniques.
The paper tackles the problem of limited data science expertise by introducing VisualSynth, a framework for human-machine collaboration that allows users to perform data analysis tasks through colored sketches in spreadsheets, achieving automation of tasks like data wrangling and predictive modeling.
Everybody wants to analyse their data, but only few posses the data science expertise to to this. Motivated by this observation we introduce a novel framework and system \textsc{VisualSynth} for human-machine collaboration in data science. It wants to democratize data science by allowing users to interact with standard spreadsheet software in order to perform and automate various data analysis tasks ranging from data wrangling, data selection, clustering, constraint learning, predictive modeling and auto-completion. \textsc{VisualSynth} relies on the user providing colored sketches, i.e., coloring parts of the spreadsheet, to partially specify data science tasks, which are then determined and executed using artificial intelligence techniques.