  Current automated machine learning (ML) tools are model-centric, focusing on
model selection and parameter optimization. However, the majority of the time
in data analysis is devoted to data cleaning and wrangling, for which limited
tools are available. Here we present DataAssist, an automated data preparation
and cleaning platform that enhances dataset quality using ML-informed methods.
We show that DataAssist provides a pipeline for exploratory data analysis and
data cleaning, including generating visualization for user-selected variables,
unifying data annotation, suggesting anomaly removal, and preprocessing data.
The exported dataset can be readily integrated with other autoML tools or
user-specified model for downstream analysis. Our data-centric tool is
applicable to a variety of fields, including economics, business, and
forecasting applications saving over 50% time of the time spent on data
cleansing and preparation.
