Democratizing data science is the notion that anyone, with little to no expertise, can do data science if provided ample data and user-friendly analytics tools. MIT researchers are hoping to support that idea with a new tool for nonstatisticians that automatically generates models for analyzing raw data. The tool ingests data sets and generates sophisticated statistical models typically used by experts to analyze, interpret, and predict underlying patterns in data.

The tool currently lives on Jupyter Notebook, an open-source web framework that allows users to run programs interactively in their browsers. Users need only write a few lines of code to uncover insights into, for instance, financial trends, air travel, voting patterns, the spread of disease, and other trends.

In a paper presented at this week’s ACM SIGPLAN Symposium on Principles of Programming Languages, the researchers show their tool can accurately extract patterns and make predictions from real-world data sets, and even outperform manually constructed models in certain data-analytics tasks.

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