Data scientists are constantly challenged with improving their ML models. But when a new algorithm won’t improve your AUC there’s only one place to look: DATA. This guide walks you through six easy steps for data acquisition, a complete checklist for data provider due diligence, and data provider tests to

The “unreasonable effectiveness” of data for machine-learning applications has been widely debated over the years (see here, here and here). It has also been suggested that many major breakthroughs in the field of Artificial Intelligence have not been constrained by algorithmic advances but by the availability of high-quality datasets (see