Virtual Roundtable: Translating Data Science, Bias in Data Sets and Analytics

Ensuring the proper curation and integrity of data from the earliest stages of collection is absolutely essential. For the quality of downstream results to be trusted, bias must be minimized and accounted for through thoughtful quality control methodologies. Join us as we explore how to best manage this critical concern.