When Data Science Goes Wrong: How Misconceptions About Data Capture and Processing Causes Wrong Conclusions
Peter Christen, Rainer Schnell · Harvard Data Science Review · 2024
Conclusions 2Column Editor's Note: In an era of large, complex data, it is important for data scientists to understand how data being analyzed have been acquired, processed and linked.In this Diving Into Data column piece, Professors Christen and Schnell explore and categorize key aspects of data provenance, highlighting issues that can arise and providing recommendations to help readers identify problems and avoid resulting errors.