When data science projects fail
PyData Ann Arbor | August 14, 2019
Everyone loves talking about successes, but data science projects fail all the time. Datasets don’t end up having signals, the work takes far longer than expected, and products end up missing the mark. In this recorded talk I examine the key themes that show up in projects that fail and how data scientists can spot them coming. To highlight these themes I use examples from the many failed data science projects that I have been personally responsible for.