The Biggest Mistakes Made by Data Scientists - InformationWeek

In 2019, companies looking to gain an edge on competitors and insight into customers and trends have come to rely more heavily on data scientists to inform their business decisions. A good data scientist is invaluable to a company with any online presence. They will assess and interpret complex information and build out machine learning algorithms. Data volume keeps growing, and the amount of skill and effort needed to create data-driven initiatives is certainly keeping pace with that growth. Mistakes can produce huge consequences and, while the tools may change, the mistakes stay the same. Over the course of my career I've seen every permutation of these common mistakes, and my hope here is to help you identify and avoid them within your own teams.