Why Critical Thinking Skills Are Essential For Data Scientists
Data science requires a range of sophisticated technical skills—but don’t let that expertise get in the way of critical thinking.
As Alexander Pope said, to err is human. By that metric, who is more human than us data scientists? We devise wrong hypotheses constantly and then spend time working on them just to find out how wrong we were.
When looking at mistakes from an experiment, a data scientist needs to be critical, always on the lookout for something that others may have missed. But sometimes, in our day-to-day routine, we can easily get lost in little details. When this happens, we often fail to look at the overall picture, ultimately failing to deliver what the business wants.
Our business partners have hired us to generate value. We won’t be able to generate that value unless we develop business-oriented critical thinking, including having a more holistic perspective of the business at hand. So here is some practical advice for your day-to-day work as a data scientist.
1. Beware of clean data syndrome
Tell me how many times this has happened to you: You get a data set and start working on it straight away. You create neat…
