From the course: Insights on Data Science: Lillian Pierson

What are some common misconceptions in the field?

From the course: Insights on Data Science: Lillian Pierson

What are some common misconceptions in the field?

- There are a few misconceptions in this field. One of them is a lot of companies are building applications to try and it seems like replace the data scientist, thinking we can build an application that will do what a data scientist does. And I think that that's not possible, because it's not just coding. A data scientist also has subject matter expertise and the mathematical and statistical know-how to understand and interpret the insights. So if you build a computer application that generates the insights for someone, that doesn't mean that they're going to understand the implications of what that data is telling them or how to use that information to affect a business. Another common misconception I have heard about is something called a citizen data scientist, and I'm pretty sure that term is referring to saying that everyone is a data scientist because everyone is interacting and creating data and using data. And while that's true, data science involves application of analytical methods and subject matter expertise, knowing how to apply data insights to a business. And so not every citizen is a data scientist.

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