From the course: 15 Mistakes to Avoid in Data Science

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Not confirming with stakeholders

Not confirming with stakeholders

From the course: 15 Mistakes to Avoid in Data Science

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Not confirming with stakeholders

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- Another common mistake is to assume you know the intended output of an analysis without confirming it with other stakeholders. For example, you're five days into a project, you've cleaned your data, you've written your code, you're starting to visualize that data, and you show it to your supervisor, and your supervisor says to you, "Oh this wasn't the question "we wanted to answer with this data." It's really important at the very beginning of your project to get input from all of the collaborators you can. Anyone who has any insight into the dataset you're using, any insight into the actions that would be taken from such a dataset, are really important perspectives to get before you start any analysis because it will help shape the types of questions you're answering, and whether those questions that you're answering are even useful to the stakeholders in your organization. When I first started working as the…

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