From the course: 15 Mistakes to Avoid in Data Science
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Assuming the knowledge level of stakeholders
From the course: 15 Mistakes to Avoid in Data Science
Assuming the knowledge level of stakeholders
- Another mistake that I have made and learned a lot from is assuming your audience knows what you're talking about. A lot of times, when you are presenting analysis to any type of stakeholder, it's easy to make assumptions about what they know about variability or how they understand variability, and to gloss over certain details when you're explaining the results of an analysis. But it's really important to take a step back and make sure that the audience you're speaking to is aware of what you're saying. If they're not, there's going to be a lot of misunderstanding about what your dataset is capable of doing.
Contents
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Communicating with overly technical language1m
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Skipping the fundamentals1m 5s
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Moving too quickly56s
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Having a data set that is too small1m
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Failing to adopt new tools1m 16s
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Not considering the level of variation1m 20s
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Lack of documentation1m 30s
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Relying solely on formal education1m 22s
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Taking too long to share results1m 10s
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Including your bias1m 1s
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Overpromising solutions to stakeholders1m 4s
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Building tools from scratch1m
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Assuming the knowledge level of stakeholders41s
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Not telling a story with the data1m 53s
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Not confirming with stakeholders1m 57s
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