Introduction
Avoid common mistakes to excel in data science
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1. Mistakes to Avoid
Communicating with overly technical language
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Skipping the fundamentals
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Moving too quickly
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Having a data set that is too small
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Failing to adopt new tools
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Not considering the level of variation
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Lack of documentation
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Relying solely on formal education
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Taking too long to share results
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Including your bias
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Overpromising solutions to stakeholders
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Building tools from scratch
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Assuming the knowledge level of stakeholders
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Not telling a story with the data
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Not confirming with stakeholders
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Conclusion
Get started on the right path
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