From the course: 15 Mistakes to Avoid in Data Science
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Communicating with overly technical language
From the course: 15 Mistakes to Avoid in Data Science
Communicating with overly technical language
- So my name is Sarah Anstey and I'm a business intelligence architect at a cybersecurity consulting firm called Novacoast. So a common mistake that I see with a lot of data scientists or data analysts, is that they are trying to convey their findings in the data in a really technical or heavily statistically oriented way. To stakeholders who aren't able to speak that same language. So a lot of the time in data science and analytics, the people asking the questions and the people with the problems are high level managers. They're maybe on the C level or VPs, you know, people who are high up. Who one don't have much time to talk to you. So you have to keep it short and brief and concise. And two don't have a huge technical background. And if you can't relay what you found in a nontechnical way, that makes sense to them, then you might as well not have found anything. Because you can do the coolest data analysis, or write the…
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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