From the course: 15 Mistakes to Avoid in Data Science
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Taking too long to share results
From the course: 15 Mistakes to Avoid in Data Science
Taking too long to share results
- Because data scientists, sometimes we get so ingrained and the problem we're working on that it takes us a long time to get to an answer and sometimes we never get to an answer. You can go and rework a problem or rebuild a model or change X, Y and Z about everything you're doing so many times and at some point you do need to come up with an answer. I got good advice at one point that, art is never finished, only abandoned. And with that, that means you could keep doing what you're doing forever. You know, you're trying to increase that accuracy by 1%. What does that really do? If we spend another two months on the same exact problem, that's a lot of money and time being spent where we could've just went and implemented and tested to see if it was working or not. My recommendation is,try to break your model, have somebody else try to break it and if there's no glaring issues, then go with it, test it and…
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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