From the course: 15 Mistakes to Avoid in Data Science
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Relying solely on formal education
From the course: 15 Mistakes to Avoid in Data Science
Relying solely on formal education
- In the terms of data science and really any technical profession and this probably expands out into any profession not just technical and data science. If you think that you know everything you need to know by the time you've graduated college, a you just couldn't be more wrong, the world is constantly changing. So if you think that just a formal education that stops down, you know, one day in time is going to tell you everything you need to know. I mean, the world changes the next day, there's no way that you can always know everything you need to know because everything is always changing. So I really want you to avoid the mistake of just relying fully on formal education, you know, and thinking that you know everything that you're going to need to know going into the working world after you've graduated college or gotten a master's degree even because the world is always changing and technology changes faster than…
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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