From the course: 15 Mistakes to Avoid in Data Science
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Moving too quickly
From the course: 15 Mistakes to Avoid in Data Science
Moving too quickly
- My name is Lacey Westphal. I have a PhD in molecular biology, but I'm no longer in science, I now work for the largest charter network in Los Angeles, Alliance College-Ready Public Schools, where I've been the manager of academic data analysis for the last three years. A common mistake that I've made in my role is to not give myself enough time to complete a project, and specifically not giving myself enough time to spot check the output of my analysis. When this happens, it's often because there are demands that are coming in from my supervisor that just have a really quick turnaround time. But the problem is when you're rushing through a project, when you're trying to get code out really quickly, you're going to make mistakes. And if you don't give yourself ample enough time to correct those mistakes in the output of the analysis, then your output is going to be wrong.
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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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