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Section 2.5 Test Yourself: Identifying Data Problems
Checkpoint 2.5.1.
According to the text, what must a data scientist do to successfully identify a data problem in a specific domain like farming?
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Sit in front of a computer all day and work with a program like R.
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No, the text explicitly states that this impression is a mistake.
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Focus only on reading books and watching videos about the domain.
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No, the text says the best way to gain domain knowledge is to ask subject matter experts.
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Immediately begin verifying stories told by professionals.
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No, the first step is to get the professional to tell the stories; verification comes later.
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Become immersed in the problem domain to think like a subject matter expert.
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Correct! The text emphasizes that a data scientist must learn to think like a farmer to identify a farmer’s data problems.
Checkpoint 2.5.2.
What is the first step in identifying an anomaly?
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Look for the exception cases that are far from the center.
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No, this is what you do after you have defined the center or typical case.
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Focus attention on a small grouping of trees that lost more fruit.
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No, this is an example of a potential anomaly, not the first step in the process of identifying one.
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Define the central or most typical occurrences.
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Right! You first need a yardstick for what is "typical" before you can identify what is "unusual".
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Perform a systematic count of lost fruit under a random sample of trees.
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No, this is a method used to establish what is typical, but defining "typical" is the actual first step.
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