Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning by Alex J. Gutman & Jordan Goldmeier

Author:Alex J. Gutman & Jordan Goldmeier [Gutman, Alex & Goldmeier, Jordan]
Language: eng
Format: epub
Tags: Business & Economics, Information Management, statistics, computers, Data Science, Data Analytics, Information Technology
ISBN: 9781119741718
Google: KCUqEAAAQBAJ
Publisher: John Wiley & Sons
Published: 2021-04-13T00:04:18.838523+00:00

Armed with this list, you'll be able to effectively challenge, understand, and appreciate the statistics you see.

NOTES

1 Don't forget to leave us a review on Amazon.

2 Recall from Chapter 1, a data science project must start with a clear question.

3 We realize 50% is an excellent shooting percentage in basketball. LeBron James, for example, has a career field goal percentage of 50%. So, no, your intern probably can't shoot that well, but 50% makes the math easier to follow. But good for you, being a Data Head and thinking, “Wait isn't that overly optimistic?”

4 O'Neil, C., & Schutt, R. (2013). Doing data science: Straight talk from the frontline. O'Reilly Media, Inc.

5 Review Chapter 1 for more on how to interrogate the question itself.

6 We used a two-sided Fisher's exact test.

7 This example would require something called an equivalence test. It's beyond the scope of this chapter but be aware it's out there. Tell your team and use it. If you can follow the logic of this chapter, you can understand an equivalence test.

8 “5 Sigma What's That?” blogs.scientificamerican.com/observations/five-sigmawhats-that

9 There are statistical ways to correct this. Look up the “Multiple Comparisons Problem.”

10 Effect size can take on many meanings in statistics. Here, we're simply talking about effect size as the difference between two numbers.

11 No, we did not actually run this test or collect data.

12 Using the statistical software R: `prop.test(c(65, 50), c(100, 100), alternative = "greater")`

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