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Principles of Data Science

You're reading from   Principles of Data Science Mathematical techniques and theory to succeed in data-driven industries

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Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781785887918
Length 388 pages
Edition 1st Edition
Languages
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Author (1):
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Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Table of Contents (15) Chapters Close

Preface 1. How to Sound Like a Data Scientist FREE CHAPTER 2. Types of Data 3. The Five Steps of Data Science 4. Basic Mathematics 5. Impossible or Improbable – A Gentle Introduction to Probability 6. Advanced Probability 7. Basic Statistics 8. Advanced Statistics 9. Communicating Data 10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials 11. Predictions Don't Grow on Trees – or Do They? 12. Beyond the Essentials 13. Case Studies Index

Collectively exhaustive events


When given a set of two or more events, if at least one of the events must occur, then such a set of events is said to be collectively exhaustive.

Consider the following examples:

  • Given a set of events {temperature < 60, temperature > 90}, these events are not collectively exhaustive because there is a third option that is not given in this set of events: The temperature could be between 60 and 90. However, they are mutually exhaustive because both cannot happen at the same time.

  • In a dice roll, the set of events of rolling a {1, 2, 3, 4, 5, or 6} are collectively exhaustive because these are the only possible events, and at least one of them must happen.

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