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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

How does statistical modeling fit into all of this?


Up until now, I have been using the term machine learning, but you may ask how statistical modeling plays a role in all of this.

This is still a debated topic in the field of data science. I believe that statistical modeling is another term for machine learning models that heavily relies on using mathematical rules borrowed from probability and statistics to create relationships between data variables (often in a predictive sense).

The remainder of this chapter will focus mostly on one statistical/probabilistic model—linear regression.

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