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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 2. Types of Data FREE CHAPTER 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

Summary

Though this is only our first look at data exploration, don't worry—this is definitely not the last time we will follow these steps for data science and exploration. From now on, every time we look at a new piece of data, we will use our steps of exploration to transform, break down, and standardize our data. The steps outlined in this chapter, while they are only guidelines, form a standard practice that any data scientist can follow in their work. The steps can also be applied to any dataset that requires analysis.

We are rapidly approaching the section of the book that deals with statistical, probabilistic, and machine learning models. Before we can truly jump into these models, we have to look at some of the basics of mathematics. In the next chapter, we will take a look at some of the math necessary to perform some of the more complicated operations in modeling, but don't worry—the math required for this process is minimal, and we will go through it step...

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