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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

Chapter 4. Basic Mathematics

It's time to start looking at some basic mathematic principles that are handy when dealing with data science. The word math tends to strike fear in the hearts of many, but I aim to make this as enjoyable as possible. In this chapter, we will go over the basics of the following topics:

  • Basic symbols/terminology
  • Logarithms/exponents
  • The set theory
  • Calculus
  • Matrix (linear) algebra

We will also cover other fields of mathematics. Moreover, we will see how to apply each of these to various aspects of data science as well as other scientific endeavors.

Recall that, in a previous chapter, we identified math as being one of the three key components of data science. In this chapter, I will introduce concepts that will become important later on in this book—when looking at probabilistic and statistical models—and I will also be looking at concepts that will be useful in this chapter. Regardless of this, all of the concepts in this chapter should be...

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