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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

Linear algebra

Remember the movie recommendation engine we looked at earlier? What if we had 10,000 movies to recommend and we had to choose only 10 to give to the user? We'd have to take a dot product between the user profile and each of the 10,000 movies. Linear algebra provides the tools to make these calculations much more efficient.

It is an area of mathematics that deals with the math of matrices and vectors. It has the aim of breaking down these objects and reconstructing them in order to provide practical applications. Let's look at a few linear algebra rules before proceeding.

Matrix multiplication

Like numbers, we can multiple matrices together. Multiplying matrices is, in essence, a mass produced way of taking several dot products at once. Let's, for example, try to multiple the following matrices:

Matrix multiplication

A couple of things:

  • Unlike numbers, multiplication is not commutative, meaning that the order in which you multiply matrices matters a great deal.
  • In order to multiply matrices...
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