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15 Math Concepts Every Data Scientist Should Know

You're reading from   15 Math Concepts Every Data Scientist Should Know Understand and learn how to apply the math behind data science algorithms

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781837634187
Length 510 pages
Edition 1st Edition
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Author (1):
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David Hoyle David Hoyle
Author Profile Icon David Hoyle
David Hoyle
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Essential Concepts FREE CHAPTER
2. Chapter 1: Recap of Mathematical Notation and Terminology 3. Chapter 2: Random Variables and Probability Distributions 4. Chapter 3: Matrices and Linear Algebra 5. Chapter 4: Loss Functions and Optimization 6. Chapter 5: Probabilistic Modeling 7. Part 2: Intermediate Concepts
8. Chapter 6: Time Series and Forecasting 9. Chapter 7: Hypothesis Testing 10. Chapter 8: Model Complexity 11. Chapter 9: Function Decomposition 12. Chapter 10: Network Analysis 13. Part 3: Selected Advanced Concepts
14. Chapter 11: Dynamical Systems 15. Chapter 12: Kernel Methods 16. Chapter 13: Information Theory 17. Chapter 14: Non-Parametric Bayesian Methods 18. Chapter 15: Random Matrices 19. Index 20. Other Books You May Enjoy

What is information and why is it useful?

As we said in the introduction, information theory and information-theoretic ideas are very useful. To understand those ideas, one of the first things we must address is what we mean by information. I am talking conceptually here. Once we have nailed down our conception of what information is about, writing down a mathematical definition will be easier.

The concept of information

Part of the difficulty in introducing information theory as a mathematical subject is that different people use the word “information” and apply it to different concepts. For example, the word “information” could apply to the following:

  • The semantic content or meaning of an action or event, such as a particular word being spoken or written. This is the most common conception that people have of what information should be about. It is the idea that the information associated with a thing should somehow be related to what that...
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