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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

Number systems

In this section, we introduce notation for describing sets of numbers. We will focus on the real numbers and the complex numbers.

Notation for numbers and fields

As this is a book about data science, we will be dealing with numbers. So, it will be worthwhile recapping the notation we use to refer to the most common sets of numbers.

Most of the numbers we will deal with in this book will be real numbers, such as 4.6, 1, or -2.3. We can think of them as “living” on the real number line shown in Figure 1.1. The real number line is a one-dimensional continuous structure. There are an infinite number of real numbers. We denote the set of all real numbers by the symbol .

Figure 1.1: The real number line

Figure 1.1: The real number line

Obviously, there will be situations where we want to restrict our datasets to, say, just integer-valued numbers. This would be the case if we were analyzing count data, such as the number of items of a particular...

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