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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

Random variables and probability distributions

We start this section by introducing a new concept that is necessary to describe the randomness we find in data.

A new concept – random variables

In computer code, when we want to use a variable, we type something such as x=5. In many programming languages, we may change the value of the variable x. We even use the word variable to indicate that its value may change. However, those changes are caused by us or by code we have written, and so typically they happen in a deterministic way; that is, we compute when the changes should happen, and we can compute the new value of the variable.

For data that contains a random component, we need a new concept. Remember – random means non-predictable. When we record, observe, or capture the value of that variable, its value is not pre-determined. Instead, it could take on a number of values. The new concept we need is that of a random variable. A random variable is a variable...

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