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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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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 a random matrix?

A random matrix sounds like it is some esoteric mathematical object – the sort of thing that is studied by mathematicians for fun but is of no practical use. Why should you care about random matrices as a data scientist?

As a data scientist, you have already been working with matrices. You know that they are useful. You know that a matrix is made up of matrix elements and that those elements are often formed from data. By now, you’re also most likely used to the idea that data has a random component, so a matrix formed from data must also have random matrix elements. This is what a random matrix is. A random matrix is just a matrix whose elements are drawn from a distribution. Random Matrix Theory (RMT) is the study of the properties of random matrices.

Usually, in RMT, the matrix elements are taken to be independent and identically distributed random variables (iid), but recent research in the RMT field has extended this to looking at more...

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