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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
2. Chapter 1: Recap of Mathematical Notation and Terminology FREE CHAPTER 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

Who this book is for

This book is for data scientists and machine learning engineers who have been using data science and machine learning techniques, software, and Python packages such as scikit-learn, but without necessarily fully understanding the mathematics behind the algorithms. This could include the following types of people:

Data scientists who have a college/undergraduate degree in a numerate subject and so have a basic understanding of mathematics, but they want to learn more, particularly those bits of mathematics that will be helpful in their roles as data scientists.

Data scientists who have a good understanding of some of the mathematics behind bits of data science but want to discover some new math concepts that will be useful to them in their data science work.

Data scientists who have business or data science problems they need to solve, but existing software does not provide appropriate algorithms. They want to construct their own algorithms but lack the mathematical guidance on how to apply mathematics to the new data science problems.

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