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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

Part 3: Selected Advanced Concepts

In this part, we will introduce a selection of advanced math concepts. As with Part 2, each concept is a standalone topic. But, in contrast to Part 2, we’re now introducing topics at the cutting edge of data science and data science research. There is still a high probability you will encounter these concepts in your data science work, especially the longer you work in data science. Because of the advanced nature of the topics, each chapter is only designed to give you a basic grounding in that topic. But by the end of Part 3, you will understand the core ideas of each of these topics and be able to use that understanding to guide your own studies.

This section contains the following chapters:

  • Chapter 11, Dynamical Systems
  • Chapter 12, Kernel Methods
  • Chapter 13, Information Theory
  • Chapter 14, Bayesian Non-Parametric Methods
  • Chapter 15, Random Matrices
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