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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 2: Intermediate Concepts

In this part, we will introduce you to more math concepts that you are very likely to encounter the longer you work in data science. In contrast to Part 1, each chapter is focused on a standalone data science task, modeling technique, or type of data. By the end of Part 2, you will have gained a solid understanding of time series data, how to run a hypothesis test, model complexity, how to build up a function from a set of simpler parts, and network data.

This section contains the following chapters:

  • Chapter 6, Time Series and Forecasting
  • Chapter 7, Hypothesis Testing
  • Chapter 8, Model Complexity
  • Chapter 9, Function Decomposition
  • Chapter 10, Network Analysis
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