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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

Summary

This chapter has been another chapter on a specific mathematical technique. There have been a lot of formulae, particularly when it comes to learning about the various forms of Fourier decompositions. However, the formulae are not the key point here. Formulae can always be looked up. It is more important to understand and remember the concepts. Of these, the most important concepts we have covered are as follows:

  • Why we decompose a function into a set of simpler building block functions.
  • How a function can be decomposed using a set of basis functions, and how we can use the inner product between functions to calculate the coefficients or amplitudes in a decomposition.
  • How to use an orthonormal basis as a convenient set of component functions to decompose a function, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>f</mml:mi><mml:mfenced separators="|"><mml:mrow><mml:munder underaccent="false"><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mo>_</mml:mo></mml:munder></mml:mrow></mml:mfenced></mml:math>.
  • The eigenfunctions of a linear operator and how they can naturally provide a complete orthonormal basis for decomposing other functions, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>f</mml:mi><mml:mfenced separators="|"><mml:mrow><mml:munder underaccent="false"><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mo>_</mml:mo></mml:munder></mml:mrow></mml:mfenced></mml:math>.
  • That a periodic function can be decomposed into...
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