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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

Notes and further reading

To learn more about the topics that were covered in this chapter, take a look at the following resources:

  1. For a short but very readable discussion on the broader aspects of what information is, I like the book by L. Floridi, Information: A Very Short Introduction, 1st Edition (2010), Oxford University Press, Oxford, UK. ISBN: 978-0199551378.
  2. For additional texts on the mathematical aspects of information theory, see the following:
    1. For a modern readable introduction to the mathematical theory of information, I like the book by J.V. Stone, Information Theory: A Tutorial Introduction, 1st Edition (2015), Sebtel Press. ISBN: 978-0956372857.
    2. Another accessible and well-established account of mathematical information theory is the book by J.R. Pierce, An Introduction to Information Theory: Symbols, Signals and Noise, Revised 2nd Edition (2003), Dover Publications, New York, USA. ISBN: 978-0486240619.
    3. The most authoritative textbook on information theory...
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