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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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Toc

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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

Symbols

Σ notation 11-13

Π notation 13

A

absolute loss 139

adaptive gradient descent algorithms 161

adjacency matrix 330

for directed graph 332

for weighted directed graphs 333

properties 331

Akaike Information Criterion (AIC) 287, 288

uses 289

weaknesses 289

alternative hypothesis 243

Amazon Web Services (AWS) 231

American Statistical Association (ASA) 245

Argand plane 6

ARIMA model, best practice 224

ACF plots 225, 226

auto.arima tool 226

PACF plots 225, 226

ARIMAX 222

AR(p) model 217-220

as infinite impulse response filters 217, 218

Augmented-Dickey-Fuller (ADF) test 224

auto.arima tool 227

code example 227-229

auto-correlation 204

for modeling time series data 205

auto-correlation function ...

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