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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

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781837634187

Length 510 pages

Edition 1st Edition

Languages

Python

Tools

NumPy

Concepts

Data Science

Author (1):

David Hoyle

View More author details

Table of Contents (21) Chapters

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

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Authors (1)

David Hoyle

David Hoyle

David Hoyle has over 30 years' experience in machine learning, statistics, and mathematical modeling. He gained a BSc. degree in mathematics and physics and a Ph.D. in theoretical physics from the University of Bristol. He did research at the University of Cambridge and led his own research groups as an Associate Professor at the University of Exeter and the University of Manchester. Previously, he worked for Lloyds Banking Group – one of the UK's largest retail banks, and as joint Head of Data Science for AutoTrader UK. He now works for the global customer data science company dunnhumby, building statistical and machine learning models for the world's largest retailers, including Tesco UK and Walmart. He lives and works in Manchester, UK.

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