15 Math Concepts Every Data Scientist Should Know | Understand and learn how to apply the math behind data science algorithms
Language: English
Published by Packt Publishing, 2024
- Softcover
- New
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15 Math Concepts Every Data Scientist Should Know | Understand and learn how to apply the math behind data science algorithms | David Hoyle | Taschenbuch | Englisch | 2024 | Packt Publishing | EAN 9781837634187 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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- Title
- 15 Math Concepts Every Data Scientist Should Know | Understand and learn how to apply the math behind data science algorithms
- Author
- David Hoyle
- Publisher
- Packt Publishing
- Publication year
- 2024
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1837634181
- ISBN 13
- 9781837634187
- Item weight
- 942 grams
- Dimensions
- 235 x 191 x 27 mm
- Seller catalogs
- Bücher
Create more effective and powerful data science solutions by learning when, where, and how to apply key math principles that drive most data science algorithms
Key Features
- Understand key data science algorithms with Python-based examples
- Increase the impact of your data science solutions by learning how to apply existing algorithms
- Take your data science solutions to the next level by learning how to create new algorithms
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
Data science combines the power of data with the rigor of scientific methodology, with mathematics providing the tools and frameworks for analysis, algorithm development, and deriving insights. As machine learning algorithms become increasingly complex, a solid grounding in math is crucial for data scientists. David Hoyle, with over 30 years of experience in statistical and mathematical modeling, brings unparalleled industrial expertise to this book, drawing from his work in building predictive models for the world's largest retailers.
Encompassing 15 crucial concepts, this book covers a spectrum of mathematical techniques to help you understand a vast range of data science algorithms and applications. Starting with essential foundational concepts, such as random variables and probability distributions, you’ll learn why data varies, and explore matrices and linear algebra to transform that data. Building upon this foundation, the book spans general intermediate concepts, such as model complexity and network analysis, as well as advanced concepts such as kernel-based learning and information theory. Each concept is illustrated with Python code snippets demonstrating their practical application to solve problems.
By the end of the book, you’ll have the confidence to apply key mathematical concepts to your data science challenges.
What you will learn
- Master foundational concepts that underpin all data science applications
- Use advanced techniques to elevate your data science proficiency
- Apply data science concepts to solve real-world data science challenges
- Implement the NumPy, SciPy, and scikit-learn concepts in Python
- Build predictive machine learning models with mathematical concepts
- Gain expertise in Bayesian non-parametric methods for advanced probabilistic modeling
- Acquire mathematical skills tailored for time-series and network data types
Who this book is for
This book is for data scientists, machine learning engineers, and data analysts who already use data science tools and libraries but want to learn more about the underlying math. Whether you’re looking to build upon the math you already know, or need insights into when and how to adopt tools and libraries to your data science problem, this book is for you. Organized into essential, general, and selected concepts, this book is for both practitioners just starting out on their data science journey and experienced data scientists.
Table of Contents
- Recap of Mathematical Notation and Terminology
- Random Variables and Probability Distributions
- Matrices and Linear Algebra
- Loss Functions and Optimization
- Probabilistic Modeling
- Time Series and Forecasting
- Hypothesis Testing
- Model Complexity
- Function Decomposition
- Network Analysis
- Dynamical Systems
- Kernel Methods
- Information Theory
- Non-Parametric Bayesian Methods
- Random Matrices
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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