Essential Math for Data Science
Language: English
Published by O'Reilly Media, US, 2022
- Softcover
- New
Seller: Rarewaves USA, HEBRON, KY, U.S.A.Rarewaves USA
AbeBooks seller since June 10, 2025
Condition: New
US$ 48.62
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To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:Recognize the nuances and pitfalls of probability mathMaster statistics and hypothesis testing (and avoid common pitfalls)Discover practical applications of probability, statistics, calculus, and machine learningIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and addedPerform calculus derivatives and integrals completely from scratch in PythonApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks.…
Seller Inventory # LU-9781098102937
- Title
- Essential Math for Data Science
- Author
- Thomas Nield
- Publisher
- O'Reilly Media, US
- Publication year
- 2022
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1098102932
- ISBN 13
- 9781098102937
Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career.
Learn how to:
- Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning
- Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon
- Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance
- Manipulate vectors and matrices and perform matrix decomposition
- Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks
- Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market
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About the Author
He is also the founder and inventor of Yawman Flight, a company developing universal handheld flight controls for flight simulation and unmanned aerial vehicles.
"About the title" may belong to another edition of this title.
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