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Essential Math for Data Science : Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics by Thomas Nield is a mathematics available to read on EtoBox.

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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 math Master statistics and hypothesis testing (and avoid common pitfalls) Discover practical applications of probability, statistics, calculus, and machine learning Intuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added Perform calculus derivatives and integrals completely from scratch in Python Apply what you've learned to machine learning, including linear regression, logistic regression, and neural networks -- Provided by publisher

Who reads Essential Math for Data Science : Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics?

Author

Thomas Nield

Publisher

O'Reilly Media, Incorporated

Published

2022

Language

EN

ISBN

9781098102937

Category

mathematics

Subjects

Mathematics, Science, Computer Science

Rating

4.5 / 5 (176 ratings)

Updated

2026-03-14