Essential Math for Data Science: Take Control of Your Data with Fundamental Lin,

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Características del artículo

Estado
Nuevo: Libro nuevo, sin usar y sin leer, que está en perfecto estado; incluye todas las páginas sin ...
Book Title
Essential Math for Data Science: Take Control of Your Data with,
Narrative Type
Computers & Technology
Genre
N/A
Intended Audience
N/A
ISBN
9781098102937
Categoría

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Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1098102932
ISBN-13
9781098102937
eBay Product ID (ePID)
22057253438

Product Key Features

Number of Pages
349 Pages
Language
English
Publication Name
Essential Math for Data Science : Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics
Subject
Algebra / Linear, Probability & Statistics / Regression Analysis, Calculus
Publication Year
2022
Type
Textbook
Author
Thomas Nield
Subject Area
Mathematics
Format
Trade Paperback

Dimensions

Item Height
0.9 in
Item Weight
21.4 Oz
Item Length
9.2 in
Item Width
7 in

Additional Product Features

LCCN
2023-276388
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.310151
Synopsis
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, 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
LC Classification Number
Q325.5

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