Essential math for data science : take control of your data with fundamental linear algebra, probability, and statistics / Thomas Nield.

Format:

Book

Author/Creator:

Nield, Thomas (Computer programmer), author.

Language:

English

Subjects (All):

Data mining--Mathematics.

Data mining.

Mathematics.

Machine learning--Mathematics.

Machine learning.

Physical Description:

1 online resource (xiv, 332 pages) : color illustrations

Edition:

First edition.

Place of Publication:

Sebastopol, CA : O'Reilly Media, Inc., 2022.

Summary:

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.

Notes:

Includes index.

OCLC-licensed vendor bibliographic record.

ISBN:

9781098102920

1098102924

OCLC:

1321899316