Linear Algebra for Data Science with Python

John M. Shea

ISBN 10: 1032659165 / ISBN 13: 9781032659169

Published by Taylor and Francis Ltd, GB, 2025

About this Item

Linear Algebra for Data Science with Python provides an introduction to vectors and matrices within the context of data science. This book starts from the fundamentals of vectors and how vectors are used to model data, builds up to matrices and their operations, and then considers applications of matrices and vectors to data fitting, transforming time-series data into the frequency domain, and dimensionality reduction. This book uses a computational-first approach: the reader will learn how to use Python and the associated data-science libraries to work with and visualize vectors and matrices and their operations, as well as to import data to apply these techniques. Readers learn the basics of performing vector and matrix operations by hand but are also shown how to use several different Python libraries for performing these operations.Key Features:Teaches the most important concepts and techniques for working with multi-dimensional data using vectors and matricesIntroduces readers to some of the most important Python libraries for working with data, including NumPy and PyTorchDemonstrate the application of linear algebra in real data and engineering applicationsIncludes many color visualizations to illustrate mathematical operations involving vectors and matricesProvides practice and feedback through a unique set of online, interactive tools on the accompanying website.

Seller Inventory # LU-9781032659169

Bibliographic Details

• Title: Linear Algebra for Data Science with Python
• Publisher: Taylor and Francis Ltd, GB
• Publication Date: 2025
• Language: English
• ISBN 10: 1032659165
• ISBN 13: 9781032659169
• Binding: Hardback
• Condition: New
• Weight: 640 grams

About this title

Synopsis

Linear Algebra for Data Science with Python provides an introduction to vectors and matrices within the context of data science. This book starts from the fundamentals of vectors and how vectors are used to model data, builds up to matrices and their operations, and then considers applications of matrices and vectors to data fitting, transforming time-series data into the frequency domain, and dimensionality reduction. This book uses a computational-first approach: the reader will learn how to use Python and the associated data-science libraries to work with and visualize vectors and matrices and their operations, as well as to import data to apply these techniques. Readers learn the basics of performing vector and matrix operations by hand but are also shown how to use several different Python libraries for performing these operations.

Key Features:

• Teaches the most important concepts and techniques for working with multi-dimensional data using vectors and matrices
• Introduces readers to some of the most important Python libraries for working with data, including NumPy and PyTorch
• Demonstrate the application of linear algebra in real data and engineering applications
• Includes many color visualizations to illustrate mathematical operations involving vectors and matrices
• Provides practice and feedback through a unique set of online, interactive tools on the accompanying website

About the Author

John M. Shea, PhD is a Professor in the Department of Electrical and Computer Engineering at the University of Florida, where he has taught classes on stochastic methods, data science, and wireless communications for over 25 years. He earned his PhD in Electrical Engineering from Clemson University in 1998 and later received the Outstanding Young Alumni award from the Clemson College of Engineering and Science. Dr. Shea was co-leader of Team GatorWings, which won the Defense Advanced Research Project Agency’s (DARPA’s) Spectrum Collaboration Challenge (DARPA's fifth Grand Challenge) in 2019; he received the Lifetime Achievement Award for Technical Achievement from the IEEE Military Communications Conference (MILCOM) and is a two-time winner of the Ellersick Award from the IEEE Communications Society for the Best Paper in the Unclassified Program of MILCOM.