Foundations of Data Science with Python
Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality.

This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science.

Key Features:

  • Applies a modern, computational approach to working with data
  • Uses real data sets to conduct statistical tests that address a diverse set of contemporary issues
  • Teaches the fundamentals of some of the most important tools in the Python data-science stack
  • Provides a basic, but rigorous, introduction to Probability and its application to Statistics
  • Offers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material
1143933574
Foundations of Data Science with Python
Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality.

This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science.

Key Features:

  • Applies a modern, computational approach to working with data
  • Uses real data sets to conduct statistical tests that address a diverse set of contemporary issues
  • Teaches the fundamentals of some of the most important tools in the Python data-science stack
  • Provides a basic, but rigorous, introduction to Probability and its application to Statistics
  • Offers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material
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Foundations of Data Science with Python

Foundations of Data Science with Python

by John M. Shea
Foundations of Data Science with Python
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Foundations of Data Science with Python

Foundations of Data Science with Python

by John M. Shea

Overview

Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality.

This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science.

Key Features:

  • Applies a modern, computational approach to working with data
  • Uses real data sets to conduct statistical tests that address a diverse set of contemporary issues
  • Teaches the fundamentals of some of the most important tools in the Python data-science stack
  • Provides a basic, but rigorous, introduction to Probability and its application to Statistics
  • Offers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material

Product Details

ISBN-13: 9781032346748
Publisher: CRC Press
Publication date: 02/22/2024
Series: Chapman & Hall/CRC The Python Series
Pages: 496
Product dimensions: 7.00(w) x 10.00(h) x (d)

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 20 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. He has been an editor for IEEE Transactions on Wireless Communications, IEEE Wireless Communications magazine, and IEEE Transactions on Vehicular Technology.

Table of Contents

1. Introduction  2. First Simulations, Visualizations, and Statistical Tests  3. First Visualizations and Statistical Tests with Real Data  4. Introduction to Probability  5. Null Hypothesis Tests  6. Conditional Probability, Dependence, and Independence  7. Introduction to Bayesian Methods  8. Random Variables  9. Expected Value, Parameter Estimation, and Hypothesis Tests on Sample Means  10. Decision Making with Observations from Continuous Distributions  11. Categorical Data, Tests for Dependence, and Goodness of Fit for Discrete Distributions  12. Multidimensional Data: Vector Moments and Linear Regression  13. Working with Dependent Data in Multiple Dimensions

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