Foundations of Data Science with Python

Author:   John M. Shea
Publisher:   Taylor & Francis Ltd
ISBN:  

9781032346748


Pages:   496
Publication Date:   22 February 2024
Format:   Hardback
Availability:   In Print   Availability explained
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Foundations of Data Science with Python


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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

Full Product Details

Author:   John M. Shea
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Weight:   1.080kg
ISBN:  

9781032346748


ISBN 10:   1032346744
Pages:   496
Publication Date:   22 February 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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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Author Information

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.

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