Foundations of Data Science with Python

John M. Shea

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Paperback

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English

Chapman & Hall/CRC
22 February 2024

Data analysis: general; Probability & statistics; Environmental science, engineering & technology; Web programming

Series: Chapman & Hall/CRC The Python Series

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

By: John M. Shea
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Dimensions: Height: 254mm, Width: 178mm,
Weight: 1.047kg
ISBN: 9781032350424
ISBN 10: 1032350423
Series: Chapman & Hall/CRC The Python Series
Pages: 464
Publication Date: 22 February 2024
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

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.

Reviews for Foundations of Data Science with Python

""Foundations of Data Science with Python, by John M. Shea, provides a comprehensive and modern introduction of data science. The book illustrates different aspects of working with data computationally using the programming language Pyhon. It incoporates many trendy and practical real data examples, such as COVID-19 datasets, in its discussion, while at the same time retaining mathematical rigor by covering topics on probability and random variables, among others. Overall, the book is beautifully written and offers sufficient coding support for Python beginners to follow along. It is a valuable addition to the shelf for anyone who wishes to gain basic knowledge of data science. In addition, it is an excellent choice as a textbook for a first course in data science."" - Qing Wang, The American Statistician