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  • Foundations of Data Science with Python (Chapman & Hall/CRC The Python Series)

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Foundations of Data Science with Python (Chapman & Hall/CRC The Python Series) 1st Edition

by John M. Shea (Author)
5.0 out of 5 stars (6)
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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
Read more
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  1. ISBN-10
    1032350423
  2. ISBN-13
    978-1032350424
  3. Edition
    1st
  4. Publisher
    Chapman and Hall/CRC
  5. Publication date
    February 22, 2024
  6. Language
    English
  7. Dimensions
    7.01 x 1.14 x 10 inches
  8. Print length
    464 pages
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Editorial Reviews

Review

"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

From the Author

What people tell me they love about this book
Foundations of Data Science with Python uses a rich mix of text, visualizations, Python code, and mathematics. The book is printed in full color to show off the visualizations, and color is used to highlight components like definitions, examples, and warnings. The accompanying website includes animations, interactive quizzes that provide intelligent feedback based on the user's responses, interactive animated flashcards for reviewing terminology, and additional Python code and discussion for more-advanced topics. The website also includes lists of key points from the chapters, as well as a section that provides books and web sites for readers who want to continue their study of data science.

About the cover art
The beautiful painting featured on the cover was painted by my mother, Agnes Shea, who died of breast cancer in 2015. I am thankful that the publisher allowed me to honor her and show off her beautiful artwork in this way.

Origin of this book
In 2012, my department (Electrical and Computer Engineering) decided to replace an Engineering Statistics course that was taught in another department with a course that would better address our students' needs - in particular, to cover both statistics and probability in a way that would prepare our students for either the workplace or grad school. I chose to develop a course that starts with a computational approach (using simulations to conduct statistical tests) while introducing students to the popular Python data stack. At the same time, I wanted the course to provide a solid theoretical background on probability, random variables, and decision theory.

In 2020, I was asked to add computational linear algebra to the course to address another departmental need, and I changed the focus of the course to a broad introduction to data science. Although linear algebra could not be included in this book, I have refined the material on statistics and probability through teaching the course many times over the past 10+ years.

When COVID-19 disrupted my sabbatical, I used the time to write the first draft of this book and the Python/Javascript libraries needed to provide the interactive experiences on the book's website. After almost three years of writing and revising, I beileve this book is a good text for an Intro to Data Science course, and especially one to replace Engineering Statistics. It is also good book for anyone who knows some programming and calculus and who wants a thorough background in areas of probability, statistics, and dimensionality reduction that are relevant to data science and machine learning.
 
FAQ: Do I need to know Python or Jupyter to learn data science from this book?
No! This book explains how to get started in Jupyter, and an introduction to Python is provided on the book's website. However, you do need to know some programming language before using this book. This book minimizes its use of Python-specific language features to make it easy to understand for new Python learners.

Product details

  • Publisher ‏ : ‎ Chapman and Hall/CRC
  • Publication date ‏ : ‎ February 22, 2024
  • Edition ‏ : ‎ 1st
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 464 pages
  • ISBN-10 ‏ : ‎ 1032350423
  • ISBN-13 ‏ : ‎ 978-1032350424
  • Item Weight ‏ : ‎ 2.25 pounds
  • Dimensions ‏ : ‎ 7.01 x 1.14 x 10 inches
  • Best Sellers Rank: #3,138,360 in Books (See Top 100 in Books)
  • Customer Reviews:
    5.0 out of 5 stars (6)

About the author

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John M. Shea

John M. Shea

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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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Customer reviews

5 out of 5 stars
6 global ratings

Top reviews from the United States

  • J. Feng
    5.0 out of 5 stars Distilled from Years of Experience
    Reviewed in the United States on January 5, 2025
    Format: KindleVerified Purchase
    Having taken multiple courses with the author since 2012, I was immediately drawn to his book. This book truly feels distilled from his years of teaching experience. Many of the probability and statistics topics, along with the real-world problems, instantly transported me back to his engaging lectures.

    It's evident that this book is a refined product of the author's pedagogical approach. He masterfully connects fundamental concepts to modern data science practices using Python. For me, it's proven to be an incredibly handy resource for refreshing the statistical knowledge I need in my current work.
    One person found this helpful
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  • Miguel
    5.0 out of 5 stars Pre-released version review
    Reviewed in the United States on May 2, 2024
    Format: Paperback
    Grande Dr. Shea
    One person found this helpful
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  • Amazon Customer
    5.0 out of 5 stars Great book to learn Data Science in Python!!
    Reviewed in the United States on February 29, 2024
    Format: Paperback
    Used a pre-released version of this book for a Data Science Class in University, concepts were thoroughly explained with plenty of examples and practical applications. Made the learning process significantly faster and easier than it would have been, recommend heavily!!
    2 people found this helpful
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  • Will
    5.0 out of 5 stars Helped me get an A
    Reviewed in the United States on March 28, 2024
    Format: Paperback
    I used this book while in a data science course taught by the author. The book came in handy with worked out examples and a plethora of situations modeled. My Python skills improved and my knowledge of statistics is deeper than before reading the book.
    3 people found this helpful
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