Название: Foundations of Data Science with PythonАвтор: John M. Shea
Издательство: CRC Press
Серия: The Python Series
Год: 2024
Страниц: 503
Язык: английский
Формат: pdf (true)
Размер: 37.9 MB
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.
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
Who is this book for?
This book is targeted toward engineers and scientists, whether working or still in school. Given this target audience, I assume that the reader has a basic working knowledge of:
• computer programming (knowing Python is helpful, but not required), and
• one-dimensional differential and integral calculus.
This book is written by an engineer with degrees in both electrical and computer engineering. This book and its companion, Introduction to Linear Algebra for Data Science with Python, were written to provide the main textbooks for a 4-credit, semester-long course for engineers, taught in the Department of Electrical and Computer Engineering at the University of Florida. These books are intended to be a broad introduction to Data Science.
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