"Foundations of Data Science with Python" by John M. Shea is an ideal resource for those looking to learn the fundamentals of data science using the Python programming language. This book offers a comprehensive introduction to the key concepts and tools of data analysis, transforming raw data into actionable insights. John M. Shea thoroughly covers topics such as data collection and cleaning, visualization, statistical analysis, and machine learning.
The book is suitable for both beginners and experienced Python users who want to deepen their understanding and leverage this powerful language for data work. It combines theory and practice, featuring real code examples and hands-on exercises that help reinforce the concepts covered. This guide provides a strong foundation for understanding data science and its applications in business, research, and everyday tasks. Download "Foundations of Data Science with Python" in PDF and make it your essential guide to the world of data science!
Who Should Read This Book?
- Aspiring Data Analysts: This guide helps beginners learn the basics of data science from scratch using Python, making it easy to apply analytical methods to real-world data.
- Programmers Transitioning to Data Science: It serves as an excellent resource for those who want to apply their programming skills to data analysis and machine learning.
- Students and Educators: This book is recommended as a course resource for data analysis and Python programming.
- Professionals from Other Fields: Economists, marketers, scientists, and other specialists who want to learn how to analyze data and use statistical methods will find this guide invaluable.
- Freelancers and Researchers: The book provides practical data-handling skills, making it useful for independent projects and research.
What’s Inside "Foundations of Data Science with Python"?
John M. Shea’s book provides a systematic introduction to data science using Python. It covers the complete data workflow — from collection and cleaning to analysis and visualization. Readers will learn how to use popular Python libraries such as pandas, numpy, and matplotlib to manipulate data and create visualizations that reveal important trends and patterns.
The book delves into the fundamentals of statistics and probability, which are essential for understanding data patterns, and introduces machine learning techniques to make predictions based on historical data.
A strong emphasis is placed on data preparation, a critical step in any data project. Readers will learn how to clean, structure, and transform data for analysis. Visualization techniques are also covered to help present complex information clearly and effectively, making the book useful not just for analysts but for anyone wanting to communicate their findings effectively.
With practical code examples that can be adapted to individual projects, "Foundations of Data Science with Python" is a great entry point into data analysis, helping readers master the essential skills for a successful career in data science.
More About the Author of the Book
FAQ for "Foundations of Data Science with Python"
Which Python libraries are covered in the book?
The book covers pandas, numpy, matplotlib, and other tools for data analysis and visualization, providing efficient ways to work with large datasets.
How is data preparation addressed?
The author explains the entire data preparation process, including cleaning, transforming, and structuring data to make it ready for analysis and visualization.
Is this book suitable for beginner programmers?
Yes, it offers a step-by-step introduction to data analysis, making it accessible to beginners. Key topics are explained in simple terms with code examples.
What statistical concepts are covered in "Foundations of Data Science with Python"?
The book covers fundamental statistical concepts such as descriptive statistics, correlation, probability, and hypothesis testing, all crucial for data analysis.
Are machine learning tasks included?
Yes, the book introduces basic machine learning models to make predictions based on historical data.
Does it include practical exercises?
Yes, readers will find exercises and tasks to help them practice and better understand key concepts.
Information
| Author: | John M. Shea | Language: | English |
| Publisher: | Chapman and Hall/CRC | ISBN-13: | 978-1032350424 |
| Publication Date: | February 22, 2024 | ISBN-10: | 1032350423 |
| Print Length: | 502 pages | Category: | Data Science Books |
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*The book is taken from free sources and is presented for informational purposes only. The contents of the book are the intellectual property of the author and express his views. After reading, we insist on purchasing the official publication on Amazon!
