Python Tools for Data Scientists Pocket Primer (eBook, ePUB)
A Quick Guide to Essential Python Libraries for Data Science
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This book, part of the best-selling Pocket Primer series, offers a comprehensive introduction to essential Python tools for data scientists. It begins with an overview of Python basics, followed by in-depth coverage of NumPy and Pandas, focusing on their features and applications. The text also addresses the critical tasks of writing regular expressions and performing data cleaning.Further sections delve into data visualization techniques and the use of Sklearn and SciPy, providing practical knowledge and skills for handling complex data analysis tasks. This structured approach ensures that re...
This book, part of the best-selling Pocket Primer series, offers a comprehensive introduction to essential Python tools for data scientists. It begins with an overview of Python basics, followed by in-depth coverage of NumPy and Pandas, focusing on their features and applications. The text also addresses the critical tasks of writing regular expressions and performing data cleaning.
Further sections delve into data visualization techniques and the use of Sklearn and SciPy, providing practical knowledge and skills for handling complex data analysis tasks. This structured approach ensures that readers gain a complete understanding of the tools and techniques necessary for effective data science.
Designed to be accessible yet thorough, this book includes numerous code samples to reinforce learning. Companion files with source code are available for download, making it an invaluable resource for anyone looking to master Python for data science and enhance their data analysis capabilities.
Further sections delve into data visualization techniques and the use of Sklearn and SciPy, providing practical knowledge and skills for handling complex data analysis tasks. This structured approach ensures that readers gain a complete understanding of the tools and techniques necessary for effective data science.
Designed to be accessible yet thorough, this book includes numerous code samples to reinforce learning. Companion files with source code are available for download, making it an invaluable resource for anyone looking to master Python for data science and enhance their data analysis capabilities.
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