Название: The Data Science Handbook, 2nd Edition
Автор: Field Cady
Издательство: Wiley
Год: 2025
Страниц: 368
Язык: английский
Формат: True/Retail EPUB, PDF
Размер: 10.1 MB
Practical, accessible guide to becoming a data scientist, updated to include the latest advances in Data Science and related fields. Becoming a data scientist is hard. The job focuses on mathematical tools, but also demands fluency with software engineering, understanding of a business situation, and deep understanding of the data itself. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Data Science is a quickly evolving field, and this 2nd edition has been updated to reflect the latest developments, including the revolution in AI that has come from Large Language Models and the growth of ML Engineering as its own discipline. I have also updated my treatment of Spark to cover its new DataFrame interface, and reduced the emphasis on Hadoop since it is on the decline. Other changes include a reduced emphasis on Bayesian networks (which have waned in popularity with the rise of Deep Learning), a switch from Python 2 to Python 3, and numerous improvements to the prose. The example code in this book is all in Python, except for a few domain‐specific languages such as SQL.
Автор: Field Cady
Издательство: Wiley
Год: 2025
Страниц: 368
Язык: английский
Формат: True/Retail EPUB, PDF
Размер: 10.1 MB
Practical, accessible guide to becoming a data scientist, updated to include the latest advances in Data Science and related fields. Becoming a data scientist is hard. The job focuses on mathematical tools, but also demands fluency with software engineering, understanding of a business situation, and deep understanding of the data itself. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Data Science is a quickly evolving field, and this 2nd edition has been updated to reflect the latest developments, including the revolution in AI that has come from Large Language Models and the growth of ML Engineering as its own discipline. I have also updated my treatment of Spark to cover its new DataFrame interface, and reduced the emphasis on Hadoop since it is on the decline. Other changes include a reduced emphasis on Bayesian networks (which have waned in popularity with the rise of Deep Learning), a switch from Python 2 to Python 3, and numerous improvements to the prose. The example code in this book is all in Python, except for a few domain‐specific languages such as SQL.