What are the best books about data science?

There are many excellent books on data science that cater to a range of expertise levels, from beginners to advanced practitioners. Here are some highly recommended books covering various aspects of data science:

1. **"Data Science for Business" by Foster Provost and Tom Fawcett**: This book provides insights into how data science can be used to inform and improve business decisions. It's great for those looking to understand the practical applications of data science in a business context.

2. **"Python Data Science Handbook" by Jake VanderPlas**: A comprehensive guide for those who want to learn how to use Python for data science. It covers essential libraries like NumPy, Pandas, Matplotlib, Scikit-Learn, and more.

3. **"The Data Science Handbook" by Field Cady**: This handbook is a compilation of in-depth interviews with 25 remarkable data scientists, where they share their insights, stories, and advice. It's great for understanding the breadth of the field and career paths.

4. **"Practical Statistics for Data Scientists" by Peter Bruce and Andrew Bruce**: This book offers a practical introduction to statistical methods essential in data science, without the need for an extensive background in mathematics.

5. **"Pattern Recognition and Machine Learning" by Christopher M. Bishop**: Suitable for advanced readers, this book covers pattern recognition and machine learning, providing a comprehensive introduction to the fields as they relate to data science.

6. **"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville**: This book is an authoritative text on deep learning, offering a deep dive into the methods and theories of deep learning. It's aimed at students and professionals with an intermediate to advanced understanding of machine learning.

7. **"Storytelling with Data: A Data Visualization Guide for Business Professionals" by Cole Nussbaumer Knaflic**: This book teaches the fundamentals of data visualization and how to communicate effectively with data. It's particularly useful for presenting data insights in a business setting.

8. **"R for Data Science" by Hadley Wickham and Garrett Grolemund**: This book introduces you to R, a language and environment for statistical computing and graphics. It's centered around the tidyverse set of packages, making data science with R accessible to beginners.

9. **"Big Data: A Revolution That Will Transform How We Live, Work, and Think" by Viktor Mayer-Schönberger and Kenneth Cukier**: This book explores the impact of big data on society and the changes it brings to technology, business, and governance.

10. **"The Hundred-Page Machine Learning Book" by Andriy Burkov**: A concise guide that covers the core concepts of machine learning. It's designed for readers who want to get up to speed quickly.

These books cover a wide range of topics within data science, from the technical aspects of machine learning and statistics to the broader implications of big data and the practical applications of data science in business.

There are many excellent books on data science that cater to a range of expertise levels, from beginners to advanced practitioners. Here are some highly recommended books covering various aspects of data science:

1. **"Data Science for Business" by Foster Provost and Tom Fawcett**: This book provides insights into how data science can be used to inform and improve business decisions. It's great for those looking to understand the practical applications of data science in a business context.

2. **"Python Data Science Handbook" by Jake VanderPlas**: A comprehensive guide for those who want to learn how to use Python for data science. It covers essential libraries like NumPy, Pandas, Matplotlib, Scikit-Learn, and more.

3. **"The Data Science Handbook" by Field Cady**: This handbook is a compilation of in-depth interviews with 25 remarkable data scientists, where they share their insights, stories, and advice. It's great for understanding the breadth of the field and career paths.

4. **"Practical Statistics for Data Scientists" by Peter Bruce and Andrew Bruce**: This book offers a practical introduction to statistical methods essential in data science, without the need for an extensive background in mathematics.

5. **"Pattern Recognition and Machine Learning" by Christopher M. Bishop**: Suitable for advanced readers, this book covers pattern recognition and machine learning, providing a comprehensive introduction to the fields as they relate to data science.

6. **"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville**: This book is an authoritative text on deep learning, offering a deep dive into the methods and theories of deep learning. It's aimed at students and professionals with an intermediate to advanced understanding of machine learning.

7. **"Storytelling with Data: A Data Visualization Guide for Business Professionals" by Cole Nussbaumer Knaflic**: This book teaches the fundamentals of data visualization and how to communicate effectively with data. It's particularly useful for presenting data insights in a business setting.

8. **"R for Data Science" by Hadley Wickham and Garrett Grolemund**: This book introduces you to R, a language and environment for statistical computing and graphics. It's centered around the tidyverse set of packages, making data science with R accessible to beginners.

9. **"Big Data: A Revolution That Will Transform How We Live, Work, and Think" by Viktor Mayer-Schönberger and Kenneth Cukier**: This book explores the impact of big data on society and the changes it brings to technology, business, and governance.

10. **"The Hundred-Page Machine Learning Book" by Andriy Burkov**: A concise guide that covers the core concepts of machine learning. It's designed for readers who want to get up to speed quickly.

These books cover a wide range of topics within data science, from the technical aspects of machine learning and statistics to the broader implications of big data and the practical applications of data science in business.