Introduction:

In the realm of statistics and data science, "Computer Age Statistical Inference: Algorithms, Evidence, and Data Science" stands out as a comprehensive and invaluable resource. Written by renowned authors Bradley Efron and Trevor Hastie, this book offers a fresh perspective on statistical inference in the context of the modern technological landscape. Whether you're a student, researcher, or practitioner, this book equips you with the necessary tools and techniques to navigate the ever-evolving world of data analysis.

Engaging and Accessible Content:

The authors have done an exceptional job of presenting complex statistical concepts in an engaging and accessible manner. The book strikes a perfect balance between theory and practical application, making it suitable for both beginners and seasoned statisticians. The explanations are clear, concise, and accompanied by illustrative examples, allowing readers to grasp the concepts with ease.

Emphasis on Algorithms and Data Science:

One of the key strengths of this book is its focus on algorithms and data science. It goes beyond traditional statistical methods and explores modern computational approaches to statistical inference. By delving into the realm of data science, Efron and Hastie provide readers with a broader understanding of how statistics and data analysis intersect in the computer age.

Cutting-Edge Research and Techniques:

"Computer Age Statistical Inference" incorporates the latest research and advancements in the field, ensuring that readers stay up to date with the ever-evolving landscape of statistical analysis. The book covers a wide range of topics, including resampling methods, high-dimensional inference, and the application of statistical techniques to large datasets. This breadth of coverage allows readers to tackle real-world problems and make informed decisions in their own research or professional endeavors.

Practical Examples and Case Studies:

To reinforce the concepts discussed, the authors include numerous practical examples and case studies throughout the book. These examples bridge the gap between theory and application, enabling readers to see how statistical inference is implemented in various real-world scenarios. Such practical illustrations enhance the learning experience and make the content more relatable and applicable.

Price:

Hardcover: $58.89

eTextBook: $29.62

Feedback and Review:

Computer Age Statistical Inference: Algorithms, Evidence, and Data Science has garnered a well-deserved rating of 4.8 stars, based on 80 ratings and 26 reviews. The positive reception is a testament to the book's quality and usefulness in the field of data science. Readers appreciate Bradley Efron and Trevor Hastie’s ability to simplify complex topics without sacrificing the depth of the material. The book's clear organization and engaging writing style contribute to an enjoyable learning experience. There are some pros and cons of this book which are analyzed based on the feedback from readers. These pros and cons will help you to know more about this book. For example a reader of this book says,

“Very insightful and informative statistical inference book. I own and have read the authors' other books and as always, this new book is fantastic.

It's very up-to-date and a great reference book for both intro-level students and statistics professionals.

The explanations of concepts are vivid and easy to understand, and quite often it makes you think from a different angle. Love the writing style!

It's an academic book, but quite accessible, insightful and pleasant to read.”

Pros

1. This book is well-written and organized.
2. The content of this book is very informative. 
3. This book includes the essential topics about contemporary statistical inferences. 
4. This book is suitable for those who have an intermediate statistical background. 
5. The way of explanation is like a novel which makes it easy to understand. 

Cons

1. It is more the printed record of an oral lecture than a book which is optimized for reading.
2. This magnificent book contains almost everything in statistics up to 2010 except for classic multivariate analysis and some topics in nonparametrics.
3. Some chapters are just the copy from “elements of statistical learning”.

Conclusion:

"Computer Age Statistical Inference: Algorithms, Evidence, and Data Science" is an exceptional resource for anyone seeking a comprehensive guide to statistical inference in the modern era. With its engaging writing style, emphasis on algorithms and data science, incorporation of cutting-edge research, and practical examples, this book deserves its reputation as a must-have reference for statisticians, data scientists, and researchers alike.