Introduction:

"Computer Age Statistical Inference, Student Edition: Algorithms, Evidence, and Data Science" by Bradley Efron and Trevor Hastie is a highly informative book that offers a comprehensive guide to statistical inference in the modern era. With its focus on algorithms, evidence, and data science, this book is a valuable resource for students and professionals in the field.

Insightful Exploration:

The authors delve deep into the foundations of statistical inference, providing readers with a thorough understanding of its principles. They highlight the role of algorithms in data analysis, shedding light on the importance of computational techniques in statistical inference.

Advanced Algorithms for Inference:

The book Computer Age Statistical Inference, goes beyond traditional statistical methods and introduces readers to advanced algorithms used in modern statistical inference. By discussing state-of-the-art techniques, the authors equip readers with the tools needed to tackle complex data analysis problems in various domains. Real-world applications are emphasized to showcase the practical relevance of these algorithms.

Data Science and Statistical Inference:

Recognizing the increasing integration of data science and statistical inference, the authors bridge the gap between these two disciplines. They explore how statistical techniques intertwine with machine learning algorithms and data analysis practices, providing readers with a comprehensive perspective on the synergy between data science and statistical inference.

Evidence-Based Approach:

One of the book's key strengths is its emphasis on evidence-based inference. The authors guide readers through the process of quantifying and interpreting evidence, ensuring that statistical decisions are grounded in solid reasoning and supported by rigorous analysis. This approach empowers readers to make informed decisions based on robust evidence.

Accessible and Practical:

The clarity of explanations and the inclusion of real-world examples make this book highly accessible to readers with varying levels of statistical expertise. The authors strike a perfect balance between theory and practice, enabling readers to grasp complex concepts while showcasing their practical applications. The book's user-friendly style makes it a valuable companion for self-study or classroom use.

Feedback from readers

This book received 28 ratings with 3 reviews and 4.5 stars which is a good rating. But there is some positive and negative feedback from the readers. These feedback reviews will help you to know more about this book. Let’s talk about the pros and cons of this book based on the reviews from readers. 

Pros

1. This book is good for data scientists.
2. This book has good content and reading.
3. It covers explanations of all topics along with the philosophy behind these topics. 
4. This book has great selection and sequencing of the subjects.
5. It provides a good set of exercise problems to practise. 

Cons

1. This book looks like a review of fundamental probability rather than a guide. 
2. It does not cover a topic properly, instead it explains the philosophy and the history behind it. 
3. It is only good for machine learning, not for learning statistics.
4. As a guide for machine learning the content of this book is good but there are no sample codes to perform practice. 

Conclusion:

"Computer Age Statistical Inference" is an indispensable resource for anyone interested in statistical inference and data science. With its comprehensive coverage, insightful exploration of algorithms, and evidence-based approach, this book equips readers with the necessary tools to navigate the complexities of modern statistical analysis. Bradley Efron and Trevor Hastie have successfully created a valuable resource that bridges the gap between statistical theory and practical data analysis, making it a must-have for students and professionals in the field.