Book description
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here?…
Why read it?
3 authors picked Computer Age Statistical Inference, Algorithms, Evidence, and Data Science as one of their favorite books. Why do they recommend it?
This is a book for (aspiring and established) data scientists, and it has been the best thematic book I held in hands for years! Huge congrats to the authors for putting the material together in such an agreeable and intriguing-to-read form!
The text covers classic statistical inference, early computer-age methods, and twenty-century topics. This puts a unique perspective on current analytic technologies labeled machine learning, artificial intelligence, and statical learning. The examples used provide a powerful description of the methods covered and the compare and contrast sections highlight the evolution of analytics. This book by Efron and Hastie is a natural follow-up source for readers interested in more details.
From Ron's list on how numbers turn into information.
If you want to find out how to make discoveries using modern data science tools, this is the book to read. My career has been based on developing and applying statistical tools. The infrastructure underlying these tools is the computer – the computer and I grew up in parallel. And it is no exaggeration to say that the computer has revolutionised the practice of statistical analysis, replacing the tedium of manual arithmetic with powerful instruments for probing and examining data sets.
On the one hand, computers allow us to store and manipulate vast data sets, while on the other hand,…
From David's list on statistics from a statistician.
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