Love Computer Age Statistical Inference, Algorithms, Evidence, and Data Science? Readers share 100 books like Computer Age Statistical Inference, Algorithms, Evidence, and Data Science...

Any book by Michael Lewis is fun and educational, but this one I couldn’t put down. In 2002, for the first time, the Nobel prize for economics did not go to an economist but to a psychologist—Daniel Kahneman—who had single-handedly (with his genius collaborator Amos Tversky) disrupted the economics profession and its core theories—much like Einstein had transformed our understanding of reality and Freud of ourselves—and created an entirely new field called behavioral economics.

This is the story of a remarkable partnership of two eminent scientists who brought about this revolution. Tversky and Kahneman had such a close relationship that even their wives became jealous. This book might make you laugh and cry. And you might learn much about cutting-edge economics and our chronic biases.

This is a deep and beautifully elegant overview of the ideas underlying statistical inference. It is the finest concise outline I know of the foundations, dealing with the key concepts and ideas in an accessible way. Written by one of the leading creators of modern statistics, without unnecessary mathematics or superfluous detail it includes a balanced description of the fundamentals of distinct schools of thought, such as Bayesian and frequentist schools. The book did not exist when I started learning statistics, but I am certain I would have understood the discipline’s subtleties much sooner if it had.

This book might as well be called Introduction to machine learning, and it is probably one of the only books truly deserving of the title. Did you know neural networks have been used for decades to scan checks at the bank? They are called Boltzman Machine. Have you ever heard of how decision trees were used in old-school data mining? You could only get them from proprietary software packages from the early 2000s.

In quant trading, you will constantly face compute power constraints, so it is invaluable to understand the mathematical foundations of the most old-school machine learning methods out there. Researchers 20 years ago used to do a lot of impressive work with a lot less computing power.

This is my go-to book for when I need to find proofs or examples of the theory or applications of probability. It’s an old book now, but it remains unsurpassed as an outline of the foundations of classical probability theory. The preface to the second edition says “in addition to an unexpected number of users, the book seems to have found friends who read it merely for fun; it is most heartening that they range from pure mathematicians to pure amateurs”. And that must surely be exactly right: I find myself re-reading it because of the insights and perspectives it sheds. 

This is a wonderful book because it says it all. Of course, that’s an exaggeration because no book could possibly encompass the vast breadth of modern statistics, but anyone who read through this multi-volume work would have an enviable knowledge of the discipline. It’s an unsurpassed general source of information about the foundational concepts and tools of statistics, and a reference source I regularly turn to when I need to remind myself of the theory underlying a concept or method.

In the ongoing debates over artificial general intelligence (AGI), Judea Pearl is taking a firm stand: He argues that an intelligent robot should be able to reason about causality and that the currently fashionable approaches to AI miss this aspect.

A celebrated AI researcher and a Turing Prize laureate, Pearl has developed an amazingly original approach to this problem. This book is a high-end popular exposition of his approach.

But it’s so much more than that. It’s a history of statistics and its conflicted attitude to causality. It’s a story of heroes (or villains?) in this history. And it’s a scientific autobiography that describes Pearl’s journey. Pearl likes picking fights with the AI community, statisticians, or economists. He’s boastful, provocative, extremely intelligent, and knows how to tell a story.

Deming showed me how to think about organizational performance improvement. I was moving from a clinical psychologist in private practice to an organizational psychologist helping companies develop change strategies. I had studied and loved statistical variation in the context of scientific research, but not in the context of addressing real-world challenges. 

But Deming does something very surprising. He starts by understanding variation and then moves on to understanding organizational culture. Not the theoretical frameworks we all know, but the work world from the view of the front-line employee. Deming’s insight is that the central challenge of culture change is understanding the view of people closest to the work, the ones who perform operations.

They are not motivated by slogans and lofty ideas but by producing great products and services. Taking pride in the work they are doing is central to performance; lost by management fads and enhanced by doing it… show more.

A lightly technical introduction to a comprehensive framework defining and evaluating the quality of information generated by statistical analysis. It expands the role of analytics by including dimensions that affect information quality such as data resolution, data integration, operationalization, and generalizability of findings. This wide-angle perspective provides a practical checklist that has been found useful in applications. Multiple case studies enable the reader to connect to his favorite topic, but also learn from other areas.

An authoritative tome on R. This book is the ultimate reference guide, heavy on statistical methods from the simple to the advanced. Of the 29 chapters, only the first five chapters or so have R syntactical and programming skills as their main focus; the remaining content highlights the many and varied statistical techniques R is capable of. I think this is a fantastic book to have on the shelf for people who are likely to need R and its contributed packages for a variety of different statistical analyses, but might not know where to initially start for any given statistical method.

Books on AI often go to extremes, either promoting it as the solution to all the world’s problems, or depicting it as an evil that will destroy humanity.

This book is much more practical, and based on experience using AI in actual business applications. It is the result of considerable research, involving investigation of applications not only in silicon-valley, but from various business sectors, such as Airbus, Ping, Progressive Insurance, and Capital One Bank.

Don’t let the title fool you; this book is not simply a promotion of AI, but addresses the practical issues that have to be considered if success is to be achieved. For example, they argue that “the most important aspect in AI success is not machinery, but human leadership, behavior, and change.”