95 books like Computer Age Statistical Inference, Algorithms, Evidence, and Data Science

In addressing decision makers, an understanding of forces affecting human behavior is essential. The author provides a comprehensive view on the work of Kahneman and Tversky mapping their journey which lead to their highly impactful behavioral economic body of knowledge. The bias they address provides data scientists a widened perspective on their role and how to address organizational challenges. This provides a language to recognise and address bias inherent in human thinking. The book provides a very powerful set of tools for data scientists who want to see behind the numbers and understand forces affecting human behavior.

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.

The previous book is about the most prevalent forms of AI, many of which focus on prediction or classification.

For example, a bank may use an AI system that utilizes data about those applying for a loan to predict whether they're likely to default. The judicial system might use an AI model which takes into account a convicted person's attributes in order to predict whether that person is likely to re-offend. A hospital might observe attributes of cells in order to classify them as cancerous or not.

Judea Pearl, a computer scientist at UCLA, has been in a long-running effort to get those working in AI to focus more on designing systems which could engage in causal reasoning. And in doing so, he's had a major influence on a number of disciplines, including computer science, philosophy, statistics, epidemiology, and the social sciences.

In The Book of Why, Pearl teams up… show more.

Before Agile and Lean had rocked the software development industry, William Deming was busy forging this new world of work.

Out of the Crisis is predominantly a management book, but it’s really the spark that started the lightweight movement in software delivery. A key concept in the book is how to identify the work system's performance, separate from the performance of individuals.

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.”