Here are 100 books that Computer Age Statistical Inference, Algorithms, Evidence, and Data Science fans have personally recommended if you like
Computer Age Statistical Inference, Algorithms, Evidence, and Data Science.
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In sixth grade, I got into an argument with my neighbor, Billy. We were in his backyard, looking at the stars through his new telescope. “I see Orion,” said Billy. “What do you see?” “A bunch of stars.” “I aimed it at Orion. See him?” ”I see a bunch of stars.” “Don’t you see his belt? His sword?” Billy got more agitated. “Everybody knows that’s Orion. I can’t believe you can’t see him.” “It’s not actually Orion – it was just a bunch of stars until someone told a story about it and gave it meaning.” That compelled me to write, to construct a meaning for what I experienced, and try to make sense of it.
I loved this book because it opened my mind to new ways of thinking about thinking and how we make decisions. We are not the rational beings we think we are. Michael Lewis has the gift of being able to take complex ideas and make them understandable, informative, and very entertaining.
The book is about psychologists Amos Tversky and Daniel Khaneman and their research into how people make decisions. Their story is riveting, and I couldn’t help but think about how I make decisions and how to frame questions to gain greater insight into that process.
'Michael Lewis could spin gold out of any topic he chose ... his best work ... vivid, original and hard to forget' Tim Harford, Financial Times
'Gripping ... There is war, heroism, genius, love, loss, discovery, enduring loyalty and friendship. It is epic stuff ... Michael Lewis is one of the best non-fiction writers of our time' Irish Times
From Michael Lewis, No.1 bestselling author of The Big Short and Flash Boys, this is the extraordinary story of the two men whose ideas changed the world.
Daniel Kahneman and Amos Tversky met in war-torn 1960s Israel. Both were gifted young…
When people ask me why I became a statistician, and what its attraction is, I simply tell them that, using statistics, I have been on voyages of discovery and travelled to worlds they didn’t know existed. Using data and statistical methods instead of light and optics, I have seen things others could not imagine. Like an explorer of old, I have joined adventures peeling back the mysteries of the world around us. In my books on statistics, data science, data mining, and artificial intelligence, I have tried to convey some of this excitement, and to show the reader how they too can take part in this wonderful modern adventure.
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.
In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years. Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field. An appendix gives a more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications…
I am a financial data scientist. I think it is important that data scientists are highly specialized if they want to be effective in their careers. I run a business called Conlan Scientific out of Charlotte, NC where me and my team of financial data scientists tackle complicated machine learning problems for our clients. Quant trading is a gladiator’s arena of financial data science. Anyone can try it, but few succeed at it. I am sharing my top five list of math books that are essential to success in this field. I hope you enjoy.
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 book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
When people ask me why I became a statistician, and what its attraction is, I simply tell them that, using statistics, I have been on voyages of discovery and travelled to worlds they didn’t know existed. Using data and statistical methods instead of light and optics, I have seen things others could not imagine. Like an explorer of old, I have joined adventures peeling back the mysteries of the world around us. In my books on statistics, data science, data mining, and artificial intelligence, I have tried to convey some of this excitement, and to show the reader how they too can take part in this wonderful modern adventure.
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.
A complete guide to the theory and practical applications of probability theory
An Introduction to Probability Theory and Its Applications uniquely blends a comprehensive overview of probability theory with the real-world application of that theory. Beginning with the background and very nature of probability theory, the book then proceeds through sample spaces, combinatorial analysis, fluctuations in coin tossing and random walks, the combination of events, types of distributions, Markov chains, stochastic processes, and more. The book's comprehensive approach provides a complete view of theory along with enlightening examples along the way.
When people ask me why I became a statistician, and what its attraction is, I simply tell them that, using statistics, I have been on voyages of discovery and travelled to worlds they didn’t know existed. Using data and statistical methods instead of light and optics, I have seen things others could not imagine. Like an explorer of old, I have joined adventures peeling back the mysteries of the world around us. In my books on statistics, data science, data mining, and artificial intelligence, I have tried to convey some of this excitement, and to show the reader how they too can take part in this wonderful modern adventure.
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.
I am an academic researcher and an avid non-fiction reader. There are many popular books on science or music, but it’s much harder to find texts that manage to occupy the space between popular and professional writing. I’ve always been looking for this kind of book, whether on physics, music, AI, or math – even when I knew that as a non-pro, I wouldn’t be able to understand everything. In my new book I’ve been trying to accomplish something similar: A book that can intrigue readers who are not professional economic theorists, that they will find interesting even if they can’t follow everything.
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.
'Wonderful ... illuminating and fun to read' - Daniel Kahneman, winner of the Nobel Prize and author of Thinking, Fast and Slow
'"Pearl's accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence and have redefined the term "thinking machine"' - Vint Cerf, Chief Internet Evangelist, Google, Inc.
The influential book in how causality revolutionized science and the world, by the pioneer of artificial intelligence
'Correlation does not imply causation.' This mantra was invoked by scientists for decades in order to avoid taking positions as to whether one thing caused another, such as smoking…
I'm a programmer and technical author at Octopus Deploy and I'm deeply interested in DevOps. Since the 1950s, people have been studying software delivery in search of better ways of working. We’ve seen many revolutions since Lincoln Labs first introduced us to phased delivery, with lightweight methods transforming how we wrote software at the turn of the century. My interest in DevOps goes beyond my enthusiasm for methods in general, because we now have a great body of research that adds to our empirical observations on the ways we work.
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.
Essential reading for managers and leaders, this is the classic work on management, problem solving, quality control, and more—based on the famous theory, 14 Points for Management
In his classic Out of the Crisis, W. Edwards Deming describes the foundations for a completely new and transformational way to lead and manage people, processes, and resources. Translated into twelve languages and continuously in print since its original publication, it has proved highly influential. Research shows that Deming’s approach has high levels of success and sustainability. Readers today will find Deming’s insights relevant, significant, and effective in business thinking and practice. This…
I was trained as a mathematician but have always been motivated by problem-solving challenges. Statistics and analytics combine mathematical models with statistical thinking. My career has always focused on this combination and, as a statistician, you can apply it in a wide range of domains. The advent of big data and machine learning algorithms has opened up new opportunities for applied statisticians. This perspective complements computer science views on how to address data science. The Real Work of Data Science, covers 18 areas (18 chapters) that need to be pushed forward in order to turning data into information, better decisions, and stronger organizations
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.
Provides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools. Technology, however, fails to deliver insights with added value if the quality of the information it generates is not assured. Information Quality (InfoQ) is a tool developed by the authors to assess the potential of a dataset to achieve a goal of interest, using data analysis. Whether the information quality of a dataset is sufficient is of practical importance…
I’m an applied statistician and academic researcher/lecturer at New Zealand’s oldest university – the University of Otago. R facilitates everything I do – research, academic publication, and teaching. It’s the latter part of my job that motivated my own book on R. From first-year statistics students who have never seen R to my own Ph.D. students using R to implement novel and highly complex statistical methods and models, my experience is that all ultimately love the ease with which the R language permits exploration, visualisation, analysis, and inference of one’s data. The ever-growing need in today’s society for skilled statisticians and data scientists means there's never been a better time to learn this essential language.
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.
Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: * Features full colour text and extensive graphics throughout. * Introduces a clear structure with numbered section headings to help readers locate information more efficiently. * Looks at the evolution of R over the…
As a professional statistician, I am naturally interested in AI and data science. However, in our current information age, everyone, in all segments of society, needs to understand the basics of AI and data science. These basics include such things as what these disciplines are, what they can contribute to society, and perhaps most importantly, what can go wrong. However, I have found that much of the literature on these topics is highly technical and beyond the reach of most readers. These books are specifically selected because they are readable by virtually everyone, and yet convey the key concepts needed to be data-literate in the 21st century. Enjoy!
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.”
A fascinating look at the trailblazing companies using artificial intelligence to create new competitive advantage, from the author of the business classic, Competing on Analytics, and the head of Deloitte's US AI practice.
Though most organizations are placing modest bets on artificial intelligence, there is a world-class group of companies that are going all-in on the technology and radically transforming their products, processes, strategies, customer relationships, and cultures.
Though these organizations represent less than 1 percent of large companies, they are all high performers in their industries. They have better business…
