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!
I wrote...
Statistical Thinking: Improving Business Performance
Introductory statistics texts are notorious for being arcane, dry, and focusing more on mathematical formulas than practical problems. This text was written by two authors who each have decades of experience applying statistics to solve real problems in business and industry. While it incorporates the underlying theory of statistics in later chapters, it focuses primarily on identification of real problems, and creative determination of how data might be of use in finding solutions. Numerous examples are provided from their applications in finance, engineering, pharmaceuticals, and manufacturing, with most coming from the business arena. This is an introductory text for those interested in learning what modern statistics is all about, without getting lost in the mathematical underpinnings.
This book goes beyond the hype of data science, the details of machine learning methods, and the coding so closely associated with data science. Rather, it emphasizes the real types of problems for which data science may help, and explains the practical issues (“the real work”) that often lead to failure in data science projects.
These issues tend to be overlooked in more technical presentations of data science. They include such critical considerations as defining the right problem to begin with, understanding the “pedigree” (background and quality) of any data used, and ensuring that the right people are involved from the start.
The essential guide for data scientists and for leaders who must get more from their data science teams
The Economist boldly claims that data are now "the world's most valuable resource." But, as Kenett and Redman so richly describe, unlocking that value requires far more than technical excellence. The Real Work of Data Science explores understanding the problems, dealing with quality issues, building trust with decision makers, putting data science teams in the right organizational spots, and helping companies become data-driven. This is the work that spells the difference between a good data scientist and a great one, between a…
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…
O’Neil received a Ph.D. in mathematics from Harvard, but don’t worry, there are no formulas in the book!
After a stint in academia, she went to work for a hedge fund in New York, where they applied statistical models in finance, and made lots of money. However, O’Neil became more and more concerned that their models enabled some people to take advantage of other people.
Digging deeper outside her own company, she found a shocking growth in untested and unverified models being used to guide hiring and firing, determine criminal sentencing and parole decisions, and find gullible people for predatory online ads. This book looks at AI and data science from an ethical point of view, describing how models should and should not be used in society.
'A manual for the 21st-century citizen... accessible, refreshingly critical, relevant and urgent' - Financial Times
'Fascinating and deeply disturbing' - Yuval Noah Harari, Guardian Books of the Year
In this New York Times bestseller, Cathy O'Neil, one of the first champions of algorithmic accountability, sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric.
We live in the age of the algorithm. Increasingly, the decisions that affect our lives - where we go to school, whether we get a loan, how much we pay for insurance - are being made…
This book, by Nate Silver of 538 fame, explains in a straightforward manner why so many predictions by “experts,” from weather forecasts to sports outcomes to election polling to economics, ultimately prove wrong.
It relates to understanding the “signal,” the underlying science that is often revealed through trends and patterns in data, relative to the “noise,” the random or unpredictable variations always present in data. Silver also explains the concept of conditional probability, probability when provided with some relevant information, in an unusually clear manner.
The book reads more like a casual conversation with the author, rather than a statistics textbook.
UPDATED FOR 2020 WITH A NEW PREFACE BY NATE SILVER
"One of the more momentous books of the decade." —The New York Times Book Review
Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of the website FiveThirtyEight.
Drawing on his own groundbreaking work, Silver examines the world of prediction,…
A “Black Swan” is a highly unlikely event that occurs with massive consequences. Think of 9/11 or the astonishing success of Google or Amazon.
The main issue relative to Black Swans, as explained by Talib, is that after the fact people are drawn to concocting detailed explanations that make them seem less random, and more predictable. In other words, people develop causal explanations that are completely wrong, but sound reasonable, and will then use them to predict the future.
In the words of Nate Silver, they invent a “signal” to explain what is in reality “noise.” These explanations also create a false sense of security about our ability to predict future events. In short, we fool ourselves into thinking that we know more than we actually do.
The most influential book of the past seventy-five years: a groundbreaking exploration of everything we know about what we don’t know, now with a new section called “On Robustness and Fragility.”
A black swan is a highly improbable event with three principal characteristics: It is unpredictable; it carries a massive impact; and, after the fact, we concoct an explanation that makes it appear less random, and more predictable, than it was. The astonishing success of Google was a black swan; so was 9/11. For Nassim Nicholas Taleb, black swans underlie almost everything about our world, from the rise of religions…
On the expertise I claim only a deep interest in history, leadership, and social history. After some thirty-six years in the fire and emergency services I can, I think, claim to have seen the best and the worst of human behaviour and condition. History, particularly naval history, has always been one of my interests and the Battle of Jutland is a truly fascinating study in the importance of communication between the leader and every level between him/her and the people performing whatever task is required. In my own career, on a very much smaller scale, this is a lesson every officer learns very quickly.
Captain Heron finds himself embroiled in a conflict that threatens to bring down the world order he is sworn to defend when a secretive Consortium seeks to undermine the World Treaty Organisation and the democracies it represents as he oversees the building and commissioning of a new starship.
When the Consortium employs an assassin from the Pantheon, it becomes personal.
Captain James Heron First Into the Fray: Prequel to Harry Heron Into the Unknown of the Harry Heron Series
The year is 2202, and the recently widowed Captain James Heron is appointed to stand by his next command, the starship NECS Vanguard, while she is being built. He and his team soon discover that they are battling the Consortium, a shadowy corporate group that seeks to steal the specs for the ship’s new super weapon. The Consortium hires the Pantheon, a mysterious espionage agency, to do their dirty work as they lay plans to take down the Fleet and gain supreme power on an intergalactic scale. When Pantheon Agent Bast and her team kidnap Felicity Rowanberg, a Fleet agent…
