Under the aegis of machine learning in our data-driven machine age, computers are programming themselves and learning about - and solving - an extraordinary range of problems, from the mundane to the most daunting. Today it is machine learning programs that enable Amazon and Netflix to predict what users will like, Apple to power Siri's ability to understand voices, and Google to pilot cars.

Today we think statistics are the enemy, numbers used to mislead and confuse us. That’s a mistake, Tim Harford says in The Data Detective. We shouldn’t be suspicious of statistics - we need to understand what they mean and how they can improve our lives: they are, at heart, human behavior seen through the prism of numbers and are often “the only way of grasping much of what is going on around us”.

Artificial intelligence does the seemingly impossible - driving cars, trading stocks, and teaching children. But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs.

In our age of accelerating progress in so many fields, it’s easy to lose sight of the underlying innovation that makes the data analytics revolution possible. These 24 lectures introduce you to the key concepts, methods, and accomplishments of this versatile approach to problem solving. You need no expertise in mathematics to follow this exciting story. Tim Chartier, professor of mathematics and computer science, explains the basic computational techniques used in data analytics, but his focus is on how these ideas are applied and the amazing results they achieve.

James Gleick, the author of the best sellers Chaos and Genius, now brings us a work just as astonishing and masterly: A revelatory chronicle and meditation that shows how information has become the modern era’s defining quality - the blood, the fuel, the vital principle of our world. The story of information begins in a time profoundly unlike our own, when every thought and utterance vanishes as soon as it is born.

In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent - really - are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant methods of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought that led to recent achievements.

His work is cited by the world's best-known thought leaders, from Steve Jobs to Malcolm Gladwell. In this classic best seller - one of the most influential business books of all time - innovation expert Clayton Christensen shows how even the most outstanding companies can do everything right - yet still lose market leadership. Christensen explains why most companies miss out on new waves of innovation.

Most startups fail. But many of those failures are preventable. The Lean Startup is a new approach being adopted across the globe, changing the way companies are built and new products are launched.

Most people believe that the best way to motivate is with rewards like money - the carrot-and-stick approach. That's a mistake, says Daniel H. Pink (author of To Sell Is Human: The Surprising Truth About Motivating Others). In this provocative and persuasive new book, he asserts that the secret to high performance and satisfaction - at work, at school, and at home - is the deeply human need to direct our own lives, to learn and create new things, and to do better by ourselves and our world.

Data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

Following his blockbuster biography of Steve Jobs, The Innovators is Walter Isaacson’s revealing story of the people who created the computer and the Internet. It is destined to be the standard history of the digital revolution and an indispensable guide to how innovation really happens. What were the talents that allowed certain inventors and entrepreneurs to turn their visionary ideas into disruptive realities? What led to their creative leaps? Why did some succeed and others fail?

In industry after industry, data, analytics, and AI-driven processes are transforming the nature of work. While we often still treat AI as the domain of a specific skill, business function, or sector, we have entered a new era in which AI is challenging the very concept of the firm. AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value.

Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence - and even more so if we hope to participate in the future, rather than being simple bystanders.

The pressures on organizations to change will only increase over the next decades. Yet the methods managers have used to strengthen their companies—total quality management, reengineering, right sizing, restructuring, cultural change, and turnarounds—routinely fall short. In Leading Change, Kotter identifies an eight-step process that every company must go through to achieve its goal, and shows where and how people—good people—often derail.

Work with data like a pro using this guide that breaks down how to organize, apply, and most importantly, understand what you are analyzing in order to become a true data ninja.