But its visionary founder, Jeff Bezos, wasn't content with being a bookseller. He wanted Amazon to become the everything store, offering limitless selection and seductive convenience at disruptively low prices. To do so, he developed a corporate culture of relentless ambition and secrecy that's never been cracked. Until now. Brad Stone enjoyed unprecedented access to current and former Amazon employees and Bezos family members, giving readers the first in-depth, fly-on-the-wall account of life at Amazon. Compared to tech's other elite innovators--Jobs, Gates, Zuckerberg--Bezos is a private man. But he stands out for his restless pursuit of new markets, leading Amazon into risky new ventures like the Kindle and cloud computing, and transforming retail in the same way Henry Ford revolutionized manufacturing.The Everything Store will be the revealing, definitive biography of the company that placed one of the first and largest bets on the Internet and forever changed the way we shop and read.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas 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 color graphics. It should be 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. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

    With this practical book, you'll explore the often-misunderstood practice of user story mapping, and learn how it can help keep your team stay focused on users and their experience throughout the development process.You and your team will learn that user stories aren't a way to write better specifications, but a way to organize and have better conversations. This book will help you understand what kinds of conversations you should be having, when to have them, and what to keep track of when you do. Learn the key concepts used to create a great story map. Understand how user stories really work, and how to make good use of them in agile and lean projects. Examine the nuts and bolts of managing stories through the development cycle. Use strategies that help you continue to learn before and after the product's release to customers and usersUser Story Mapping is ideal for agile and lean software development team members, product managers and UX practitioners in commercial product companies, and business analysts and project managers in IT organizations—whether you're new to this approach or want to understand more about it.

    He shows how leading enterprises such as Enel, 3M, Royal Dutch Shell, the U.S. Department of Defense, and others are applying AI and IoT with stunning results. Digital Transformation is the guidebook every business and government leader needs to survive and thrive in the new digital age.

    The consensus is clear: MBA programs are a waste of time and money. Even the elite schools offer outdated assembly-line educations about profit-and-loss statements and PowerPoint presentations. After two years poring over sanitized case studies, students are shuffled off into middle management to find out how business really works.Josh Kaufman has made a business out of distilling the core principles of business and delivering them quickly and concisely to people at all stages of their careers. His blog has introduced hundreds of thousands of readers to the best business books and most powerful business concepts of all time. In The Personal MBA, he shares the essentials of sales, marketing, negotiation, strategy, and much more.True leaders aren't made by business schools-they make themselves, seeking out the knowledge, skills, and experiences they need to succeed. Read this book and in one week you will learn the principles it takes most people a lifetime to master.

    In performance, how you organize can be the key to growth. In the past five years, the business world has seen the birth of a new breed of company - the Exponential Organization - that has revolutionized how a company can accelerate its growth by using technology.

    He explains why invisible and unmanaged queues are the underlying root cause of poor product development performance. He shows why these queues form and how they undermine the speed, quality, and efficiency in product development.

    Drawing on Lopp's management experiences at Apple, Netscape, Symantec, and Borland, this book is full of stories based on companies in the Silicon Valley where people have been known to yell at each other. It is a place full of dysfunctional bright people who are in an incredible hurry to find the next big thing so they can strike it rich and then do it all over again. Among these people are managers, a strange breed of people who through a mystical organizational ritual have been given power over your future and your bank account.Whether you're an aspiring manager, a current manager, or just wondering what the heck a manager does all day, there is a story in this book that will speak to you.

    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.” If we can toss aside our fears and learn to approach them clearly—understanding how our own preconceptions lead us astray—statistics can point to ways we can live better and work smarter.As “perhaps the best popular economics writer in the world” (New Statesman), Tim Harford is an expert at taking complicated ideas and untangling them for millions of readers. In The Data Detective, he uses new research in science and psychology to set out ten strategies for using statistics to erase our biases and replace them with new ideas that use virtues like patience, curiosity, and good sense to better understand ourselves and the world. As a result, The Data Detective is a big-idea book about statistics and human behavior that is fresh, unexpected, and insightful.

    In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

    By 1993, the computer industry had changed so rapidly the company was on its way to losing $16 billion and IBM was on a watch list for extinction -- victimized by its own lumbering size, an insular corporate culture, and the PC era IBM had itself helped invent.Then Lou Gerstner was brought in to run IBM. Almost everyone watching the rapid demise of this American icon presumed Gerstner had joined IBM to preside over its continued dissolution into a confederation of autonomous business units. This strategy, well underway when he arrived, would have effectively eliminated the corporation that had invented many of the industry's most important technologies.Instead, Gerstner took hold of the company and demanded the managers work together to re-establish IBM's mission as a customer-focused provider of computing solutions. Moving ahead of his critics, Gerstner made the hold decision to keep the company together, slash prices on his core product to keep the company competitive, and almost defiantly announced, "The last thing IBM needs right now is a vision."Who Says Elephants Can't Dance? tells the story of IBM's competitive and cultural transformation. In his own words, Gerstner offers a blow-by-blow account of his arrival at the company and his campaign to rebuild the leadership team and give the workforce a renewed sense of purpose. In the process, Gerstner defined a strategy for the computing giant and remade the ossified culture bred by the company's own success.The first-hand story of an extraordinary turnaround, a unique case study in managing a crisis, and a thoughtful reflection on the computer industry and the principles of leadership, Who Says Elephants Can't Dance? sums up Lou Gerstner's historic business achievement. Taking readers deep into the world of IBM's CEO, Gerstner recounts the high-level meetings and explains the pressure-filled, no-turning-back decisions that had to be made. He also offers his hard-won conclusions about the essence of what makes a great company run.In the history of modern business, many companies have gone from being industry leaders to the verge of extinction. Through the heroic efforts of a new management team, some of those companies have even succeeded in resuscitating themselves and living on in the shadow of their former stature. But only one company has been at the pinnacle of an industry, fallen to near collapse, and then, beyond anyone's expectations, returned to set the agenda. That company is IBM.Lou Gerstener, Jr., served as chairman and chief executive officer of IBM from April 1993 to March 2002, when he retired as CEO. He remained chairman of the board through the end of 2002. Before joining IBM, Mr. Gerstner served for four years as chairman and CEO of RJR Nabisco, Inc. This was preceded by an eleven-year career at the American Express Company, where he was president of the parent company and chairman and CEO of its largest subsidiary. Prior to that, Mr. Gerstner was a director of the management consulting firm of McKinsey & Co., Inc. He received a bachelor's degree in engineering from Dartmouth College and an MBA from Harvard Business School.

    Tracy Kidder got a preview of this world in the late 1970s when he observed the engineers of Data General design and build a new 32-bit minicomputer in just one year. His thoughtful, prescient book, The Soul of a New Machine, tells stories of 35-year-old "veteran" engineers hiring recent college graduates and encouraging them to work harder and faster on complex and difficult projects, exploiting the youngsters' ignorance of normal scheduling processes while engendering a new kind of work ethic.These days, we are used to the "total commitment" philosophy of managing technical creation, but Kidder was surprised and even a little alarmed at the obsessions and compulsions he found. From in-house political struggles to workers being permitted to tease management to marathon 24-hour work sessions, The Soul of a New Machine explores concepts that already seem familiar, even old-hat, less than 20 years later. Kidder plainly admires his subjects; while he admits to hopeless confusion about their work, he finds their dedication heroic. The reader wonders, though, what will become of it all, now and in the future. —Rob Lightner

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    Kanban leverages visual management techniques to involve stakeholders and to facilitate understanding of how the work works. Through limiting the amount of work in process, and by focusing on finishing that work as soon as possible, kanban helps you to adjust demand to capacity, to reduce lead times and to create a driver for continuous improvement.Kanban in Action is a down-to-earth, no-frills, get-to-know-the-ropes introduction to kanban. It's based on the real-world experience and observations from two kanban coaches who have introduced this process to dozens of teams. In this book, you'll discover basic but powerful techniques on how to visualize and track work, how to construct a kanban board, how to visualize queues and bottlenecks, and much much more. You'll learn the principles of why kanban works as well as nitty-gritty details like how to use different color stickies to help you organize and track your work items.