It’s not about to-do lists and accountability charts. It’s about creating a framework for regular check-ins, key results, and most of all, the beauty of a good fail – and how to take a temporary disaster and turn it into a future success. In this book, Wodtke takes you through the fictional case study of Hanna and Jack, who are struggling to survive in their own startup. They fight shiny object syndrome, losing focus, and dealing with communication issues. After hard lessons, they learn the practical steps they need to do what must be done. The second half of the book demonstrates how to use Objectives and Key Results (OKRs) to help teams realize big goals in a methodical way, leaving nothing to chance. Laid out in a practical but compelling way, she makes the lessons of Hanna and Jack’s story clear and actionable. Ready to move your team in the right direction? Read this, and learn the system of creating your focus – and finding success. "Busy grinding without purpose is the secret death of too many startups. In this memorable story, Christina gives us a glimpse of a more satisfying kind of startup--still hard and chaotic but full of purpose and the chance to build something great." James Cham, Founder, Bloomberg Beta "This book is useful, actionable, and actually fun to read! If you want to get your team aligned around real, measurable goals, Radical Focus will teach you how to do it quickly and clearly." -Laura Klein, Principal, Users Know "Someone once told me that 'problems are just opportunities that haven’t presented themselves'. Since I was introduced to OKRs, they've been an invaluable tool for me, and our company. Christina's ideas have been instrumental, allowing me to better navigate the often ambiguous approach to goal setting and along the way creating a more open and accountable team and a clearer path for myself professionally. I personally can't thank her enough for the guidance." Scott Baldwin, Director of Services, Yellow Pencil "Radical Focus illustrates how to implement OKRs in an engaging, compact, realistic story. Best of all, Wodtke proves OKRs can be fun!" Ben Lamorte, OKRs.com

    Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

    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.

    Cooper details many of these meta functions to explain his central thesis: programmers need to seriously re-evaluate the many user-hostile concepts deeply embedded within the software development process. Rather than provide users with a straightforward set of options, programmers often pile on the bells and whistles and ignore or de-prioritise lingering bugs. For the average user, increased functionality is a great burden, adding to the recurrent chorus that plays: "computers are hard, mysterious, unwieldy things." (An average user, Cooper asserts, who doesn't think that way or who has memorised all the esoteric commands and now lords it over others, has simply been desensitised by too many years of badly designed software.) Cooper's writing style is often overblown, with a pantheon of cutesy terminology (i.e. "dancing bearware") and insider back-patting. (When presenting software to Bill Gates, he reports that Gates replied: "How did you do that?" to which he writes: "I love stumping Bill!") More seriously, he is also unable to see beyond software development's importance--a sin he accuses programmers of throughout the book. Even with that in mind, the central questions Cooper asks are too important to ignore: Are we making users happier? Are we improving the process by which they get work done? Are we making their work hours more effective? Cooper looks to programmers, business managers and what he calls "interaction designers" to question current assumptions and mindsets. Plainly, he asserts that the goal of computer usage should be "not to make anyone feel stupid." Our distance from that goal reinforces the need to rethink entrenched priorities in software planning. -- Jennifer Buckendorff, Amazon.com

    Rather than believing they have to start with a big idea or plan a whole project out in advance, trying to foresee the final outcome, they make a series of little bets about what might be a good direction, learning from lots of little failures and from small but highly significant wins that allow them to happen upon unexpected avenues and arrive at extraordinary outcomes.           Based on deep and extensive research, including more than 200 interviews with leading innovators, Sims discovered that productive, creative thinkers and doers—from Ludwig van Beethoven to Thomas Edison and Amazon’s Jeff Bezos—practice a key set of simple but ingenious experimental methods—such as failing quickly to learn fast, tapping into the genius of play, and engaging in highly immersed observation—that free their minds, opening them up to making unexpected connections and perceiving invaluable insights. These methods also unshackle them from the constraints of overly analytical thinking and linear problem solving that our education places so much emphasis on, as well as from the fear of failure, all of which thwart so many of us in trying to be more innovative.               Reporting on a fascinating range of research, from the psychology of creative blocks to the influential Silicon Valley–based field of design thinking, Sims offers engaging and wonderfully illuminating accounts of breakthrough innovators at work, including how Hewlett-Packard stumbled onto the breakaway success of the first hand-held calculator; the remarkable storyboarding process at Pixar films that has been the key to their unbroken streak of box office successes; the playful discovery process by which Frank Gehry arrived at his critically acclaimed design for Disney Hall; the aha revelation that led Amazon to pursue its wildly successful affiliates program; and the U.S. Army’s ingenious approach to counterinsurgency operations that led to the dramatic turnaround in Iraq.               Fast paced and as entertaining as it is illuminating, Little Bets offers a whole new way of thinking about how to break away from the narrow strictures of the methods of analyzing and problem solving we were all taught in school and unleash our untapped creative powers.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    Politicians and marketers present shoddy evidence for dubious claims all the time. But smart people make mistakes too, and when it comes to statistics, plenty of otherwise great scientists--yes, even those published in peer-reviewed journals--are doing statistics wrong."Statistics Done Wrong" comes to the rescue with cautionary tales of all-too-common statistical fallacies. It'll help you see where and why researchers often go wrong and teach you the best practices for avoiding their mistakes.In this book, you'll learn: - Why "statistically significant" doesn't necessarily imply practical significance- Ideas behind hypothesis testing and regression analysis, and common misinterpretations of those ideas- How and how not to ask questions, design experiments, and work with data- Why many studies have too little data to detect what they're looking for-and, surprisingly, why this means published results are often overestimates- Why false positives are much more common than "significant at the 5% level" would suggestBy walking through colorful examples of statistics gone awry, the book offers approachable lessons on proper methodology, and each chapter ends with pro tips for practicing scientists and statisticians. No matter what your level of experience, "Statistics Done Wrong" will teach you how to be a better analyst, data scientist, or researcher.

    For millions of years, humans have been seeking out tribes, be they religious, ethnic, economic, political, or even musical (think of the Deadheads). It's our nature.Now the Internet has eliminated the barriers of geography, cost, and time. All those blogs and social networking sites are helping existing tribes get bigger. But more important, they're enabling countless new tribes to be born—groups of ten or ten thousand or ten million who care about their iPhones, or a political campaign, or a new way to fight global warming. And so the key question: Who is going to lead us?The Web can do amazing things, but it can't provide leadership. That still has to come from individuals—people just like you who have passion about something. The explosion in tribes means that anyone who wants to make a difference now has the tools at her fingertips.If you think leadership is for other people, think again—leaders come in surprising packages. Consider Joel Spolsky and his international tribe of scary-smart software engineers. Or Gary Vaynerhuck, a wine expert with a devoted following of enthusiasts. Chris Sharma leads a tribe of rock climbers up impossible cliff faces, while Mich Mathews, a VP at Microsoft, runs her internal tribe of marketers from her cube in Seattle. All they have in common is the desire to change things, the ability to connect a tribe, and the willingness to lead.If you ignore this opportunity, you risk turning into a "sheepwalker"—someone who fights to protect the status quo at all costs, never asking if obedience is doing you (or your organization) any good. Sheepwalkers don't do very well these days.Tribes will make you think (really think) about the opportunities in leading your fellow employees, customers, investors, believers, hobbyists, or readers. . . . It's not easy, but it's easier than you think.

    In this updated and expanded 10th anniversary edition, internationally known leadership transition expert Michael D. Watkins gives you the keys to successfully negotiating your next move—whether you’re onboarding into a new company, being promoted internally, or embarking on an international assignment.In The First 90 Days, Watkins outlines proven strategies that will dramatically shorten the time it takes to reach what he calls the "breakeven point" when your organization needs you as much as you need the job. This new edition includes a substantial new preface by the author on the new definition of a career as a series of transitions; and notes the growing need for effective and repeatable skills for moving through these changes. As well, updated statistics and new tools make this book more reader-friendly and useful than ever.As hundreds of thousands of readers already know, The First 90 Days is a road map for taking charge quickly and effectively during critical career transition periods—whether you are a first-time manager, a mid-career professional on your way up, or a newly minted CEO.

    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 FiveThirtyEight.com. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the "prediction paradox": The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good-or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary-and dangerous-science.Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise.

    Yet, managing human beings and navigating complex product roadmaps is no easy task, and it's rare to find a product leader who can steward a digital product from concept to launch without a couple of major hiccups. Why do some product leaders succeed while others don't?This insightful book presents interviews with nearly 100 leading product managers from all over the world. Authors Richard Banfield, Martin Eriksson, and Nate Walkingshaw draw on decades of experience in product design and development to capture the approaches, styles, insights, and techniques of successful product managers. If you want to understand what drives good product leaders, this book is an irreplaceable resource.In three parts, Product Leadership helps you explore:Themes and patterns of successful teams and their leaders, and ways to attain those characteristicsThe best approaches for guiding your product team through the startup, emerging, and enterprise stages of a company's evolutionStrategies and tactics for working with customers, agencies, partners, and external stakeholders

    They yearn to come up with a game—changing innovation like Apple's iPod, or create an entirely new category like Facebook. Many make genuine efforts to be innovative—they spend on R&D, bring in creative designers, hire innovation consultants. But they get disappointing results.Why? In The Design of Business, Roger Martin offers a compelling and provocative answer: we rely far too exclusively on analytical thinking, which merely refines current knowledge, producing small improvements to the status quo.To innovate and win, companies need design thinking. This form of thinking is rooted in how knowledge advances from one stage to another—from mystery (something we can't explain) to heuristic (a rule of thumb that guides us toward solution) to algorithm (a predictable formula for producing an answer) to code (when the formula becomes so predictable it can be fully automated). As knowledge advances across the stages, productivity grows and costs drop-creating massive value for companies.Martin shows how leading companies such as Procter & Gamble, Cirque du Soleil, RIM, and others use design thinking to push knowledge through the stages in ways that produce breakthrough innovations and competitive advantage.Filled with deep insights and fresh perspectives, The Design of Business reveals the true foundation of successful, profitable innovation.

    Adopting Lean will take time and commitment, but it’s vital for harnessing the cultural and technical forces that are accelerating the rate of innovation.* Discover how Lean focuses on people and teamwork at every level, in contrast to traditional management practices* Approach problem-solving experimentally, by exploring solutions, testing assumptions, and getting feedback from real users* Lead and manage large-scale programs in a way that empowers employees, increases the speed and quality of delivery, and lowers costs* Learn how to implement ideas from the DevOps and Lean Startup movements even in complex, regulated environments

    But have you ever wondered how Google makes billions of dollars while providing search, email, and maps for free? How do they figure out which ads perfectly capture your interests? And how do they search the entire internet so quickly, anyway?By answering real-world questions like this, Swipe to Unlock gives you a peek under the hood of the technology you use every day, decodes technologists' weirdest buzzwords, and shows you how technology is changing the society we live in for better or for worse. Unlock the answers you need to become a better-educated consumer, digital citizen, or technology professional.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details