One of the primary business texts of the modern era, it is based on the work of the Harvard Negotiation Project, a group that deals with all levels of negotiation and conflict resolution. Getting to Yes offers a proven, step-by-step strategy for coming to mutually acceptable agreements in every sort of conflict. Thoroughly updated and revised, it offers readers a straight- forward, universally applicable method for negotiating personal and professional disputes without getting angry-or getting taken.This is by far the best thing I`ve ever read about negotiation. It is equally relevant for the individual who would like to keep his friends, property, and income and the statesman who would like to keep the peace." --John Kenneth Galbraith"

    With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark’s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation

    Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    In Zero to One, legendary entrepreneur and investor Peter Thiel shows how we can find singular ways to create those new things. Thiel begins with the contrarian premise that we live in an age of technological stagnation, even if we’re too distracted by shiny mobile devices to notice. Information technology has improved rapidly, but there is no reason why progress should be limited to computers or Silicon Valley. Progress can be achieved in any industry or area of business. It comes from the most important skill that every leader must master: learning to think for yourself.Doing what someone else already knows how to do takes the world from 1 to n, adding more of something familiar. But when you do something new, you go from 0 to 1. The next Bill Gates will not build an operating system. The next Larry Page or Sergey Brin won’t make a search engine. Tomorrow’s champions will not win by competing ruthlessly in today’s marketplace. They will escape competition altogether, because their businesses will be unique. Zero to One presents at once an optimistic view of the future of progress in America and a new way of thinking about innovation: it starts by learning to ask the questions that lead you to find value in unexpected places.

    R is packed with powerful programming capabilities, but learning to use R in the real world can be overwhelming for even the most seasoned statisticians. This easy-to-follow guide explains how to use R for data processing and statistical analysis, and then, shows you how to present your data using compelling and informative graphics. You'll gain practical experience using R in a variety of settings and delve deeper into R's feature-rich toolset.Includes tips for the initial installation of RDemonstrates how to easily perform calculations on vectors, arrays, and lists of dataShows how to effectively visualize data using R's powerful graphics packagesGives pointers on how to find, install, and use add-on packages created by the R communityProvides tips on getting additional help from R mailing lists and websitesWhether you're just starting out with statistical analysis or are a procedural programming pro, "R For Dummies" is the book you need to get the most out of R.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself.Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines.The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness.In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways.Written with acclaimed science writer Sandra Blakeslee, On Intelligence promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.

    Crossing the Chasm has become the bible for bringing cutting-edge products to progressively larger markets. This edition provides new insights into the realities of high-tech marketing, with special emphasis on the Internet. It's essential reading for anyone with a stake in the world's most exciting marketplace.

    Dr. Robert Cialdini is the seminal expert in the rapidly expanding field of influence and persuasion. His thirty-five years of rigorous, evidence-based research along with a three-year program of study on what moves people to change behavior has resulted in this highly acclaimed book.You'll learn the six universal principles, how to use them to become a skilled persuader—and how to defend yourself against them. Perfect for people in all walks of life, the principles of Influence will move you toward profound personal change and act as a driving force for your success.

    But today, success is increasingly dependent on how we interact with others. It turns out that at work, most people operate as either takers, matchers, or givers. Whereas takers strive to get as much as possible from others and matchers aim to trade evenly, givers are the rare breed of people who contribute to others without expecting anything in return. Using his own pioneering research as Wharton's youngest tenured professor, Grant shows that these styles have a surprising impact on success. Although some givers get exploited and burn out, the rest achieve extraordinary results across a wide range of industries. Combining cutting-edge evidence with captivating stories, this landmark book shows how one of America's best networkers developed his connections, why the creative genius behind one of the most popular shows in television history toiled for years in anonymity, how a basketball executive responsible for multiple draft busts transformed his franchise into a winner, and how we could have anticipated Enron's demise four years before the company collapsed - without ever looking at a single number. Praised by bestselling authors such as Dan Pink, Tony Hsieh, Dan Ariely, Susan Cain, Dan Gilbert, Gretchen Rubin, Bob Sutton, David Allen, Robert Cialdini, and Seth Godin-as well as senior leaders from Google, McKinsey, Merck, Estee Lauder, Nike, and NASA - Give and Take highlights what effective networking, collaboration, influence, negotiation, and leadership skills have in common. This landmark book opens up an approach to success that has the power to transform not just individuals and groups, but entire organizations and communities.

    "We spend more time working than doing anything else in life. It's not right that the experience of work should be so demotivating and dehumanizing." So says Laszlo Bock, head of People Operations at the company that transformed how the world interacts with knowledge. This insight is the heart of WORK RULES!, a compelling and surprisingly playful manifesto that offers lessons including:Take away managers' power over employeesLearn from your best employees-and your worstHire only people who are smarter than you are, no matter how long it takes to find themPay unfairly (it's more fair!)Don't trust your gut: Use data to predict and shape the futureDefault to open-be transparent and welcome feedbackIf you're comfortable with the amount of freedom you've given your employees, you haven't gone far enough. Drawing on the latest research in behavioral economics and a profound grasp of human psychology, WORK RULES! also provides teaching examples from a range of industries-including lauded companies that happen to be hideous places to work and little-known companies that achieve spectacular results by valuing and listening to their employees. Bock takes us inside one of history's most explosively successful businesses to reveal why Google is consistently rated one of the best places to work in the world, distilling 15 years of intensive worker R&D into principles that are easy to put into action, whether you're a team of one or a team of thousands. WORK RULES! shows how to strike a balance between creativity and structure, leading to success you can measure in quality of life as well as market share. Read it to build a better company from within rather than from above; read it to reawaken your joy in what you do.

    Characterized by a range of new technologies that are fusing the physical, digital and biological worlds, the developments are affecting all disciplines, economies, industries and governments, and even challenging ideas about what it means to be human.Artificial intelligence is already all around us, from supercomputers, drones and virtual assistants to 3D printing, DNA sequencing, smart thermostats, wear­able sensors and microchips smaller than a grain of sand. But this is just the beginning: nanomaterials 200 times stronger than steel and a million times thinner than a strand of hair and the first transplant of a 3D printed liver are already in development. Imagine “smart factories” in which global systems of manu­facturing are coordinated virtually, or implantable mobile phones made of biosynthetic materials.The fourth industrial revolution, says Schwab, is more significant, and its ramifications more profound, than in any prior period of human history. He outlines the key technologies driving this revolution and discusses the major impacts expected on government, business, civil society and individu­als. Schwab also offers bold ideas on how to harness these changes and shape a better future—one in which technology empowers people rather than replaces them; progress serves society rather than disrupts it; and in which innovators respect moral and ethical boundaries rather than cross them. We all have the opportunity to contribute to developing new frame­works that advance progress.

    In this one-of-a-kind text, author V. Anton Spraul breaks down the ways that programmers solve problems and teaches you what other introductory books often ignore: how to Think Like a Programmer. Each chapter tackles a single programming concept, like classes, pointers, and recursion, and open-ended exercises throughout challenge you to apply your knowledge. You'll also learn how to:Split problems into discrete components to make them easier to solve Make the most of code reuse with functions, classes, and libraries Pick the perfect data structure for a particular job Master more advanced programming tools like recursion and dynamic memory Organize your thoughts and develop strategies to tackle particular types of problems Although the book's examples are written in C++, the creative problem-solving concepts they illustrate go beyond any particular language; in fact, they often reach outside the realm of computer science. As the most skillful programmers know, writing great code is a creative art—and the first step in creating your masterpiece is learning to Think Like a Programmer.

    Along the way we’ll learn how selection bias can explain why your boss doesn’t know he sucks (even when everyone else does); how to use Bayes’ Theorem to decide if your partner is cheating on you; and why Mark Zuckerberg should never be used as an example for anything. See the world in a whole new light, and make better decisions and judgements without ever going near a t-test. Think. Think Statistically.

    This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.