Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    But concern over what Louis Brandeis called the "curse of bigness" can no longer remain the province of specialist lawyers and economists, for it has spilled over into policy and politics, even threatening democracy itself. History suggests that tolerance of inequality and failing to control excessive corporate power may prompt the rise of populism, nationalism, extremist politicians, and fascist regimes. In short, as Wu warns, we are in grave danger of repeating the signature errors of the twentieth century.In The Curse of Bigness, Columbia professor Tim Wu tells of how figures like Brandeis and Theodore Roosevelt first confronted the democratic threats posed by the great trusts of the Gilded Age--but the lessons of the Progressive Era were forgotten in the last 40 years. He calls for recovering the lost tenets of the trustbusting age as part of a broader revival of American progressive ideas as we confront the fallout of persistent and extreme economic inequality.

    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

    If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others. Whether you're a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in Head First Data Analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool. You'll learn how to:Determine which data sources to use for collecting information Assess data quality and distinguish signal from noise Build basic data models to illuminate patterns, and assimilate new information into the models Cope with ambiguous information Design experiments to test hypotheses and draw conclusions Use segmentation to organize your data within discrete market groups Visualize data distributions to reveal new relationships and persuade others Predict the future with sampling and probability models Clean your data to make it useful Communicate the results of your analysis to your audience Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First Data Analysis uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.

    But satisfactory answers have been hard to find for lack of adequate data and clear guiding theories. In Capital in the Twenty-First Century, Thomas Piketty analyzes a unique collection of data from twenty countries, ranging as far back as the eighteenth century, to uncover key economic and social patterns. His findings will transform debate and set the agenda for the next generation of thought about wealth and inequality.Piketty shows that modern economic growth and the diffusion of knowledge have allowed us to avoid inequalities on the apocalyptic scale predicted by Karl Marx. But we have not modified the deep structures of capital and inequality as much as we thought in the optimistic decades following World War II. The main driver of inequality—the tendency of returns on capital to exceed the rate of economic growth—today threatens to generate extreme inequalities that stir discontent and undermine democratic values. But economic trends are not acts of God. Political action has curbed dangerous inequalities in the past, Piketty says, and may do so again.

    The implications of this argument are highly significant. If this is the case, methods are always political, and it raises the question of what kinds of social realities we want to create.Most current methods look for clarity and precision. It is usually said that only poor research produces messy findings, and the idea that things in the world might be fluid, elusive, or multiple is unthinkable. Law's startling argument is that this is wrong and it is time for a new approach. Many realities, he says, are vague and ephemeral. If methods want to know and help to shape the world, then they need to reinvent themselves and their politics to deal with mess. That is the challenge. Nothing less will do.

    We all know that the very rich have gotten a lot richer these past few decades while most Americans haven’t. In fact, the exorbitantly paid have continued to thrive during the current economic crisis, even as the rest of Americans have continued to fall behind. Why do the “haveit- alls” have so much more? And how have they managed to restructure the economy to reap the lion’s share of the gains and shift the costs of their new economic playground downward, tearing new holes in the safety net and saddling all of us with increased debt and risk? Lots of so-called experts claim to have solved this great mystery, but no one has really gotten to the bottom of it—until now. In their lively and provocative Winner-Take-All Politics, renowned political scientists Jacob S. Hacker and Paul Pierson demonstrate convincingly that the usual suspects—foreign trade and financial globalization, technological changes in the workplace, increased education at the top—are largely innocent of the charges against them. Instead, they indict an unlikely suspect and take us on an entertaining tour of the mountain of evidence against the culprit. The guilty party is American politics. Runaway inequality and the present economic crisis reflect what government has done to aid the rich and what it has not done to safeguard the interests of the middle class. The winner-take-all economy is primarily a result of winner-take-all politics. In an innovative historical departure, Hacker and Pierson trace the rise of the winner-take-all economy back to the late 1970s when, under a Democratic president and a Democratic Congress, a major transformation of American politics occurred. With big business and conservative ideologues organizing themselves to undo the regulations and progressive tax policies that had helped ensure a fair distribution of economic rewards, deregulation got under way, taxes were cut for the wealthiest, and business decisively defeated labor in Washington. And this transformation continued under Reagan and the Bushes as well as under Clinton, with both parties catering to the interests of those at the very top. Hacker and Pierson’s gripping narration of the epic battles waged during President Obama’s first two years in office reveals an unpleasant but catalyzing truth: winner-take-all politics, while under challenge, is still very much with us. Winner-Take-All Politics—part revelatory history, part political analysis, part intellectual journey— shows how a political system that traditionally has been responsive to the interests of the middle class has been hijacked by the superrich. In doing so, it not only changes how we think about American politics, but also points the way to rebuilding a democracy that serves the interests of the many rather than just those of the wealthy few.

    Explaining why the whole is sometimes smarter than the sum of its parts, Johnson presents surprising examples of feedback, self-organization, and adaptive learning. How does a lively neighborhood evolve out of a disconnected group of shopkeepers, bartenders, and real estate developers? How does a media event take on a life of its own? How will new software programs create an intelligent World Wide Web? In the coming years, the power of self-organization -- coupled with the connective technology of the Internet -- will usher in a revolution every bit as significant as the introduction of electricity. Provocative and engaging, Emergence puts you on the front lines of this exciting upheaval in science and thought.

    This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

    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.

    With this book, you'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's programming tools.RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You'll gain valuable programming skills and support your work as a data scientist at the same time.Work hands-on with three practical data analysis projects based on casino gamesStore, retrieve, and change data values in your computer's memoryWrite programs and simulations that outperform those written by typical R usersUse R programming tools such as if else statements, for loops, and S3 classesLearn how to write lightning-fast vectorized R codeTake advantage of R's package system and debugging toolsPractice and apply R programming concepts as you learn them

    This urgent report is required reading, from defense expert P.W. Singer and Council on Foreign Relations fellow Emerson Brooking.

     into their ideal job   Each year tens of thousands of students will, after years of hard work and enormous amounts of money, earn their Ph.D. And each year only a small percentage of them will land a job that justifies and rewards their investment. For every comfortably tenured professor or well-paid former academic, there are countless underpaid and overworked adjuncts, and many more who simply give up in frustration.   Those who do make it share an important asset that separates them from the pack: they have a plan. They understand exactly what they need to do to set themselves up for success.  They know what really moves the needle in academic job searches, how to avoid the all-too-common mistakes that sink so many of their peers, and how to decide when to point their Ph.D. toward other, non-academic options.   Karen Kelsky has made it her mission to help readers join the select few who get the most out of their Ph.D. As a former tenured professor and department head who oversaw numerous academic job searches, she knows from experience exactly what gets an academic applicant a job. And as the creator of the popular and widely respected advice site The Professor is In, she has helped countless Ph.D.’s turn themselves into stronger applicants and land their dream careers.   Now, for the first time ever, Karen has poured all her best advice into a single handy guide that addresses the most important issues facing any Ph.D., including:   -When, where, and what to publish -Writing a foolproof grant application -Cultivating references and crafting the perfect CV -Acing the job talk and campus interview -Avoiding the adjunct trap -Making the leap to nonacademic work, when the time is right  The Professor Is In addresses all of these issues, and many more.

    Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions.Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more.Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses Helps readers see how to shepherd predictive analytics projects through their companies Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies.

    In How Not to Be Wrong, Jordan Ellenberg shows us how terribly limiting this view is: Math isn’t confined to abstract incidents that never occur in real life, but rather touches everything we do—the whole world is shot through with it.Math allows us to see the hidden structures underneath the messy and chaotic surface of our world. It’s a science of not being wrong, hammered out by centuries of hard work and argument. Armed with the tools of mathematics, we can see through to the true meaning of information we take for granted: How early should you get to the airport? What does “public opinion” really represent? Why do tall parents have shorter children? Who really won Florida in 2000? And how likely are you, really, to develop cancer?How Not to Be Wrong presents the surprising revelations behind all of these questions and many more, using the mathematician’s method of analyzing life and exposing the hard-won insights of the academic community to the layman—minus the jargon. Ellenberg chases mathematical threads through a vast range of time and space, from the everyday to the cosmic, encountering, among other things, baseball, Reaganomics, daring lottery schemes, Voltaire, the replicability crisis in psychology, Italian Renaissance painting, artificial languages, the development of non-Euclidean geometry, the coming obesity apocalypse, Antonin Scalia’s views on crime and punishment, the psychology of slime molds, what Facebook can and can’t figure out about you, and the existence of God.Ellenberg pulls from history as well as from the latest theoretical developments to provide those not trained in math with the knowledge they need. Math, as Ellenberg says, is “an atomic-powered prosthesis that you attach to your common sense, vastly multiplying its reach and strength.” With the tools of mathematics in hand, you can understand the world in a deeper, more meaningful way. How Not to Be Wrong will show you how.