It's not just a technical book or just a businessguide. Data is ubiquitous and it doesn't pay much attention toborders, so we've calibrated our coverage to follow it wherever itgoes.In the first edition of Big Data Now, the O'Reilly team tracked thebirth and early development of data tools and data science. Now, withthis second edition, we're seeing what happens when big data grows up:how it's being applied, where it's playing a role, and theconsequences -- good and bad alike -- of data's ascendance.We've organized the second edition of Big Data Now into five areas:Getting Up to Speed With Big Data -- Essential information on thestructures and definitions of big data.Big Data Tools, Techniques, and Strategies -- Expert guidance forturning big data theories into big data products.The Application of Big Data -- Examples of big data in action,including a look at the downside of data.What to Watch for in Big Data -- Thoughts on how big data will evolveand the role it will play across industries and domains.Big Data and Health Care -- A special section exploring thepossibilities that arise when data and health care come together.

    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.

    Merriam synthesizes twenty years of developments in qualitative research with clarity and acumen." ?Michael Quinn Patton, author, Qualitative Research and Evaluation Methods"Here is a qualitative research methods book that reinforces the connection between professional experience and qualitative inquiry." ?Robert Stake, author, The Art of Case Study Research and Multiple Case Study Analysis"In this new edition, Sharan Merriam once again presents the world of qualitative research in language engaging and accessible?for new and experienced readers alike. If you can have only one book about qualitative research, this is it!" ?Patricia M. Reeves, associate professor, School of Social Work, University of Georgia"Mystified by qualitative research? You couldn't ask for a better guide than Sharan Merriam, who introduces you to the fundamental concepts of this research method, explains its complex forms, and then shows you exactly how to do a high-quality qualitative study." ?M. Carolyn Clark, coeditor, The International Journal of Qualitative Studies in Education

    The average young person in the UK will spend 10,000 hours gaming by the age of twenty-one. What's causing this mass exodus? According to world-renowned game designer Jane McGonigal the answer is simple: videogames are fulfilling genuine human needs. Drawing on positive psychology, cognitive science and sociology, Reality is Broken shows how game designers have hit on core truths about what makes us happy, and utilized these discoveries to astonishing effect in virtual environments. But why, McGonigal asks, should we use the power of games for escapist entertainment alone? In this groundbreaking exploration of the power and future of gaming, she reveals how gamers have become expert problem solvers and collaborators, and shows how we can use the lessons of game design to socially positive ends, be it in our own lives, our communities or our businesses. Written for gamers and non-gamers alike, Reality is Broken sends a clear and provocative message: the future will belong to those who can understand, design and play games.

    It's the art of anticipating your opponent's next moves, knowing full well that your rival is trying to do the same thing to you. Though parts of game theory involve simple common sense, much is counterintuitive, and it can only be mastered by developing a new way of seeing the world. Using a diverse array of rich case studies—from pop culture, TV, movies, sports, politics, and history—the authors show how nearly every business and personal interaction has a game-theory component to it. Are the winners of reality-TV contests instinctive game theorists? Do big-time investors see things that most people miss? What do great poker players know that you don't? Mastering game theory will make you more successful in business and life, and this lively book is the key to that mastery.

    A generation ago, Christensen revolutionized business with his groundbreaking theory of disruptive innovation. Now, he goes further, offering powerful new insights.After years of research, Christensen has come to one critical conclusion: our long held maxim—that understanding the customer is the crux of innovation—is wrong. Customers don’t buy products or services; they "hire" them to do a job. Understanding customers does not drive innovation success, he argues. Understanding customer jobs does. The "Jobs to Be Done" approach can be seen in some of the world’s most respected companies and fast-growing startups, including Amazon, Intuit, Uber, Airbnb, and Chobani yogurt, to name just a few. But this book is not about celebrating these successes—it’s about predicting new ones.Christensen contends that by understanding what causes customers to "hire" a product or service, any business can improve its innovation track record, creating products that customers not only want to hire, but that they’ll pay premium prices to bring into their lives. Jobs theory offers new hope for growth to companies frustrated by their hit and miss efforts.This book carefully lays down Christensen’s provocative framework, providing a comprehensive explanation of the theory and why it is predictive, how to use it in the real world—and, most importantly, how not to squander the insights it provides.

    It doesn't shy away from the full range of viewpoints or from the most controversial issues--from superintelligence to meaning, consciousness and the ultimate physical limits on life in the cosmos.

    No statistical knowledge is required, and your programming skills can range from hobbyist to pro.Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: Create artful graphs to visualize complex data sets and functions Write more efficient code using parallel R and vectorization Interface R with C/C++ and Python for increased speed or functionality Find new R packages for text analysis, image manipulation, and more Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    Offering an approach with a visual/graphical emphasis, this text offers a number of examples on the premise that students learn best by doing. This book features an emphasis on interpretation of results and critical thinking over calculations.

    In Standard Deviations, economics professor Gary Smith walks us through the various tricks and traps that people use to back up their own crackpot theories. Sometimes, the unscrupulous deliberately try to mislead us. Other times, the well-intentioned are blissfully unaware of the mischief they are committing. Today, data is so plentiful that researchers spend precious little time distinguishing between good, meaningful indicators and total rubbish. Not only do others use data to fool us, we fool ourselves.With the breakout success of Nate Silver’s The Signal and the Noise, the once humdrum subject of statistics has never been hotter. Drawing on breakthrough research in behavioral economics by luminaries like Daniel Kahneman and Dan Ariely and taking to task some of the conclusions of Freakonomics author Steven D. Levitt, Standard Deviations demystifies the science behind statistics and makes it easy to spot the fraud all around.

    R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks. Lander's self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You'll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you'll construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time you're done, you won't just know how to write R programs, you'll be ready to tackle the statistical problems you care about most. COVERAGE INCLUDES - Exploring R, RStudio, and R packages - Using R for math: variable types, vectors, calling functions, and more - Exploiting data structures, including data.frames, matrices, and lists - Creating attractive, intuitive statistical graphics - Writing user-defined functions - Controlling program flow with if, ifelse, and complex checks - Improving program efficiency with group manipulations - Combining and reshaping multiple datasets - Manipulating strings using R's facilities and regular expressions - Creating normal, binomial, and Poisson probability distributions - Programming basic statistics: mean, standard deviation, and t-tests - Building linear, generalized linear, and nonlinear models - Assessing the quality of models and variable selection - Preventing overfitting, using the Elastic Net and Bayesian methods - Analyzing univariate and multivariate time series data - Grouping data via K-means and hierarchical clustering - Preparing reports, slideshows, and web pages with knitr - Building reusable R packages with devtools and Rcpp - Getting involved with the R global community

    Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. This book can be used for readers who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.

    It could forever change how you experience and interact with your physical surroundings, open your eyes to the perversity of bad design and the desirability of good design, and raise your expectations about how things should be designed.B & W photographs and illustrations throughout.

    Though following best practice can help in some ways, it also poses a danger: By emulating what a great leader did in a particular situation, you'll likely be terribly disappointed with your own results. Why? Your situation is different.Instead of focusing on what exceptional leaders do, we need to understand and emulate how they think. Successful businesspeople engage in what Martin calls integrative thinking creatively resolving the tension in opposing models by forming entirely new and superior ones. Drawing on stories of leaders as diverse as AG Lafley of Procter & Gamble, Meg Whitman of eBay, Victoria Hale of the Institute for One World Health, and Nandan Nilekani of Infosys, Martin shows how integrative thinkers are relentlessly diagnosing and synthesizing by asking probing questions including: What are the causal relationships at work here? and What are the implied trade-offs?Martin also presents a model for strengthening your integrative thinking skills by drawing on different kinds of knowledge including conceptual and experiential knowledge.Integrative thinking can be learned, and The Opposable Mind helps you master this vital skill.