Based on research in the USA, Asia, Latin America, and Europe, it aims to formulate a systematic theory of the information society which takes account of the fundamental effects of information technology on the contemporary world.

    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

    "Web Analytics 2.0" presents a new framework that will permanently change how you think about analytics. It provides specific recommendations for creating an actionable strategy, applying analytical techniques correctly, solving challenges such as measuring social media and multichannel campaigns, achieving optimal success by leveraging experimentation, and employing tactics for truly listening to your customers. The book will help your organization become more data driven while you become a super analysis ninja Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

    General introductions by Janet Murray and Lev Manovich, along with short introductions to each of the texts, place the works in their historical context and explain their significance. The texts were originally published between World War II--when digital computing, cybernetic feedback, and early notions of hypertext and the Internet first appeared--and the emergence of the World Wide Web--when they entered the mainstream of public life.The texts are by computer scientists, artists, architects, literary writers, interface designers, cultural critics, and individuals working across disciplines. The contributors include (chronologically) Jorge Luis Borges, Vannevar Bush, Alan Turing, Ivan Sutherland, William S. Burroughs, Ted Nelson, Italo Calvino, Marshall McLuhan, Billy Kl?Jean Baudrillard, Nicholas Negroponte, Alan Kay, Bill Viola, Sherry Turkle, Richard Stallman, Brenda Laurel, Langdon Winner, Robert Coover, and Tim Berners-Lee. The CD accompanying the book contains examples of early games, digital art, independent literary efforts, software created at universities, and home-computer commercial software. Also on the CD is digitized video, documenting new media programs and artwork for which no operational version exists. One example is a video record of Douglas Engelbart's first presentation of the mouse, word processor, hyperlink, computer-supported cooperative work, video conferencing, and the dividing up of the screen we now call non-overlapping windows; another is documentation of Lynn Hershman's Lorna, the first interactive video art installation.

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    Sold in over 85 countries and translated into more than 5 languages.---------------Want to get started on data science?Our promise: no math added.This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations and visuals.Popular concepts covered include:- A/B Testing- Anomaly Detection- Association Rules- Clustering- Decision Trees and Random Forests- Regression Analysis- Social Network Analysis- Neural NetworksFeatures:- Intuitive explanations and visuals- Real-world applications to illustrate each algorithm- Point summaries at the end of each chapter- Reference sheets comparing the pros and cons of algorithms- Glossary list of commonly-used termsWith this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.

     Once one of Silicon Valley’s greatest success stories, Facebook has been under constant fire for the past five years, roiled by controversies and crises. It turns out that while the tech giant was connecting the world, they were also mishandling users’ data, spreading fake news, and amplifying dangerous, polarizing hate speech. The company, many said, had simply lost its way. But the truth is far more complex. Leadership decisions enabled, and then attempted to deflect attention from, the crises. Time after time, Facebook’s engineers were instructed to create tools that encouraged people to spend as much time on the platform as possible, even as those same tools boosted inflammatory rhetoric, conspiracy theories, and partisan filter bubbles. And while consumers and lawmakers focused their outrage on privacy breaches and misinformation, Facebook solidified its role as the world’s most voracious data-mining machine, posting record profits, and shoring up its dominance via aggressive lobbying efforts. Drawing on their unrivaled sources, Sheera Frenkel and Cecilia Kang take readers inside the complex court politics, alliances and rivalries within the company to shine a light on the fatal cracks in the architecture of the tech behemoth. Their explosive, exclusive reporting led them to a shocking conclusion: The missteps of the last five years were not an anomaly but an inevitability—this is how Facebook was built to perform. In a period of great upheaval, growth has remained the one constant under the leadership of Mark Zuckerberg and Sheryl Sandberg. Both have been held up as archetypes of uniquely 21st century executives—he the tech “boy genius” turned billionaire, she the ultimate woman in business, an inspiration to millions through her books and speeches. But sealed off in tight circles of advisers and hobbled by their own ambition and hubris, each has stood by as their technology is coopted by hate-mongers, criminals and corrupt political regimes across the globe, with devastating consequences. In An Ugly Truth, they are at last held accountable.

    In this third edition of The Oxford Guide to Library Research, Thomas Mann maps out an array not just of important databases and print sources, but of several specific search techniques that can be applied profitably in any area of research. From academic resources to government documents to manuscripts in archives to business Web sites, Mann shows readers how best to exploit controlled subject headings, explains why browsing library shelves is still important in an online age, demonstrates how citation searching and related record searching produce results far beyond keyword inquiries, and offers practical tips on making personal contacts with knowledgeable people. Against the trendy but mistaken assumption that "everything" can be found on the Internet, Mann shows the lasting value of physical libraries and the unexpected power of traditional search mechanisms, while also providing the best overview of the new capabilities of computer indexing. Throughout the book Mann enlivens his advice with real-world examples derived from his experience of having helped thousands of researchers, with interests in all subjects areas, over a quarter century. Along the way he provides striking demonstrations and powerful arguments against those theorists who have mistakenly announced the demise of print. Essential reading for students, scholars, professional researchers, and laypersons, The Oxford Guide to Library Research offers a rich, inclusive overview of the information field, one that can save researchers countless hours of frustration in the search for the best sources on their topics.

    This new edition has been rewritten and expanded to stay current with database system trends.

    You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples--ready for immediate application to your next graph or presentation.Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data--Storytelling with Data will give you the skills and power to tell it!

    The phenomenon he describes as social production is reshaping markets, while at the same time offering new opportunities to enhance individual freedom, cultural diversity, political discourse, and justice. But these results are by no means inevitable: a systematic campaign to protect the entrenched industrial information economy of the last century threatens the promise of today’s emerging networked information environment.In this comprehensive social theory of the Internet and the networked information economy, Benkler describes how patterns of information, knowledge, and cultural production are changing—and shows that the way information and knowledge are made available can either limit or enlarge the ways people can create and express themselves. He describes the range of legal and policy choices that confront us and maintains that there is much to be gained—or lost—by the decisions we make today.

    Although dashboards are potentially powerful, this potential is rarely realized. The greatest display technology in the world won't solve this if you fail to use effective visual design. And if a dashboard fails to tell you precisely what you need to know in an instant, you'll never use it, even if it's filled with cute gauges, meters, and traffic lights. Don't let your investment in dashboard technology go to waste.This book will teach you the visual design skills you need to create dashboards that communicate clearly, rapidly, and compellingly. Information Dashboard Design will explain how to:Avoid the thirteen mistakes common to dashboard design Provide viewers with the information they need quickly and clearly Apply what we now know about visual perception to the visual presentation of information Minimize distractions, cliches, and unnecessary embellishments that create confusion Organize business information to support meaning and usability Create an aesthetically pleasing viewing experience Maintain consistency of design to provide accurate interpretation Optimize the power of dashboard technology by pairing it with visual effectiveness Stephen Few has over 20 years of experience as an IT innovator, consultant, and educator. As Principal of the consultancy Perceptual Edge, Stephen focuses on data visualization for analyzing and communicating quantitative business information. He provides consulting and training services, speaks frequently at conferences, and teaches in the MBA program at the University of California in Berkeley. He is also the author of Show Me the Numbers: Designing Tables and Graphs to Enlighten. Visit his website at www.perceptualedge.com.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    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.

    By 1974, the British computer industry was all but extinct. What happened in the intervening thirty years holds lessons for all postindustrial superpowers. As Britain struggled to use technology to retain its global power, the nation's inability to manage its technical labor force hobbled its transition into the information age.In Programmed Inequality, Marie Hicks explores the story of labor feminization and gendered technocracy that undercut British efforts to computerize. That failure sprang from the government's systematic neglect of its largest trained technical workforce simply because they were women. Women were a hidden engine of growth in high technology from World War II to the 1960s. As computing experienced a gender flip, becoming male-identified in the 1960s and 1970s, labor problems grew into structural ones and gender discrimination caused the nation's largest computer user -- the civil service and sprawling public sector -- to make decisions that were disastrous for the British computer industry and the nation as a whole.Drawing on recently opened government files, personal interviews, and the archives of major British computer companies, Programmed Inequality takes aim at the fiction of technological meritocracy. Hicks explains why, even today, possessing technical skill is not enough to ensure that women will rise to the top in science and technology fields. Programmed Inequality shows how the disappearance of women from the field had grave macroeconomic consequences for Britain, and why the United States risks repeating those errors in the twenty-first century."