Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video.With Beautiful Data, you will: Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web Learn how to visualize trends in urban crime, using maps and data mashups Discover the challenges of designing a data processing system that works within the constraints of space travel Learn how crowdsourcing and transparency have combined to advance the state of drug research Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data Learn about the massive infrastructure required to create, capture, and process DNA data That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include:Nathan Yau Jonathan Follett and Matt Holm J.M. Hughes Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava Jeff Hammerbacher Jason Dykes and Jo Wood Jeff Jonas and Lisa Sokol Jud Valeski Alon Halevy and Jayant Madhavan Aaron Koblin with Valdean Klump Michal Migurski Jeff Heer Coco Krumme Peter Norvig Matt Wood and Ben Blackburne Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen Lukas Biewald and Brendan O'Connor Hadley Wickham, Deborah Swayne, and David Poole Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza Toby Segaran

    I did the basic research for my PhD in about twelve months, then spent two years writing up the results - and producing possibly too much. It succeeded, but I think I might have made a better job of it if I had read a book like this first. But they didn't exist in those days." Mantex This book looks at things the other books don’t tell you about doing a PhD - what it’s really like and how to come through it with a happy ending! It covers all the things you wish someone had told you before you started: What a PhD is really about, and how to do one well The "unwritten rules" of research and of academic writing What your supervisor actually means by terms like "good referencing" and "clean research question" How to write like a skilled researcher How academic careers really work An ideal resource if someone you care about (including yourself!) is undergoing or considering a PhD. This book turns lost, clueless students back into people who know what they are doing, and who can enjoy life again.

    People unconsciously perform ultraordinary actions every day, from throwing a jacket over a chair back to claim the seat, or placing something in the teeth when all hands are full. These "thoughtless acts" reveal the subtle but crucial ways people behave in a world not always perfectly tailored to their needs. Thoughtless Acts? is a collection of dozens of (often humorous) snapshots capturing such fleeting adaptations and minor exploitations. This method of observation demonstrates the kind of common-sense approach that can inspire designers and anyone involved in creative endeavors. Thoughtless Acts? is a privileged peek at how IDEO creates the people-friendly products, services, and spaces for which they are so widely recognized.

    Morgan shows managers how to break free of management fads by understanding the strengths and weaknesses of management metaphors and applying them to organizational life.

    The book demonstrates what sharply rising standards for literacy have meant to successive generations of Americans and how--as students, workers, parents, and citizens--they have responded to rapid changes in the meaning and methods of literacy learning in their society. Drawing on more than 80 life histories of Americans from all walks of life, the book addresses critical questions facing public education at the start of the twenty-first century.

    Your colleague's husband's sister can make you fat, even if you don't know her. A happy neighbor has more impact on your happiness than a happy spouse. These startling revelations of how much we truly influence one another are revealed in the studies of Dr. Christakis and Fowler, which have repeatedly made front-page news nationwide. In Connected, the authors explain why emotions are contagious, how health behaviors spread, why the rich get richer, even how we find and choose our partners. Intriguing and entertaining, Connected overturns the notion of the individual and provides a revolutionary paradigm-that social networks influence our ideas, emotions, health, relationships, behavior, politics, and much more. It will change the way we think about every aspect of our lives.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    It can be used in a variety of disciplines and is ideal for an introductory comprehensive undergraduate or graduate level course. Through its conversational, informal style it makes material that is often challenging for students both accessible and understandable. The Research Methods Knowledge Base, 3e covers everything from the development of a research question to the writing of a final report, describing both practical and technical issues of sampling, measurement, design and analysis.

    Lean Analytics steers you in the right direction.This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without.Understand Lean Startup, analytics fundamentals, and the data-driven mindsetLook at six sample business models and how they map to new ventures of all sizesFind the One Metric That Matters to youLearn how to draw a line in the sand, so you’ll know it’s time to move forwardApply Lean Analytics principles to large enterprises and established products

    Along the way, the reader will find out: * What is a sign?* Which codes do we take for granted?* What is a text?* How can semiotics be used in textual analysis?* Who were Saussure, Peirce, Barthes and Jakobson - and why are they important?Features include a glossary of key terms and realistic suggestions for further reading. There is also a highly-developed and long-established online version of the book at: www.aber.ac.uk/media/Documents/S4B.

    Nine out of ten American high school seniors aspire to go to college, yet the United States has fallen from world leader to only the tenth most educated nation. Almost half of college students don't graduate; those who do have unprecedented levels of federal and private student loan debt, which constitutes a credit bubble similar to the mortgage crisis. The system particularly fails the first-generation, the low-income, and students of color who predominate in coming generations. What we need to know is changing more quickly than ever, and a rising tide of information threatens to swamp knowledge and wisdom. America cannot regain its economic and cultural leadership with an increasingly ignorant population. Our choice is clear: Radically change the way higher education is delivered, or resign ourselves to never having enough of it. The roots of the words "university" and "college" both mean community. In the age of constant connectedness and social media, it's time for the monolithic, millennium-old, ivy-covered walls to undergo a phase change into something much lighter, more permeable, and fluid. The future lies in personal learning networks and paths, learning that blends experiential and digital approaches, and free and open-source educational models. Increasingly, you will decide what, when, where, and with whom you want to learn, and you will learn by doing. The university is the cathedral of modernity and rationality, and with our whole civilization in crisis, we are poised on the brink of Reformation.

    It covers topics ranging from personal responsibility and career development to architectural techniques for keeping your code flexible and easy to adapt and reuse. Read this book, and you'll learn how toFight software rot; Avoid the trap of duplicating knowledge; Write flexible, dynamic, and adaptable code; Avoid programming by coincidence; Bullet-proof your code with contracts, assertions, and exceptions; Capture real requirements; Test ruthlessly and effectively; Delight your users; Build teams of pragmatic programmers; and Make your developments more precise with automation. Written as a series of self-contained sections and filled with entertaining anecdotes, thoughtful examples, and interesting analogies, The Pragmatic Programmer illustrates the best practices and major pitfalls of many different aspects of software development. Whether you're a new coder, an experienced programmer, or a manager responsible for software projects, use these lessons daily, and you'll quickly see improvements in personal productivity, accuracy, and job satisfaction. You'll learn skills and develop habits and attitudes that form the foundation for long-term success in your career. You'll become a Pragmatic Programmer.

    Psychology of Learning for Instruction, Third Edition, focuses on the applications and implications of the learning theories. Using excellent examples ranging from primary school instruction to corporate training, this text combines the latest thinking and research to give readers the opportunity to explore the individual theories as viewed by the experts. Readers are encouraged to apply "reflective practice," which is designed to foster a critical and reflective mode of thinking when considering any particular approach to learning and instruction. Provides readers with the practical knowledge needed to apply learning theories to instruction. KEY TOPICS: This text addresses learning as it relates to behavior, cognition, development, biology, motivation and instruction. MARKET: Pre-service and in-service teachers, and educational psychologists.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    This new and expanded third edition continues to offer students and readers the best one-volume introduction to the field.The bewildering variety of approaches, theorists and technical language is lucidly and expertly unraveled. Unlike many books which assume certain positions about the critics and the theories they represent, Peter Barry allows readers to develop their own ideas once first principles and concepts have been grasped.