The book's approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. The author stresses analysis of data, examines real problems with real data, and motivates the theory. The book's descriptive statistics, graphical displays, and realistic applications stand in strong contrast to traditional texts which are set in abstract settings.

    It’s easy and fun with this practical, hands-on introduction. Author Scott Murray teaches you the fundamental concepts and methods of D3, a JavaScript library that lets you express data visually in a web browser. Along the way, you’ll expand your web programming skills, using tools such as HTML and JavaScript.This step-by-step guide is ideal whether you’re a designer or visual artist with no programming experience, a reporter exploring the new frontier of data journalism, or anyone who wants to visualize and share data.Learn HTML, CSS, JavaScript, and SVG basicsDynamically generate web page elements from your data—and choose visual encoding rules to style themCreate bar charts, scatter plots, pie charts, stacked bar charts, and force-directed layoutsUse smooth, animated transitions to show changes in your dataIntroduce interactivity to help users explore data through different viewsCreate customized geographic maps with dataExplore hands-on with downloadable code and over 100 examples

    Today, Total Quality is an integral component of management success in today's complex business environment. This text presents an overview of the key principles of total quality and links those concepts to traditional management practices and organizational models in management theory. This book has three objectives: 1) to familiarize readers with the basic principles and methods associated with total quality management; 2) to show readers how these principles and methods have been put into effect in a variety of organizations; and, 3) to illustrate the relationship between total quality principles and the theories and models studied in management courses.

    In Lead Like a PIRATE, education leaders Shelley Burgess and Beth Houf map out the character traits necessary to captain a school or district. You'll learn where to find the treasure that's already in your classrooms and schools--and how to bring out the very best in your educators. What does it take to be a PIRATE Leader? Passion--both professional and personal A willingness to Immerse yourself in your work Good Rapport with your staff, students and community The courage to Ask questions and Analyze what is and isn't working The determination to seek positive Transformation And the kind of Enthusiasm that gets others excited about education The ultimate goal for any education leader is to create schools and districts where students and staff are knocking down the doors to get in rather than out. This book will equip and encourage you to be relentless in your quest to make school amazing for your students, staff, parents, and communities. Are you ready to set sail?

    This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues.The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting general purpose nature of genetic algorithms as search methods that can be employed across disciplines.An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

    The clarity and eloquence of the presentation make it popular with teachers and students alike. The text aims to expand the reader's view of the field and to present standard material in a novel way. All of the most important topics in the field are covered with a fresh perspective, including iterative methods for systems of equations and eigenvalue problems and the underlying principles of conditioning and stability. Presentation is in the form of 40 lectures, which each focus on one or two central ideas. The unity between topics is emphasized throughout, with no risk of getting lost in details and technicalities. The book breaks with tradition by beginning with the QR factorization - an important and fresh idea for students, and the thread that connects most of the algorithms of numerical linear algebra.

    Jenkinson takes readers through Ruth's 1921 season, in which his pattern of battled balls would have accounted for more than 100 home runs in today's ballparks and under today's rules. Yet, 1921 is just tip of the iceberg, for Jenkinson's research reveals that during an era of mammoth field dimensions Ruth hit more 450-plus-feet shots than anybody in history, and the conclusions one can draw are mind boggling.

    Do you dream about increased student engagement and more effective classroom management? How about reducing teacher stress and overwhelm? Or leaving campus at a reasonable hour without dragging a cart full of lesson planning and papers to grade in tow?If the answer to these questions is “Yes!”  then this book is for YOU!Based on current research in positive psychology and more than 15 years “real world” experience in the classroom, this book provides a practical roadmap to reduce stress, improve student behavior and be happier in your classroom and your life.These 10 simple positive mindset habits train you to flex your “happy muscle” and easily: Eliminate teacher overwhelm and stress Leave school every day energized and fulfilled Connect with students in a way that turns every group of kinds into a “dream class” Rediscover the passion and excitement that made you want to become a teacher A quick read in a conversational tone, this book will put a smile back on your face and laughter back in your classroom – two critical elements for teacher fulfillment and student success.*** For a LIMITED TIME your purchase INCLUDES a free download of the 30 page Companion Workbook and a six-week version of the Positive Mindset Journal for Teachers ***

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    With equal parts humanity, insanity, and profanity, Frank Stepnowski, a twenty year veteran of the academic wars, offers unique insight into a world everybody knows about but very few understand. "Step" as he was re-christened by his students, pulls no punches in the classroom, and takes no prisoners in his writing debut. The title, which comes from a line that the author heard many times throughout his career, is both a confession and a confirmation. "I wanted a book," he explains, "that would make people laugh out loud but also open their eyes to just how insane the teaching profession can get. With that in mind, the book is a riotous success, providing searing insight into the classroom and giving an iconoclastic voice to a profession that often goes unheard. Why Are All the Good teachers Crazy? is a wake up call for some, a rallying cry for others, and an invitation to laugh and learn for everyone.Based on actual events, the vivid imagery, colorful characters, and incendiary dialogue of this nuclear powered novel will take readers on a roller coaster ride that they will be talking about long after the ride is over.

    In this book, we'll will walk you through installing, deploying, managing, and extending Docker. We're going to do that by first introducing you to the basics of Docker and its components. Then we'll start to use Docker to build containers and services to perform a variety of tasks. We're going to take you through the development life cycle, from testing to production, and see where Docker fits in and how it can make your life easier. We'll make use of Docker to build test environments for new projects, demonstrate how to integrate Docker with continuous integration workflow, and then how to build application services and platforms. Finally, we'll show you how to use Docker's API and how to extend Docker yourself. We'll teach you how to: * Install Docker. * Take your first steps with a Docker container. * Build Docker images. * Manage and share Docker images. * Run and manage more complex Docker containers. * Deploy Docker containers as part of your testing pipeline. * Build multi-container applications and environments. * Explore the Docker API. * Getting Help and Extending Docker.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    Gregory Mankiw (Harvard University), expertly adapted by Mark P. Taylor (Washington University) so as to be even more relevant to a UK and European audience.A major strength of the original—that the authors present economics from the viewpoint of a reader completely new to the subject has been maintained. The conversational yet precise writing style is superb for presenting the politics and science of economic theories to tomorrow's decision-makers. The book stands out amongst all other principles texts by encouraging students to apply an economic way of thinking in their daily lives. Economics is written to provide students with a robust conceptual understanding of the subject using contemporary approaches to theory where possible.It follows the structure of the original book while reflecting European economic structures and institutions and adapting the language and cultural references for a European readership.

    Robert Gibbons addresses scholars in applied fields within economics who want a serious and thorough discussion of game theory but who may have found other works overly abstract. Gibbons emphasizes the economic applications of the theory at least as much as the pure theory itself; formal arguments about abstract games play a minor role. The applications illustrate the process of model building--of translating an informal description of a multi-person decision situation into a formal game-theoretic problem to be analyzed. Also, the variety of applications shows that similar issues arise in different areas of economics, and that the same game-theoretic tools can be applied in each setting. In order to emphasize the broad potential scope of the theory, conventional applications from industrial organization have been largely replaced by applications from labor, macro, and other applied fields in economics. The book covers four classes of games, and four corresponding notions of equilibrium: static games of complete information and Nash equilibrium, dynamic games of complete information and subgame-perfect Nash equilibrium, static games of incomplete information and Bayesian Nash equilibrium, and dynamic games of incomplete information and perfect Bayesian equilibrium.

    Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal. Features include: The use of boxes to strengthen the narrative: pieces that provide historical context, descriptions of how the algorithms are used in practice, and excursions for the mathematically sophisticated.Carefully chosen advanced topics that can be skipped in a standard one-semester course, but can be covered in an advanced algorithms course or in a more leisurely two-semester sequence.An accessible treatment of linear programming introduces students to one of the greatest achievements in algorithms. An optional chapter on the quantum algorithm for factoring provides a unique peephole into this exciting topic. In addition to the text, DasGupta also offers a Solutions Manual, which is available on the Online Learning Center.Algorithms is an outstanding undergraduate text, equally informed by the historical roots and contemporary applications of its subject. Like a captivating novel, it is a joy to read. Tim Roughgarden Stanford University