It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.

    But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In The Model Thinker, social scientist Scott E. Page shows us the mathematical, statistical, and computational models—from linear regression to random walks and far beyond—that can turn anyone into a genius. At the core of the book is Page's "many-model paradigm," which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs. The Model Thinker provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage.

    The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.Create vectors, handle variables, and perform other basic functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your dataWonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language--one practical example at a time.--Jeffrey Ryan, software consultant and R package author

    Properly understood, they can tell us how the teams we root for could employ better strategies, put more effective players on the field, and win more games. The revolution in baseball statistics that began in the 1970s is a controversial subject that professionals and fans alike argue over without end. Despite this fundamental change in the way we watch and understand the sport, no one has written the book that reveals, across every area of strategy and management, how the best practitioners of statistical analysis in baseball-people like Bill James, Billy Beane, and Theo Epstein-think about numbers and the game. Baseball Between the Numbers is that book. In separate chapters covering every aspect of the game, from hitting, pitching, and fielding to roster construction and the scouting and drafting of players, the experts at Baseball Prospectus examine the subtle, hidden aspects of the game, bring them out into the open, and show us how our favorite teams could win more games. This is a book that every fan, every follower of sports radio, every fantasy player, every coach, and every player, at every level, can learn from and enjoy.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    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.

    It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpayd�n offers a concise and accessible overview of the new AI. This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpayd�n, author of a popular textbook on machine learning, explains that as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.

    As a mother of seven, she knows we don't just need to be told what kind of women we should be. We need some help getting there.Drawing from her own experience of balancing marriage, motherhood, and work inside and outside the home, Lisa helps you uncover the key to living a busy life with inner calm. What's the secret? Identifying key priorities and doing first what matters most.With humor and wisdom, Lisa will help you:• Stop striving and rest in God's unconditional love.• Experience new hope in your marriage.• Reach your child's heart.• Create clarity in a cluttered home.• Find friendships that go below the surface and satisfy.• Discover your passion and purpose.Once in a while, things may seem as if they're under control, but we want to walk with purpose regardless of our circumstances. God wants us to daily experience the joy and contentment that comes from knowing we have given our all to what he considers most important. The abundant and purposeful life we were created to live is just around the corner.

    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

    It can be freely downloaded and it works on multiple computer platforms. This book provides an elementary introduction to R. In each chapter, brief introductory sections are followed by code examples and comments from the computational and statistical viewpoint. A supplementary R package containing the datasets can be downloaded from the web.

    'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

    Written by a panel of expert authors, the book covers every aspect of Drupal, from planning a successful project all the way up to making a living from designing Drupal sites and to contributing to the Drupal community yourself. With this book you will:Follow practical approaches to solving many online communication needs with Drupal with real examples. Learn how to keep learning about Drupal: administration, development, theming, design, and architecture. Go beyond the code to engage with the Drupal community as a contributing member and to do Drupal sustainably as a business.The Definitive Guide to Drupal 7 was written by the following team of expert Drupal authors:Benjamin Melançon, Jacine Luisi, Károly Négyesi, Greg Anderson, Bojhan Somers, Stéphane Corlosquet, Stefan Freudenberg, Michelle Lauer, Ed Carlevale, Florian Lorétan, Dani Nordin, Ryan Szrama, Susan Stewart, Jake Strawn, Brian Travis, Dan Hakimzadeh, Amye Scavarda, Albert Albala, Allie Micka, Robert Douglass, Robin Monks, Roy Scholten, Peter Wolanin, Kay VanValkenburgh, Greg Stout, Kasey Qynn Dolin, Mike Gifford, Claudina Sarahe, Sam Boyer, and Forest Mars, with contributions from George Cassie, Mike Ryan, Nathaniel Catchpole, and Dmitri Gaskin.For more information, check out the Drupaleasy podcast #63, in which author Benjamin Melançon discusses The Definitive Guide to Drupal 7 in great detail:http: //drupaleasy.com/podcast/2011/08/drupal...

    The invaluable companion to the new edition of the bestselling How to Measure Anything This companion workbook to the new edition of the insightful and eloquent How to Measure Anything walks readers through sample problems and exercises in which they can master and apply the methods discussed in the book.The book explains practical methods for measuring a variety of intangibles, including approaches to measuring customer satisfaction, organizational flexibility, technology risk, technology ROI, and other problems in business, government, and not-for-profits.Companion to the revision of the bestselling How to Measure AnythingProvides chapter-by-chapter exercises Written by industry leader Douglas Hubbard Written by recognized expert Douglas Hubbard--creator of Applied Information Economics--How to Measure Anything Workbook illustrates how the author has used his approach across various industries and how any problem, no matter how difficult, ill defined, or uncertain can lend itself to measurement using proven methods.

    In this title, the treatment is made easy-to-follow by giving step-by-step derivations, explanations and by avoiding advanced mathematics unfamiliar to students. It covers: math and physics thorough review sections; and worked examples, followed by a practice exercise.

    In chapters that follow, detailed topic reviews cover polynomial, trigonometric, exponential, logarithmic, and rational functions; coordinate and three-dimensional geometry; numbers and operations; data analysis, statistics, and probability; and graphing calculators, their operations and applications. Six full-length model tests with answers, explanations, and self-evaluation charts conclude this manual.