. ." and "The data bear this out. . . ." But the field of statistics is not just about data. Statistics is the entire process involved in gathering evidence to answer questions about the world, in cases where that evidence happens to be numerical data. Statistics For Dummies is for everyone who wants to sort through and evaluate the incredible amount of statistical information that comes to them on a daily basis. (You know the stuff: charts, graphs, tables, as well as headlines that talk about the results of the latest poll, survey, experiment, or other scientific study.) This book arms you with the ability to decipher and make important decisions about statistical results, being ever aware of the ways in which people can mislead you with statistics. Get the inside scoop on number-crunching nuances, plus insight into how you canDetermine the odds Calculate a standard score Find the margin of error Recognize the impact of polls Establish criteria for a good survey Make informed decisions about experiments This down-to-earth reference is chock-full of real examples from real sources that are relevant to your everyday life: from the latest medical breakthroughs, crime studies, and population trends to surveys on Internet dating, cell phone use, and the worst cars of the millennium. Statistics For Dummies departs from traditional statistics texts, references, supplement books, and study guides in the following ways:Practical and intuitive explanations of statistical concepts, ideas, techniques, formulas, and calculations. Clear and concise step-by-step procedures that intuitively explain how to work through statistics problems. Upfront and honest answers to your questions like, "What does this really mean?" and "When and how I will ever use this?" Chances are, Statistics For Dummies will be your No. 1 resource for discovering how numerical data figures into your corner of the universe.

    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.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems.Understand the basics of the language, including the nature of R objectsLearn how to write R functions and build your own packagesWork with data through visualization, statistical analysis, and other methodsExplore the wealth of packages contributed by the R communityBecome familiar with the lattice graphics package for high-level data visualizationLearn about bioinformatics packages provided by Bioconductor"I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides 'real world' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians."

    Many designers and researchers view usability and design as qualitative activities, which do not require attention to formulas and numbers. However, usability practitioners and user researchers are increasingly expected to quantify the benefits of their efforts. The impact of good and bad designs can be quantified in terms of conversions, completion rates, completion times, perceived satisfaction, recommendations, and sales.The book discusses ways to quantify user research; summarize data and compute margins of error; determine appropriate samples sizes; standardize usability questionnaires; and settle controversies in measurement and statistics. Each chapter concludes with a list of key points and references. Most chapters also include a set of problems and answers that enable readers to test their understanding of the material. This book is a valuable resource for those engaged in measuring the behavior and attitudes of people during their interaction with interfaces.

    It provides a high-productivity programming model that promotes cleaner code architecture, test-driven development, and powerful extensibility, combined with all the benefits of ASP.NET.ASP.NET MVC 4 contains a number of significant advances over previous versions. New mobile and desktop templates (employing adaptive rendering) are included together with support for jQuery Mobile for the first time. New display modes allow your application to select views based on the browser that's making the request while Code Generation Recipes for Visual Studio help you auto-generate project-specific code for a wide variety of situtations including NuGet support.In this fourth edition, the core model-view-controller (MVC) architectural concepts are not simply explained or discussed in isolation, but are demonstrated in action. You'll work through an extended tutorial to create a working e-commerce web application that combines ASP.NET MVC with the latest C# language features and unit-testing best practices. By gaining this invaluable, practical experience, you'll discover MVC's strengths and weaknesses for yourself--and put your best-learned theory into practice.The book's authors, Steve Sanderson and Adam Freeman, have both watched the growth of ASP.NET MVC since its first release. Steve is a well-known blogger on the MVC Framework and a member of the Microsoft Web Platform and Tools team. Adam started designing and building web applications 15 years ago and has been responsible for some of the world's largest and most ambitious projects. You can be sure you are in safe hands.

    You'll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.To get you started--whether you're on Windows, OS X, or Linux--author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools.Discover why the command line is an agile, scalable, and extensible technology. Even if you're already comfortable processing data with, say, Python or R, you'll greatly improve your data science workflow by also leveraging the power of the command line.Obtain data from websites, APIs, databases, and spreadsheetsPerform scrub operations on plain text, CSV, HTML/XML, and JSONExplore data, compute descriptive statistics, and create visualizationsManage your data science workflow using DrakeCreate reusable tools from one-liners and existing Python or R codeParallelize and distribute data-intensive pipelines using GNU ParallelModel data with dimensionality reduction, clustering, regression, and classification algorithms

    In this new Seventh Edition of his perennially successful social research text, author Russell K. Schutt continues to make research come alive through stories that illustrate the methods presented in each chapter, and hands-on exercises that help students learn by doing. Investigating the Social World helps readers understand research methods as an integrated whole, appreciate the value of both qualitative and quantitative methodologies, and understand the need to make ethical research decisions. New to this Edition: * upgraded coverage of research methods to include the spread of cell phones and the use of the Internet, including expanded coverage of Web surveys * larger page size in full color allows for better display of pedagogical features * new 'Research in the News' boxes included within chapters * more international examples * expanded statistics coverage now includes more coverage of inferenctial statistics and regression analysi

    This is complexity. But too many leaders approach complexity the wrong way - they push their people harder and harder and tackle problems one at a time over months, sometimes even years, and nearly always in a linear fashion. It's like setting a pot of water on "low" and waiting for it to boil. To solve the seemingly intractable challenges that leaders bang their heads against for months - to get the metaphorical water to boil - you must generate a high amount of heat very quickly. In this book, the authors share their proven formula for dramatically shortening the process and solving an organization's toughest challenges in mere days.

    No longer. A new generation of tools and massive amounts of available data make it easy for anyone to create visualizations that communicate ideas far more effectively than generic spreadsheet charts ever could.What’s more, building good charts is quickly becoming a need-to-have skill for managers. If you’re not doing it, other managers are, and they’re getting noticed for it and getting credit for contributing to your company’s success.In Good Charts, dataviz maven Scott Berinato provides an essential guide to how visualization works and how to use this new language to impress and persuade. Dataviz today is where spreadsheets and word processors were in the early 1980s—on the cusp of changing how we work. Berinato lays out a system for thinking visually and building better charts through a process of talking, sketching, and prototyping.This book is much more than a set of static rules for making visualizations. It taps into both well-established and cutting-edge research in visual perception and neuroscience, as well as the emerging field of visualization science, to explore why good charts (and bad ones) create “feelings behind our eyes.” Along the way, Berinato also includes many engaging vignettes of dataviz pros, illustrating the ideas in practice.Good Charts will help you turn plain, uninspiring charts that merely present information into smart, effective visualizations that powerfully convey ideas.

    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.

    By scale, we mean having numbers of users in the millions and being built by large engineering teams.For mobile engineers, this book is a blueprint for modern app engineering approaches. For non-mobile engineers and managers, it is a resource with which to build empathy and appreciation for the complexity of world-class mobile engineering.

    Scholars and students need to be able to analyze, design and curate information into useful tools of communication, insight and understanding. This book is the starting point in learning the process and skills of data visualization, teaching the concepts and skills of how to present data and inspiring effective visual design. Benefits of this book: A flexible step-by-step journey that equips you to achieve great data visualization.A curated collection of classic and contemporary examples, giving illustrations of good and bad practice Examples on every page to give creative inspiration Illustrations of good and bad practice show you how to critically evaluate and improve your own work Advice and experience from the best designers in the field Loads of online practical help, checklists, case studies and exercises make this the most comprehensive text available

    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!

    Each idiom comes with a detailed description, example code showing the "wrong" way to do it, and code for the idiomatic, "Pythonic" alternative. *This version of the book is for Python 2.7.3+. There is also a Python 3.3+ version available.* "Writing Idiomatic Python" contains the most common and important Python idioms in a format that maximizes identification and understanding. Each idiom is presented as a recommendation to write some commonly used piece of code. It is followed by an explanation of why the idiom is important. It also contains two code samples: the "Harmful" way to write it and the "Idiomatic" way. * The "Harmful" way helps you identify the idiom in your own code. * The "Idiomatic" way shows you how to easily translate that code into idiomatic Python. This book is perfect for you: * If you're coming to Python from another programming language * If you're learning Python as a first programming language * If you're looking to increase the readability, maintainability, and correctness of your Python code What is "Idiomatic" Python? Every programming language has its own idioms. Programming language idioms are nothing more than the generally accepted way of writing a certain piece of code. Consistently writing idiomatic code has a number of important benefits: * Others can read and understand your code easily * Others can maintain and enhance your code with minimal effort * Your code will contain fewer bugs * Your code will teach others to write correct code without any effort on your part