Book annotation not available for this title.Title: CCNP ROUTE 642-902Author: Odom, WendellPublisher: Pearson P T RPublication Date: 2010/02/09Number of Pages: 730Binding Type: HARDCOVERLibrary of Congress: 2009049908

    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 novel approach taken here banishes determinants to the end of the book and focuses on the central goal of linear algebra: understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space (or an odd-dimensional real vector space) has an eigenvalue. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition includes a new section on orthogonal projections and minimization problems. The sections on self-adjoint operators, normal operators, and the spectral theorem have been rewritten. New examples and new exercises have been added, several proofs have been simplified, and hundreds of minor improvements have been made throughout the text.

    Presentation software is one of the few tools that requires professionals to think visually on an almost daily basis. But unlike verbal skills, effective visual expression is not easy, natural, or actively taught in schools or business training programs. slide:ology fills that void.Written by Nancy Duarte, President and CEO of Duarte Design, the firm that created the presentation for Al Gore's Oscar-winning film, An Inconvenient Truth, this book is full of practical approaches to visual story development that can be applied by anyone. The book combines conceptual thinking and inspirational design, with insightful case studies from the world's leading brands. With slide:ology you'll learn to:Connect with specific audiencesTurn ideas into informative graphicsUse sketching and diagramming techniques effectivelyCreate graphics that enable audiences to process information easilyDevelop truly influential presentationsUtilize presentation technology to your advantageMillions of presentations and billions of slides have been produced -- and most of them miss the mark. slide:ology will challenge your traditional approach to creating slides by teaching you how to be a visual thinker. And it will help your career by creating momentum for your cause.--back cover

    You want to take your web skills to the next level. And you're finally ready to add "programmer" to the resume. It sounds like you're ready to learn the Web's hottest programming language: JavaScript. Head First JavaScript is your ticket to going beyond copying and pasting the code from someone else's web site, and writing your own interactive web pages. With Head First JavaScript, you learn:The basics of programming, from variables to types to looping How the web browser runs your code, and how you can talk to the browser with your code Why you'll never have to worry about casting, overloading, or polymorphism when you're writing JavaScript code How to use the Document Object Model to change your web pages without making your users click buttons If you've ever read a Head First book, you know what to expect -- a visually rich format designed for the way your brain works. Head First JavaScript is no exception. It starts where HTML and CSS leave off, and takes you through your first program into more complex programming concepts -- like working directly with the web browser's object model and writing code that works on all modern browsers. Don't be intimidated if you've never written a line of code before! In typical Head First style, Head First JavaScript doesn't skip steps, and we're not interested in having you cut and paste code. You'll learn JavaScript, understand it, and have a blast along the way. So get ready... dynamic and exciting web pages are just pages away.

    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.

    Now, for the first time, leading agile methodologist Scott Ambler and renowned consultantPramodkumar Sadalage introduce powerful refactoring techniquesspecifically designed for database systems. Ambler and Sadalagedemonstrate how small changes to table structures, data, storedprocedures, and triggers can significantly enhance virtually anydatabase design - without changing semantic

    With this comprehensive guide, Mr. Spreadsheet shows you how to maximize your Excel experience using professional spreadsheet application development tips from his own personal bookshelf.Featuring a complete introduction to Visual Basic for Applications and fully updated for the new features of Excel 2010, this essential reference includes an analysis of Excel application development and is packed with procedures, tips, and ideas for expanding Excel's capabilities with VBA.Offers an analysis of Excel application development and a complete introduction to Visual Basic for Applications (VBA) Features invaluable advice from Mr. Spreadsheet himself (bestselling author John Walkenbach), who demonstrates all the techniques you need to create large and small Excel applications Provides tips, tricks, and techniques for expanding Excel's capabilities with VBA that you won't find anywhere else Includes a CD with templates and worksheets from the book This power-user's guide is packed with procedures, tips, and ideas for expanding Excel's capabilities with VBA. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

    Ideal for processing large datasets, the Apache Hadoop framework is an open source implementation of the MapReduce algorithm on which Google built its empire. This comprehensive resource demonstrates how to use Hadoop to build reliable, scalable, distributed systems: programmers will find details for analyzing large datasets, and administrators will learn how to set up and run Hadoop clusters. Complete with case studies that illustrate how Hadoop solves specific problems, this book helps you:Use the Hadoop Distributed File System (HDFS) for storing large datasets, and run distributed computations over those datasets using MapReduce Become familiar with Hadoop's data and I/O building blocks for compression, data integrity, serialization, and persistence Discover common pitfalls and advanced features for writing real-world MapReduce programs Design, build, and administer a dedicated Hadoop cluster, or run Hadoop in the cloud Use Pig, a high-level query language for large-scale data processing Take advantage of HBase, Hadoop's database for structured and semi-structured data Learn ZooKeeper, a toolkit of coordination primitives for building distributed systems If you have lots of data -- whether it's gigabytes or petabytes -- Hadoop is the perfect solution. Hadoop: The Definitive Guide is the most thorough book available on the subject. "Now you have the opportunity to learn about Hadoop from a master-not only of the technology, but also of common sense and plain talk." -- Doug Cutting, Hadoop Founder, Yahoo!

    

    In Making Numbers Count, Chip Heath argues that it's crucial for us all to be able to interpret and communicate numbers and stats more effectively so that data comes alive. By combining years of research into making ideas stick with a deep understanding of how the brain really works, Heath has discerned six critical principles that will give anyone the tools to communicate numbers with more transparency and meaning. These ideas - including simplicity, concreteness and familiarity - reveal what's compelling about a number and show how to transform it into its most understandable form. And if we can do this when we're using numbers, Heath tells us, then the idea of data won't drive people to panic. We're not hungry for numbers - there's an unfathomable amount of information being generated each year - but we are starved for meaning. The ability to communicate and understand numbers has never mattered more.

    In this one-of-a-kind text, author V. Anton Spraul breaks down the ways that programmers solve problems and teaches you what other introductory books often ignore: how to Think Like a Programmer. Each chapter tackles a single programming concept, like classes, pointers, and recursion, and open-ended exercises throughout challenge you to apply your knowledge. You'll also learn how to:Split problems into discrete components to make them easier to solve Make the most of code reuse with functions, classes, and libraries Pick the perfect data structure for a particular job Master more advanced programming tools like recursion and dynamic memory Organize your thoughts and develop strategies to tackle particular types of problems Although the book's examples are written in C++, the creative problem-solving concepts they illustrate go beyond any particular language; in fact, they often reach outside the realm of computer science. As the most skillful programmers know, writing great code is a creative art—and the first step in creating your masterpiece is learning to Think Like a Programmer.

    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

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    Building these systems is complicated and, because few formally established patterns are available for designing them, most of these systems end up looking very unique. This practical guide shows you how to use existing software design patterns for designing and building reliable distributed applications.Although patterns such as those developed more than 20 years ago by the Gang of Four were largely restricted to running on single machines, author Brendan Burns--a Partner Architect in Microsoft Azure--demonstrates how you can reuse several of them in modern distributed applications.Systems engineers and application developers will learn how these patterns provide a common language and framework for dramatically increasing the quality of your system.