Designed for experienced developers ready to advance their status, Exam Ref focuses on the critical-thinking and decision-making acumen needed for success at the Microsoft Specialist level.Focus on the expertise measured by these objectives:Implement and manipulate document structures and objectsImplement program flowAccess and secure dataUse CSS3 in applicationsThis Microsoft Exam Ref:Organizes its coverage by exam objectives.Features strategic, what-if scenarios to challenge you.Includes a 15% exam discount from Microsoft. (Limited time offer)

    Progressive Insurance tracks real-time customer driving patterns and uses that information to offer rates truly commensurate with individual safety. Google accurately predicts local flu outbreaks based upon thousands of user search queries. Amazon provides remarkably insightful, relevant, and timely product recommendations to its hundreds of millions of customers. Quantcast lets companies target precise audiences and key demographics throughout the Web. NASA runs contests via gamification site TopCoder, awarding prizes to those with the most innovative and cost-effective solutions to its problems. Explorys offers penetrating and previously unknown insights into healthcare behavior.How do these organizations and municipalities do it? Technology is certainly a big part, but in each case the answer lies deeper than that. Individuals at these organizations have realized that they don't have to be Nate Silver to reap massive benefits from today's new and emerging types of data. And each of these organizations has embraced Big Data, allowing them to make astute and otherwise impossible observations, actions, and predictions.It's time to start thinking big.In Too Big to Ignore, recognized technology expert and award-winning author Phil Simon explores an unassailably important trend: Big Data, the massive amounts, new types, and multifaceted sources of information streaming at us faster than ever. Never before have we seen data with the volume, velocity, and variety of today. Big Data is no temporary blip of fad. In fact, it is only going to intensify in the coming years, and its ramifications for the future of business are impossible to overstate.Too Big to Ignore explains why Big Data is a big deal. Simon provides commonsense, jargon-free advice for people and organizations looking to understand and leverage Big Data. Rife with case studies, examples, analysis, and quotes from real-world Big Data practitioners, the book is required reading for chief executives, company owners, industry leaders, and business professionals.

    Star Schema: The Complete Reference offers in-depth coverage of design principles and their underlying rationales. Organized around design concepts and illustrated with detailed examples, this is a step-by-step guidebook for beginners and a comprehensive resource for experts.This all-inclusive volume begins with dimensional design fundamentals and shows how they fit into diverse data warehouse architectures, including those of W.H. Inmon and Ralph Kimball. The book progresses through a series of advanced techniques that help you address real-world complexity, maximize performance, and adapt to the requirements of BI and ETL software products. You are furnished with design tasks and deliverables that can be incorporated into any project, regardless of architecture or methodology.Master the fundamentals of star schema design and slow change processingIdentify situations that call for multiple stars or cubesEnsure compatibility across subject areas as your data warehouse growsAccommodate repeating attributes, recursive hierarchies, and poor data qualitySupport conflicting requirements for historic dataHandle variation within a business process and correlation of disparate activitiesBoost performance using derived schemas and aggregatesLearn when it's appropriate to adjust designs for BI and ETL tools

    R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks. Lander's self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You'll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you'll construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time you're done, you won't just know how to write R programs, you'll be ready to tackle the statistical problems you care about most. COVERAGE INCLUDES - Exploring R, RStudio, and R packages - Using R for math: variable types, vectors, calling functions, and more - Exploiting data structures, including data.frames, matrices, and lists - Creating attractive, intuitive statistical graphics - Writing user-defined functions - Controlling program flow with if, ifelse, and complex checks - Improving program efficiency with group manipulations - Combining and reshaping multiple datasets - Manipulating strings using R's facilities and regular expressions - Creating normal, binomial, and Poisson probability distributions - Programming basic statistics: mean, standard deviation, and t-tests - Building linear, generalized linear, and nonlinear models - Assessing the quality of models and variable selection - Preventing overfitting, using the Elastic Net and Bayesian methods - Analyzing univariate and multivariate time series data - Grouping data via K-means and hierarchical clustering - Preparing reports, slideshows, and web pages with knitr - Building reusable R packages with devtools and Rcpp - Getting involved with the R global community

    Chat Software Stock Trading Websites. Career Websites. USB Ports. Download & Speed Limits. Torrents And just about everything else!Who should read this book? College Students. Office Goers. Travelers to countries where websites are blocked (China, UAE, Saudi Arabia and others). Anybody else who wants to unblock stuff on the Internet.About The AuthorAGE 10 - Gifted a computer at home by his parents.AGE 12 - Developed an interest in Computer Hacking.AGE 14 - Published his first book titled The Unofficial Guide to Ethical Hacking which became an instant bestseller worldwide, sold 3 million copies and was translated into 11 languages.AGE 16 - After the Sept. 11th attacks, cracked an encrypted email sent by the Al-Qaeda terrorist network for a classified intelligence agency.AGE 26 - Widely recognized as a Computer Security Expert and Cyber Terrorism guru. Written 14 bestselling books, delivered more than 1000 talks in 25 countries, received 45 awards, has trained more than 20,000 people in India & China, hosts his own TV show called MTV What the Hack!, is writing a script for a movie, runs his own consulting company and also went to Stanford University. His work has touched & influenced the cyber lives of millions of individuals and organizations worldwide.

    according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog "AI Weirdness." She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans--all to understand the technology that governs so much of our daily lives.We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really, and how does it solve problems, understand humans, and even drive self-driving cars?Shane delivers the answers to every AI question you've ever asked, and some you definitely haven't--like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world's best Halloween costume really "Vampire Hog Bride"?In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt--and how they reflect the best and worst of humanity. You Look Like a Thing and I Love You is the perfect book for anyone curious about what the robots in our lives are thinking.

    We wouldn’t have unraveled DNA or discovered Neptune or figured out how to put 5,000 songs in your pocket. Though many of us were scared away from this essential, engrossing subject in high school and college, Steven Strogatz’s brilliantly creative, down‑to‑earth history shows that calculus is not about complexity; it’s about simplicity. It harnesses an unreal number—infinity—to tackle real‑world problems, breaking them down into easier ones and then reassembling the answers into solutions that feel miraculous. Infinite Powers recounts how calculus tantalized and thrilled its inventors, starting with its first glimmers in ancient Greece and bringing us right up to the discovery of gravitational waves (a phenomenon predicted by calculus). Strogatz reveals how this form of math rose to the challenges of each age: how to determine the area of a circle with only sand and a stick; how to explain why Mars goes “backwards” sometimes; how to make electricity with magnets; how to ensure your rocket doesn’t miss the moon; how to turn the tide in the fight against AIDS. As Strogatz proves, calculus is truly the language of the universe. By unveiling the principles of that language, Infinite Powers makes us marvel at the world anew.

    The new edition from Chopra includes many topics encompassing the theory of structural dynamics and the application of this theory regarding earthquake analysis, response, and design of structures. No prior knowledge of structural dynamics is assumed and the manner of presentation is sufficiently detailed and integrated, to make the book suitable for self-study by students and professional engineers.

    This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to - Use the scientific method to evaluate hypotheses using controlled experiments - Define key metrics and ideally an Overall Evaluation Criterion - Test for trustworthiness of the results and alert experimenters to violated assumptions - Build a scalable platform that lowers the marginal cost of experiments close to zero - Avoid pitfalls like carryover effects and Twyman's law - Understand how statistical issues play out in practice.

    How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.

    But what is a “good” shot? Are all good shots created equally? And how might one identify players who are more or less likely to make and prevent those shots in the first place? The concept of basketball “analytics,” for lack of a better term, has been lauded, derided, and misunderstood. The incorporation of more data into NBA decision-making has been credited—or blamed—for everything from the death of the traditional center to the proliferation of three-point shooting to the alleged abandonment of the area of the court known as the midrange. What is beyond doubt is that understanding its methods has never been more important to watching and appreciating the NBA. In The Midrange Theory, Seth Partnow, NBA analyst for The Athletic and former Director of Basketball Research for the Milwaukee Bucks, explains how numbers have affected the modern NBA game, and how those numbers seek not to “solve” the game of basketball but instead urge us toward thinking about it in new ways.The relative value of Russell Westbrook’s triple-doublesWhy some players succeed in the playoffs while others don’tHow NBA teams think about constructing their rosters through the draft and free agencyThe difficulty in measuring defensive achievementThe fallacy of the “quick two”From shot selection to evaluating prospects to considering aesthetics and ethics while analyzing the box scores, Partnow deftly explores where the NBA is now, how it got here, and where it might be going next.

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    In addition to the language skills of listening, speaking, reading, and writing, it covers the basics of English grammer. It also includes key topics such as technical reports, business correspondence, group discussions, interviews, and presentation strategies. With its up-to-date coverage and practical orientation, the book would prove to be an extremely useful text for students, while also serving as a ready reference for day-to-day communication.

    Have you ever opened a Kindle book to find that the font started out way too small or way too large? Have you tried to change to a different font while reading and discovered you couldn't? Have you jumped to a new chapter in a Kindle book and seen that the chapter heading lost its formatting? Has a Kindle completely ignored formatting you knew was in the book? According to Amazon, the simplest way to publish your Kindle book is to upload an HTML file you've saved from Microsoft Word or another app. By itself, that method can bring you maybe 80% of the way to a well-formatted, trouble-free ebook. But what about the other 20%? In this follow-up to his bestselling -From Word to Kindle, - Aaron Shepard takes your saved HTML as a starting point and tells how to quickly tweak and tune it to avoid common problems. Assuming no knowledge of HTML, he introduces the basics of the language, then reveals how to use find-and-replace and macros to touch up an entire book in seconds! If you're serious about Kindle publishing and you're technically inclined -- but not a full-fledged geek -- Aaron provides the tips you need to bring your Kindle book to the next level, making it something truly to be proud of. ///////////////////////////////////////////////// Aaron Shepard is a foremost proponent of the new business of profitable self publishing, which he has practiced and helped develop since 1998. He is the author of -Aiming at Amazon, - -POD for Profit, - -Perfect Pages, - and Amazon's #1 and #2 bestselling paid books on Kindle formatting, -From Word to Kindle- and -Pictures on Kindle.- ///////////////////////////////////////////////// CONTENTS Getting Started 1 WORKING WITH HTML HTML and Kindle HTML Export HTML Editing HTML Processing HTML Basics HTML Checking HTML Cleanup HTML Testing 2 HTML FIXES Fixes for Fonts Fixes for Paragraphs Fixes for Headings Fixes for Line Breaking Fixes for Pictures Fixes for Navigation ///////////////////////////////////////////////// SAMPLE Here are some of the things you can accomplish through changes in HTML. * Adjust bookmarks so headings retain proper formatting when jumped to. * Remove settings that stop the user from choosing their own. * Keep fonts from appearing much too small or much too large when the book is opened. * Make sure indents and other spacing stays relative to larger and smaller font sizes. * Avoid line breaks that leave short words dangling at the ends of lines or paragraphs. * Make up for features lost in translation from your word processor, like nonbreaking hyphens. * Stop -ghost hyphens- from appearing in the middle of words. * Keep pages of text from disappearing for some users. * Prevent the Kindle from applying its own defaults in place of your settings.