A fresh look at visualization from the author of Visualize This Whether it's statistical charts, geographic maps, or the snappy graphical statistics you see on your favorite news sites, the art of data graphics or visualization is fast becoming a movement of its own. In Data Points: Visualization That Means Something, author Nathan Yau presents an intriguing complement to his bestseller Visualize This, this time focusing on the graphics side of data analysis. Using examples from art, design, business, statistics, cartography, and online media, he explores both standard-and not so standard-concepts and ideas about illustrating data. Shares intriguing ideas from Nathan Yau, author of Visualize This and creator of flowingdata.com, with over 66,000 subscribers Focuses on visualization, data graphics that help viewers see trends and patterns they might not otherwise see in a table Includes examples from the author's own illustrations, as well as from professionals in statistics, art, design, business, computer science, cartography, and more Examines standard rules across all visualization applications, then explores when and where you can break those rules Create visualizations that register at all levels, with Data Points: Visualization That Means Something.

Forget Apple and IBM. For that matter forget Silicon Valley. The first personal computer, a self-contained unit with its own programmable processor, display, keyboard, internal memory, telephone interface, and mass storage of data was born in San Antonio TX. US Patent number 224,415 was filed November 27, 1970 for a machine that is the direct lineal ancestor to the PC as we know it today. The story begins in 1968, when two Texans, Phil Ray and Gus Roche, founded a firm called Computer Terminal Corporation. As the name implies their first product was a Datapoint 3300 computer terminal replacement for a mechanical Teletype. However, they knew all the while that the 3300 was only a way to get started, and it was cover for what their real intentions were - to create a programmable mass-produced desktop computer. They brought in Jack Frassanito, Vic Poor, Jonathan Schmidt, Harry Pyle and a team of designers, engineers and programmers to create the Datapoint 2200. In an attempt to reduce the size and power requirement of the computer it became apparent that the 2200 processor could be printed on a silicon chip. Datapoint approached Intel who rejected the concept as a "dumb idea" but were willing to try for a development contract. Intel belatedly came back with their chip but by then the Datapoint 2200 was already in production. Intel added the chip to its catalog designating it the 8008. A later upgrade, the 8080 formed the heart of the Altair and IMSI in the mid-seventies. With further development it was used in the first IBM PC-the PC revolution's chip dynasty. If you're using a PC, you're using a modernized Datapoint 2000.

- Based on the best-selling book, Basic Skills in Interpreting Laboratory Data, 4th Edition, by Mary Lee - The Quick View charts from Basic Skills in Interpreting Laboratory Data have been updated and revised to create a quick reference for the Point-of-Care series. - It provides concise information about pertinent laboratory values and their corresponding assays. - There are 100 new charts, 157 in total.

Today, a hurricane is forming, and businesses are headed straight into it. This hurricane is arising from the convergence of several enormous trends in information technology, including cloud, mobility, Software as a Service, and Big Data. In Inflection Point, Scott Stawski shows how to harness these fierce winds of change, put them at your back, and sail towards greater competitiveness and customer value. Stawski explains the strategic implications of today’s new technology paradigms, helping you reshape strategy to embrace and profit from them. You’ll discover how technology and other factors are driving a radical new round of disintermediation, reintermediation, and disruption–and what that means to you and your company. Stawski shows how to go beyond inadequate incremental improvements, dramatically reducing IT spend and virtually eliminating IT capital expenditures. One meaningful step at a time, you’ll learn how to transform Operational IT into both a utility and a true business enabler, bringing new speed, flexibility, and focus to what really matters: your true core competencies. BUILD A CONTINUAL TRANSFORMATION ENVIRONMENT THAT’S READY FOR ANYTHING Focus on high-value core competencies, not fixed assets or unchanging processes CONSUME IT THE WAY YOU CONSUME ELECTRICITY Stop running data centers, buying software, and managing applications–forever! LEVERAGE THE CLOUD’S SIMPLICITY WITHOUT LOSING CONTROL OR SECURITY Use standards and governance to maximize cloud benefits with minimal risk HARNESS THE PRECIOUS ASSET YOU MUST ALWAYS KEEP CLOSE: DATA Build data-centric operations to deliver the right knowledge, right now, wherever it’s needed

This book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k-nearest neighbors’ setting. The respective chapters highlight the latest developments in k-nearest neighbor-based outlier detection research and cover such topics as our present understanding of unsupervised outlier detection in general; distance-based and density-based outlier detection in particular; and the applications of the latest findings to boundary point detection and novel object detection. The book also offers a new perspective on bridging the gap between k-nearest neighbor-based outlier detection and clustering-based outlier detection, laying the groundwork for future advances in unsupervised outlier detection research. The authors hope the algorithms and applications proposed here will serve as valuable resources for outlier detection researchers for years to come.

Summary Imagine predicting which customers are thinking about switching to a competitor or flagging potential process failures before they happen Think about the benefits of forecasting tedious business processes and back-office tasks Envision quickly gauging customer sentiment from social media content (even large volumes of it). Consider the competitive advantage of making decisions when you know the most likely future events Machine learning can deliver these and other advantages to your business, and it’s never been easier to get started! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Machine learning can deliver huge benefits for everyday business tasks. With some guidance, you can get those big wins yourself without complex math or highly paid consultants! If you can crunch numbers in Excel, you can use modern ML services to efficiently direct marketing dollars, identify and keep your best customers, and optimize back office processes. This book shows you how. About the book Machine Learning for Business teaches business-oriented machine learning techniques you can do yourself. Concentrating on practical topics like customer retention, forecasting, and back office processes, you’ll work through six projects that help you form an ML-for-business mindset. To guarantee your success, you’ll use the Amazon SageMaker ML service, which makes it a snap to turn your questions into results. What's inside Identifying tasks suited to machine learning Automating back office processes Using open source and cloud-based tools Relevant case studies About the reader For technically inclined business professionals or business application developers. About the author Doug Hudgeon and Richard Nichol specialize in maximizing the value of business data through AI and machine learning for companies of any size. Table of Contents: PART 1 MACHINE LEARNING FOR BUSINESS 1 ¦ How machine learning applies to your business PART 2 SIX SCENARIOS: MACHINE LEARNING FOR BUSINESS 2 ¦ Should you send a purchase order to a technical approver? 3 ¦ Should you call a customer because they are at risk of churning? 4 ¦ Should an incident be escalated to your support team? 5 ¦ Should you question an invoice sent by a supplier? 6 ¦ Forecasting your company’s monthly power usage 7 ¦ Improving your company’s monthly power usage forecast PART 3 MOVING MACHINE LEARNING INTO PRODUCTION 8 ¦ Serving predictions over the web 9 ¦ Case studies

This two-volume set on Mathematical Principles of the Internet provides a comprehensive overview of the mathematical principles of Internet engineering. The books do not aim to provide all of the mathematical foundations upon which the Internet is based. Instead, these cover only a partial panorama and the key principles. Volume 1 explores Internet engineering, while the supporting mathematics is covered in Volume 2. The chapters on mathematics complement those on the engineering episodes, and an effort has been made to make this work succinct, yet self-contained. Elements of information theory, algebraic coding theory, cryptography, Internet traffic, dynamics and control of Internet congestion, and queueing theory are discussed. In addition, stochastic networks, graph-theoretic algorithms, application of game theory to the Internet, Internet economics, data mining and knowledge discovery, and quantum computation, communication, and cryptography are also discussed. In order to study the structure and function of the Internet, only a basic knowledge of number theory, abstract algebra, matrices and determinants, graph theory, geometry, analysis, optimization theory, probability theory, and stochastic processes, is required. These mathematical disciplines are defined and developed in the books to the extent that is needed to develop and justify their application to Internet engineering.