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"Dashing dog! Dashing dog! Oh, what a sight to see!" When a dashing dog gets into messy mischief, his family is exasperated. Then baby Betty falls off the jetty, and it's up to the brave dashing dog to save the day. Can this bedraggled puppy prove that looks aren't everything?Award-winning author Margaret Mahy's canine caper will tickle young readers until they sit up and beg for more! Other books by Margaret Mahy are available in this library.
Nancy Wins A Holiday Party Raffle -- but Loses The Prize! Nancy and her Labrador puppy, Chocolate Chip, are enjoying the holiday party at the Dashing Dog salon. They even win the raffle prize -- a one-of-a-kind doggie collar with rhinestones shaped like bones. But then the collar vanishes! Now Nancy is digging up suspects. There's Petra, a girl from her school. She really, really wanted the collar for her dog, Prince Fabian. And what about Alice Cahill, who writes about pets for the local newspaper? She was crazy for the collar, too. Then there's Mrs. Vanderpool and her two yipping Yorkies -- the collar is just their style. To solve this case, Nancy has to be sure she's barking up the right tree!
SOMETHING YOU NEVER EXPECTED TO FIND THE DAY BEFORE CHRISTMAS... In the Santa booth... When Amy Riley's son disappears in the mall, Santa-who is actually police officer Nick DiCaprio-comes to the rescue. Whoever said the Big Red Guy wasn't sexy? In the stores... Retail manager Joy O'Connell is fraying at the edges when rheumatologist Ed Hall and his three child-terrors enter her store. Between shopping hell and bah humbug, can Ed uncover the true Joy of the season? And even on TV...! TV reporter Merry Deluca's "'tis the season to be greedy" story is about to get complicated-her new cameraman is recently ex-fiancé Patrick MacFarland! And he's got a whole new angle for her story....
From beloved mother-daughter duo Mary Higgins Clark, America's Queen of Suspense, and Carol Higgins Clark, author of the hugely popular Regan Reilly mystery series, comes Dashing Through the Snow, a holiday treat you won't want to miss. In the picturesque village of Branscombe, New Hampshire, the townsfolk are all pitching in to prepare for the first (and many hope annual) Festival of Joy. The night before the festival begins, a group of employees at the local market learn that they have won $160 million in the lottery. One of their co-workers, Duncan, decided at the last minute, on the advice of a pair of crooks masquerading as financial advisers, not to play. Then he goes missing. A second winning lottery ticket was purchased in the next town, but the winner hasn't come forward. Could Duncan have secretly bought it? The Clarks' endearing heroes -- Alvirah Meehan, the amateur sleuth, and private investigator Regan Reilly -- have arrived in Branscombe for the festival. They are just the people to find out what is amiss. As they dig beneath the surface, they find that life in Branscombe is not as tranquil as it appears. So much for an old-fashioned weekend in the country. This fast-paced holiday caper will keep you dashing through the pages!
This book explores the many provocative questions concerning the fundamentals of data analysis. It is based on the time-tested experience of one of the gurus of the subject matter. Why should one study data analysis? How should it be taught? What techniques work best, and for whom? How valid are the results? How much data should be tested? Which machine languages should be used, if used at all? Emphasis on apprenticeship (through hands-on case studies) and anecdotes (through real-life applications) are the tools that Peter J. Huber uses in this volume. Concern with specific statistical techniques is not of immediate value; rather, questions of strategy - when to use which technique - are employed. Central to the discussion is an understanding of the significance of massive (or robust) data sets, the implementation of languages, and the use of models. Each is sprinkled with an ample number of examples and case studies. Personal practices, various pitfalls, and existing controversies are presented when applicable. The book serves as an excellent philosophical and historical companion to any present-day text in data analysis, robust statistics, data mining, statistical learning, or computational statistics.
Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like.
Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. Use graphics to describe data with one, two, or dozens of variables Develop conceptual models using back-of-the-envelope calculations, as well as scaling and probability arguments Mine data with computationally intensive methods such as simulation and clustering Make your conclusions understandable through reports, dashboards, and other metrics programs Understand financial calculations, including the time-value of money Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data." --Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists." --Michael E. Driscoll, CEO/Founder, Dataspora
The question of what types of data and evidence can be used is one of the most important topics in linguistics. This book is the first to comprehensively present the methodological problems associated with linguistic data and evidence. Its originality is twofold. First, the authors' approach accounts for a series of unexplained characteristics of linguistic theorising: the uncertainty and diversity of data, the role of evidence in the evaluation of hypotheses, the problem solving strategies as well as the emergence and resolution of inconsistencies. Second, the findings are obtained by the application of a new model of plausible argumentation which is also of relevance from a general argumentation theoretical point of view. All concepts and theses are systematically introduced and illustrated by a number of examples from different linguistic theories, and a detailed case-study section shows how the proposed model can be applied to specific linguistic problems.
The Internet used to be a tool for telling your customers about your business. Now its real value lies in what it tells you about them. Every move your customers make online can be tracked, catalogued, and analyzed to better understand their preferences and predict their future behavior. And with mobile technology like smartphones, customers are online almost every second of every day. The companies that succeed going forward will be those that learn to leverage this torrent of information--without being drowned by it. Balancing examples from giants like Amazon, Home Depot, and Ford with newer players like Rovio, Groupon, and scores of niche-market winners, Data Crush examines the forces behind the explosive growth in data and reveals how the most innovative companies are responding to this challenge. The book clarifies the key drivers: the proliferation of "big data" generated by a never-ending range of online activities (and the mobility that enables much of it); the seemingly infinite array of digital commerce and entertainment pathways; and the rising growth of Cloud computing. These and other factors combine to create an overwhelming universe of valuable information--all constantly updated in real time with billions of mouse clicks each day. It's daunting, but with this onslaught of information comes tremendous opportunity--and Data Crush will help you make sense of it all.
Topics covered in this book are: making sense of variability, making sense of measures of center, comparing distributions: equal numbers of data values and comparing distributions.
Succeeding with data isn't just a matter of putting Hadoop in your machine room, or hiring some physicists with crazy math skills. It requires you to develop a data culture that involves people throughout the organization. In this O'Reilly report, DJ Patil and Hilary Mason outline the steps you need to take if your company is to be truly data-driven--including the questions you should ask and the methods you should adopt.You'll not only learn examples of how Google, LinkedIn, and Facebook use their data, but also how Walmart, UPS, and other organizations took advantage of this resource long before the advent of Big Data. No matter how you approach it, building a data culture is the key to success in the 21st century.You'll explore:Data scientist skills--and why every company needs a SpockHow the benefits of giving company-wide access to data outweigh the costsWhy data-driven organizations use the scientific method to explore and solve data problemsKey questions to help you develop a research-specific process for tackling important issuesWhat to consider when assembling your data teamDeveloping processes to keep your data team (and company) engagedChoosing technologies that are powerful, support teamwork, and easy to use and learn
NAMED BEST MARKETING BOOK OF 2011 BY THE AMERICAN MARKETING ASSOCIATIONHow organizations can deliver significant performance gains through strategic investment in marketingIn the new era of tight marketing budgets, no organization can continue to spend on marketing without knowing what's working and what's wasted. Data-driven marketing improves efficiency and effectiveness of marketing expenditures across the spectrum of marketing activities from branding and awareness, trail and loyalty, to new product launch and Internet marketing. Based on new research from the Kellogg School of Management, this book is a clear and convincing guide to using a more rigorous, data-driven strategic approach to deliver significant performance gains from your marketing.Explains how to use data-driven marketing to deliver return on marketing investment (ROMI) in any organizationIn-depth discussion of the fifteen key metrics every marketer should knowBased on original research from America's leading marketing business school, complemented by experience teaching ROMI to executives at Microsoft, DuPont, Nisan, Philips, Sony and many other firmsUses data from a rigorous survey on strategic marketing performance management of 252 Fortune 1000 firms, capturing $53 billion of annual marketing spendingIn-depth examples of how to apply the principles in small and large organizationsFree downloadable ROMI templates for all examples given in the bookWith every department under the microscope looking for results, those who properly use data to optimize their marketing are going to come out on top every time.
Embrace data and use it to sell and market your productsData is everywhere and it keeps growing and accumulating. Companies need to embrace big data and make it work harder to help them sell and market their products. Successful data analysis can help marketing professionals spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty. Data Driven Marketing For Dummies helps companies use all the data at their disposal to make current customers more satisfied, reach new customers, and sell to their most important customer segments more efficiently.Identifying the common characteristics of customers who buy the same products from your company (or who might be likely to leave you)Tips on using data to predict customer purchasing behavior based on past performanceUsing customer data and marketing analytics to predict when customers will purchase certain itemsInformation on how data collected can help with merchandise planningBreaking down customers into segments for easier market targetingBuilding a 360 degree view of a customer baseData Driven Marketing For Dummies assists marketing professionals at all levels of business in accelerating sales through analytical insights.
Data driven methods have long been used in Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) synthesis and have more recently been introduced for dialogue management, spoken language understanding, and Natural Language Generation. Machine learning is now present "end-to-end" in Spoken Dialogue Systems (SDS). However, these techniques require data collection and annotation campaigns, which can be time-consuming and expensive, as well as dataset expansion by simulation. In this book, we provide an overview of the current state of the field and of recent advances, with a specific focus on adaptivity.
DATA ENGINEERING: Mining, Information, and Intelligence describes applied research aimed at the task of collecting data and distilling useful information from that data. Most of the work presented emanates from research completed through collaborations between Acxiom Corporation and its academic research partners under the aegis of the Acxiom Laboratory for Applied Research (ALAR). Chapters are roughly ordered to follow the logical sequence of the transformation of data from raw input data streams to refined information. Four discrete sections cover Data Integration and Information Quality; Grid Computing; Data Mining; and Visualization. Additionally, there are exercises at the end of each chapter. The primary audience for this book is the broad base of anyone interested in data engineering, whether from academia, market research firms, or business-intelligence companies. The volume is ideally suited for researchers, practitioners, and postgraduate students alike. With its focus on problems arising from industry rather than a basic research perspective, combined with its intelligent organization, extensive references, and subject and author indices, it can serve the academic, research, and industrial audiences.
The world is awash with digital data from social networks, blogs, business, science and engineering. Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data. Through the development of new classes of software, algorithms and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements. This emerging area brings many challenges that are different from traditional high-performance computing. This reference for computing professionals and researchers describes the dimensions of the field, the key challenges, the state of the art and the characteristics of likely approaches that future data-intensive problems will require. Chapters cover general principles and methods for designing such systems and for managing and analyzing the big data sets of today that live in the cloud and describe example applications in bioinformatics and cybersecurity that illustrate these principles in practice.
<p>When you combine the sheer scale and range of digital information now available with a journalist’s "nose for news" and her ability to tell a compelling story, a new world of possibility opens up. With <i>The Data Journalism Handbook</i>, you’ll explore the potential, limits, and applied uses of this new and fascinating field.</p>
Acclaimed data scientist DJ Patil details a new approach to solving problems in Data Jujitsu.Learn how to use a problem's "weight" against itself to: Break down seemingly complex data problems into simplified parts; Use alternative data analysis techniques to examine them; Use human input, such as Mechanical Turk, and design tricks that enlist the help of your users to take short cuts around tough problems; Learn more about the problems before starting on the solutions--and use the findings to solve them, or determine whether the problems are worth solving at all.
A lively, thought-provoking memoir about how one woman "gamed" online dating sites like JDate, OKCupid and eHarmony - and met her eventual husband. After yet another online dating disaster, Amy Webb was about to cancel her JDate membership when an epiphany struck: It wasn't that her standards were too high, as women are often told, but that she wasn't evaluating the right data in suitors' profiles. That night Webb, an award-winning journalist and digital-strategy expert, made a detailed, exhaustive list of what she did and didn't want in a mate. The result: seventy-two requirements ranging from the expected (smart, funny) to the super-specific (likes selected musicals: Chess, Les Misérables. Not Cats. Must not like Cats!). Next she turned to her own profile. In order to craft the most compelling online presentation, she needed to assess the competition--so she signed on to JDate again, this time as a man. Using the same gift for data strategy that made her company the top in its field, she found the key words that were digital man magnets, analyzed photos, and studied the timing of women's messages, then adjusted her (female) profile to make the most of that intel. Then began the deluge--dozens of men wanted to meet her, men who actually met her requirements. Among them: her future husband, now the father of her child. Forty million people date online each year. Most don't find true love. Thanks to Data, a Love Story, their odds just got a whole lot better.
Data Mining introduces in clear and simple ways how to use existing data mining methods to obtain effective solutions for a variety of management and engineering design problems. Data Mining is organised into two parts: the first provides a focused introduction to data mining and the second goes into greater depth on subjects such as customer analysis. It covers almost all managerial activities of a company, including: * supply chain design, * product development, * manufacturing system design, * product quality control, and * preservation of privacy. Incorporating recent developments of data mining that have made it possible to deal with management and engineering design problems with greater efficiency and efficacy, Data Mining presents a number of state-of-the-art topics. It will be an informative source of information for researchers, but will also be a useful reference work for industrial and managerial practitioners.
The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike.
Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents:* A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools* Illustrations of how to use the outlined concepts in real-world situations* Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials* Numerous exercises to help readers with computing skills and deepen their understanding of the materialData Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.
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