The book provides a unique contribution to the literature in this field in that the studies of decision theory and data-based decision making are... integrated. Focusing on educators assuming leadership roles in school improvement, the book’s content is equally relevant for administrators, supervisors, and teachers. The book, however, is centered on data-driven decision making, both as a requirement of the No Child Left Behind Act and as a normative professional standard. Issues related to accumulating, storing, and protecting data in districts and schools also are covered. Applications in administration, supervision, and teaching are demonstrated.

This timely book offers school leaders a practical toolkit for structuring and facilitating collaborative inquiry with and about data. This resource... presents a three-phase model that supports groups in discovering assumptions, promotes data-focused investigations and develops shared understandings of both problems and possible solutions. Readers will increase their confidence and skill in facilitating data-driven dialogue by applying these easy-to-follow directions for tools and techniques.

Step-by-step guidance and a generous collection of templates, rubrics, and planners make this an essential resource for every instructional leader who... wants to design differentiated instruction with student success in mind.

With real-world examples from actual schools, this book shows you how to nurture a culture of continuous improvement, meet the needs of individual... students, foster an environment of high expectations, and meet the requirements of NCLB.

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.

Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy... to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making.In Data Driven, Thomas Redman, the "Data Doc," shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: The special properties that make data such a powerful asset The hidden costs of flawed, outdated, or otherwise poor-quality dataHow to improve data quality for competitive advantageStrategies for exploiting your data to make better business decisionsThe many ways to bring data to marketIdeas for dealing with political struggles over data and concerns about privacy rightsYour company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that.

This timely reference guide is specifically directed toward the needs of second language researchers, who can expect to gain a clearer understanding of... which techniques may be most appropriate and fruitful in given research domains. Data Elicitation for Second and Foreign Language Research is a perfect companion to the same author team’s bestselling Second Language Research: Methodology and Design. It is an indispensable text for graduate or advanced-level undergraduate students who are beginning research projects in the fields of applied linguistics, second language acquisition, and TESOL as well as a comprehensive reference for more seasoned researchers.

This volume systematically details both the basic principles and new developments in Data Envelopment Analysis (DEA), offering a solid understanding of... the methodology, its uses, and its potential. New material in this edition includes coverage of recent developments that have greatly extended the power and scope of DEA and have lead to new directions for research and DEA uses. Each chapter accompanies its developments with simple numerical examples and discussions of actual applications. The first nine chapters cover the basic principles of DEA, while the final seven chapters provide a more advanced treatment.

Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new... opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks

Wanted: Expert Data Modeling and SQL Skills... Inquire Within.Data modeling and SQL--these are the data management skills that are in demand in today's... job market. That's why Richard Watson's Fifth Edition of Data Management: Databases and Organizations offers in-depth, fully integrated coverage of data modeling and SQL, and a broad managerial perspective.Updated with the latest developments in the field, the Fifth Edition will help you design and create relational databases, formulate complex SQL queries, understand OLAP, use SQL with Java, learn how to use XML, and prepare yourself for the real world of data management.New Features of the Fifth Edition:* A new chapter on embedded SQL in Java and JDBC* A section on multidimensional expressions (MDX)* New material on content management systems (CMS) and wiki technology* Greater focus on MySQL* Increased coverage of mandatory and optional elements in data modeling

Using simple language and illustrative examples, this book comprehensively covers data management tasks that bridge the gap between raw data and... statistical analysis. Rather than focus on clusters of commands, the author takes a modular approach that enables readers to quickly identify and implement the necessary task without having to access background information first. Each section in the chapters presents a self-contained lesson that illustrates a particular data management task via examples, such as creating data variables and automating error checking. The text also discusses common pitfalls and how to avoid them and provides strategic data management advice. Ideal for both beginning statisticians and experienced users, this handy book helps readers solve problems and learn comprehensive data management skills.

This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data. In addition to the built-in... functions, a number of readily available packages from CRAN (the Comprehensive R Archive Network) are also covered. All of the methods presented take advantage of the core features of R: vectorization, efficient use of subscripting, and the proper use of the varied functions in R that are provided for common data management tasks. Most experienced R users discover that, especially when working with large data sets, it may be helpful to use other programs, notably databases, in conjunction with R. Accordingly, the use of databases in R is covered in detail, along with methods for extracting data from spreadsheets and datasets created by other programs. Character manipulation, while sometimes overlooked within R, is also covered in detail, allowing problems that are traditionally solved by scripting languages to be carried out entirely within R. For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided. Since many statistical modeling and graphics functions need their data presented in a data frame, techniques for converting the output of commonly used functions to data frames are provided throughout the book.

Data Mining and Business Intelligence: A Guide to Productivity provides an overview of data mining technology and how it is applied in a business... environment. It describes the corresponding data mining methodologies that are used to solve a variety of business problems, which enhance firm-level efficiency in a less technical, more managerial style. The book incorporates the data mining process into the spectrum of complementary technologies that together comprise corporate information systems that promote business intelligence. Business intelligence involves the proliferation of value-added information throughout a given enterprise through the use of various applications that promotes efficiency for the firm.

With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity.... Although several conferences, workshops, and journals focus on the fragmented research topics in this area, there has been no single interdisciplinary resource on past and current works and possible paths for future research in this area. This book fills this need. From basic concepts in machine learning and data mining to advanced problems in the machine learning domain, Data Mining and Machine Learning in Cybersecurity provides a unified reference for specific machine learning solutions to cybersecurity problems. It supplies a foundation in cybersecurity fundamentals and surveys contemporary challenges—detailing cutting-edge machine learning and data mining techniques. It also: Unveils cutting-edge techniques for detecting new attacks Contains in-depth discussions of machine learning solutions to detection problems Categorizes methods for detecting, scanning, and profiling intrusions and anomalies Surveys contemporary cybersecurity problems and unveils state-of-the-art machine learning and data mining solutions Details privacy-preserving data mining methods This interdisciplinary resource includes technique review tables that allow for speedy access to common cybersecurity problems and associated data mining methods. Numerous illustrative figures help readers visualize the workflow of complex techniques and more than forty case studies provide a clear understanding of the design and application of data mining and machine learning techniques in cybersecurity.