Providing a solid foundation for twenty-first-century scientists and engineers, Data Analysis and Statistics for Geography, Environmental Science, and Engineering guides readers in learning quantitative methodology, including how to implement data analysis methods using open-source software. Given the importance of interdisciplinary work in sustainability, the book brings together principles of statistics and probability, multivariate analysis, and spatial analysis methods applicable across a variety of science and engineering disciplines. Learn How to Use a Variety of Data Analysis and Statistics Methods Based on the author’s many years of teaching graduate and undergraduate students, this textbook emphasizes hands-on learning. Organized into two parts, it allows greater flexibility using the material in various countries and types of curricula. The first part covers probability, random variables and inferential statistics, applications of regression, time series analysis, and analysis of spatial point patterns. The second part uses matrix algebra to address multidimensional problems. After a review of matrices, it delves into multiple regression, dependent random processes and autoregressive time series, spatial analysis using geostatistics and spatial regression, discriminant analysis, and a variety of multivariate analyses based on eigenvector methods. Build from Fundamental Concepts to Effective Problem Solving Each chapter starts with conceptual and theoretical material to give a firm foundation in how the methods work. Examples and exercises illustrate the applications and demonstrate how to go from concepts to problem solving. Hands-on computer sessions allow students to grasp the practical implications and learn by doing. Throughout, the computer examples and exercises use seeg and RcmdrPlugin.seeg, open-source R packages developed by the author, which help students acquire the skills to implement and conduct analysis and to analyze the results. This self-contained book offers a unified presentation of data analysis methods for more effective problem solving. With clear, easy-to-follow explanations, the book helps students to develop a solid understanding of basic statistical analysis and prepares them for learning the more advanced and specialized methods they will need in their work.

Unique insights to implement big data analytics and reap big returns to your bottom line Focusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data AnalyticsM. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities. Reveals big data analytics as the next wave for businesses looking for competitive advantage Takes an in-depth look at the financial value of big data analytics Offers tools and best practices for working with big data Once the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, Big Data Analytics shows how you can leverage big data into a key component in your business's growth strategy.

Business Intelligence: The Savvy Managers Guide, Second Edition, discusses the objectives and practices for designing and deploying a business intelligence (BI) program. It looks at the basics of a BI program, from the value of information and the mechanics of planning for success to data model infrastructure, data preparation, data analysis, integration, knowledge discovery, and the actual use of discovered knowledge. Organized into 21 chapters, this book begins with an overview of the kind of knowledge that can be exposed and exploited through the use of BI. It then proceeds with a discussion of information use in the context of how value is created within an organization, how BI can improve the ways of doing business, and organizational preparedness for exploiting the results of a BI program. It also looks at some of the critical factors to be taken into account in the planning and execution of a successful BI program. In addition, the reader is introduced to considerations for developing the BI roadmap, the platforms for analysis such as data warehouses, and the concepts of business metadata. Other chapters focus on data preparation and data discovery, the business rules approach, and data mining techniques and predictive analytics. Finally, emerging technologies such as text analytics and sentiment analysis are considered. This book will be valuable to data management and BI professionals, including senior and middle-level managers, Chief Information Officers and Chief Data Officers, senior business executives and business staff members, database or software engineers, and business analysts. Guides managers through developing, administering, or simply understanding business intelligence technology Keeps pace with the changes in best practices, tools, methods and processes used to transform an organization’s data into actionable knowledge Contains a handy, quick-reference to technologies and terminology

Data Insights: New Ways to Visualize and Make Sense of Data offers thought-provoking insights into how visualization can foster a clearer and more comprehensive understanding of data. The book offers perspectives from people with different backgrounds, including data scientists, statisticians, painters, and writers. It argues that all data is useless, or misleading, if we do not know what it means.Organized into seven chapters, the book explores some of the ways that data visualization and other emerging approaches can make data meaningful and therefore useful. It also discusses some fundamental ideas and basic questions in the data lifecycle; the process of interactions between people, data, and displays that lead to better questions and more useful answers; and the fundamentals, origins, and purposes of the basic building blocks that are used in data visualization. The reader is introduced to tried and true approaches to understanding users in the context of user interface design, how communications can get distorted, and how data visualization is related to thinking machines. Finally, the book looks at the future of data visualization by assessing its strengths and weaknesses. Case studies from business analytics, healthcare, network monitoring, security, and games, among others, as well as illustrations, thought-provoking quotes, and real-world examples are included.This book will prove useful to computer professionals, technical marketing professionals, content strategists, Web and product designers, and researchers. Demonstrates, with a variety of case studies, how visualizations can foster a clearer and more comprehensive understanding of data Answers the question, "How can data visualization help me?" with discussions of how it fits into a wide array of purposes and situations Makes the case that data visualization is not just about technology; it also involves a deeply human process

Helping readers master digital imaging, this text presents a unified theoretical basis for understanding and designing methods of imaging and image processing. Designed for newcomers to imaging science and engineering, the book covers the subject in its entirety, from image formation to image perfecting. The author avoids using heavy mathematics and derives all formulas in full detail. To facilitate a deeper understanding of the major results, the book includes a number of exercises supported by MATLAB programs, with the code available at www.crcpress.com.

Open Source BI solutions have many advantages over traditional proprietary software, from offering lower initial costs to more flexible support and integration options; but, until now, there has been no comprehensive guide to the complete offerings of the OS BI market. Writing for IT managers and business analysts without bias toward any BI suite, industry insider Lyndsay Wise covers the benefits and challenges of all available open source BI systems and tools, enabling readers to identify the solutions and technologies that best meet their business needs. Wise compares and contrasts types of OS BI and proprietary tools on the market, including Pentaho, Jaspersoft, RapidMiner, SpagoBI, BIRT, and many more. Real-world case studies and project templates clarify the steps involved in implementing open source BI, saving new users the time and trouble of developing their own solutions from scratch. For business managers who are hard pressed to indentify the best BI solutions and software for their companies, this book provides a practical guide to evaluating the ROI of open source versus traditional BI deployments. The only book to provide complete coverage of all open source BI systems and tools specifically for business managers, without bias toward any OS BI suite A practical, step-by-step guide to implementing OS BI solutions that maximize ROI Comprehensive coverage of all open source systems and tools, including architectures, data integration, support, optimization, data mining, data warehousing, and interoperability Case studies and project templates enable readers to evaluate the benefits and tradeoffs of all OS BI options without having to spend time developing their own solutions from scratch

Maximize the Value of Business Intelligence with IBM Cognos v10 -- Hands-on, from Start to Finish This easy-to-use, hands-on guide brings together all the information and insight you need to drive maximum business value from IBM Cognos v10. Long-time IBM Cognos expert and product designer Sangeeta Gautam thoroughly illuminates Cognos BI v10’s key capabilities: analysis, query, reporting, and dashboards. Gautam shows how to take full advantage of each key IBM Cognos feature, including brand-new innovations such as Active Reports and the new IBM Cognos Workspace report consumption environment. She concludes by walking you through successfully planning and implementing an integrated business intelligence solution using IBM’s best-practice methodologies. The first and only guide of its kind, offers expert insights for BI designers, architects, developers, administrators, project managers, nontechnical end-users, and partners throughout all areas of the business—from sales and marketing to operations and lines of business. If you’re pursuing official IBM Cognos certification, you’ll also find Cognos certification sample questions and information to help you with the certification process. IBM Cognos Business Intelligence v10 Coverage Includes • Understanding IBM Cognos BI’s components and open, extensible architecture • Working with IBM Cognos key “studio” tools: Analysis Studio, Query Studio, Report Studio, and Event Studio • Developing and managing powerful reports that draw on the rich capabilities of IBM Cognos Workspace and Workspace Advanced • Designing Star Schema databases and metadata models to answer the questions your organization cares about most • Efficiently maintaining and systematically securing IBM Cognos BI environments and their objects • Using IBM Cognos Connection as your single point of entry to all corporate data • Building interactive, easy-to-manage Active Reports for casual business users • Using new IBM Cognos BI v10.1 Dynamic Query Mode (DQM) to improve performance with complex heterogeneous data • Identifying, exploring, and exploiting hidden data relationships • Creating quick ad hoc queries that deliver fast answers • Establishing user and administrator roles

In 'QlikView 11 for Developers', you will learn how to leverage QlikView to create robust, insightful business intelligence applications. By following the case study of HighCloud Airlines, this book offers a practical, step-by-step guide to developing and mastering QlikView to address real-world data analysis needs. What this Book will help me do Build comprehensive QlikView dashboards with advanced techniques. Understand how to create associative data models by integrating different data sources. Design interactive user interfaces to enable insightful analysis. Apply best practices in QlikView scripting to transform and prepare data. Master QlikView's built-in functions for calculations and analyses. Author(s) The authors of 'QlikView 11 for Developers' bring extensive experience in business intelligence and QlikView application development. With backgrounds in software engineering and data visualization, they combine technical rigor with practical insights. Their teaching approach ensures concepts are accessible and actionable for a wide range of learners. Who is it for? This book is ideal for software developers and data analysts who want to harness QlikView to build business intelligence applications. It is tailored for those with a basic understanding of business intelligence looking to deepen their expertise in QlikView. The content is also valuable for power users seeking efficiency through advanced features. Readers will gain practical, hands-on knowledge by following this guide.

An essential library of basic commands you can copy and paste into R The powerful and open-source statistical programming language R is rapidly growing in popularity, but it requires that you type in commands at the keyboard rather than use a mouse, so you have to learn the language of R. But there is a shortcut, and that's where this unique book comes in. A companion book to Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, this practical reference is a library of basic R commands that you can copy and paste into R to perform many types of statistical analyses. Whether you're in technology, science, medicine, business, or engineering, you can quickly turn to your topic in this handy book and find the commands you need. Comprehensive command reference for the R programming language and a companion book to Visualize This: The FlowingData Guide to Design, Visualization, and Statistics Combines elements of a dictionary, glossary, and thesaurus for the R language Provides easy accessibility to the commands you need, by topic, which you can cut and paste into R as needed Covers getting, saving, examining, and manipulating data; statistical test and math; and all the things you can do with graphs Also includes a collection of utilities that you'll find useful Simplify the complex statistical R programming language with The Essential R Reference.

Are you new to SciPy and NumPy? Do you want to learn it quickly and easily through examples and a concise introduction? Then this is the book for you. You’ll cut through the complexity of online documentation and discover how easily you can get up to speed with these Python libraries. Ideal for data analysts and scientists in any field, this overview shows you how to use NumPy for numerical processing, including array indexing, math operations, and loading and saving data. You’ll learn how SciPy helps you work with advanced mathematical functions such as optimization, interpolation, integration, clustering, statistics, and other tools that take scientific programming to a whole new level. The new edition is now available, fully revised and updated in June 2013. Learn the capabilities of NumPy arrays, element-by-element operations, and core mathematical operations Solve minimization problems quickly with SciPy’s optimization package Use SciPy functions for interpolation, from simple univariate to complex multivariate cases Apply a variety of SciPy statistical tools such as distributions and functions Learn SciPy’s spatial and cluster analysis classes Save operation time and memory usage with sparse matrices

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 problemsLearn 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.

Health economics is concerned with the study of the cost-effectiveness of health care interventions. This book provides an overview of Bayesian methods for the analysis of health economic data. After an introduction to the basic economic concepts and methods of evaluation, it presents Bayesian statistics using accessible mathematics. The next chapters describe the theory and practice of cost-effectiveness analysis from a statistical viewpoint, and Bayesian computation, notably MCMC. The final chapter presents three detailed case studies covering cost-effectiveness analyses using individual data from clinical trials, evidence synthesis and hierarchical models and Markov models. The text uses WinBUGS and JAGS with datasets and code available online.

With continuous advancements and an increase in user popularity, data mining technologies serve as an invaluable resource for researchers across a wide range of disciplines in the humanities and social sciences. In this comprehensive guide, author and research scientist Kalev Leetaru introduces the approaches, strategies, and methodologies of current data mining techniques, offering insights for new and experienced users alike. Designed as an instructive reference to computer-based analysis approaches, each chapter of this resource explains a set of core concepts and analytical data mining strategies, along with detailed examples and steps relating to current data mining practices. Every technique is considered with regard to context, theory of operation and methodological concerns, and focuses on the capabilities and strengths relating to these technologies. In addressing critical methodologies and approaches to automated analytical techniques, this work provides an essential overview to a broad innovative field.

Statistics is the branch of mathematics that deals with real-life problems. As such, it is an essential tool for economists. Unfortunately, the way you and many other economists learn the concept of statistics is not compatible with the way economists think and learn. The problem is worsened by the use of mathematical jargon and complex derivations. Here’s a book that proves none of this is necessary. All the examples and exercises in this book are constructed within the field of economics, thus eliminating the difficulty of learning statistics with examples from fields that have no relation to business, politics, or policy. Statistics is, in fact, not more difficult than economics. Anyone who can comprehend economics can understand and use statistics successfully within this field, including you! This book utilizes Microsoft Excel to obtain statistical results, as well as to perform additional necessary computations. Microsoft Excel is not the software of choice for performing sophisticated statistical analysis. However, it is widely available, and almost everyone has some degree of familiarity with it. Using Excel will eliminate the need for students and readers to buy and learn new software, the need that itself would prove to be another impediment to learning and using statistics.

Need to learn statistics for your job? Want help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference for anyone new to the subject. Thoroughly revised and expanded, this edition helps you gain a solid understanding of statistics without the numbing complexity of many college texts. Each chapter presents easy-to-follow descriptions, along with graphics, formulas, solved examples, and hands-on exercises. If you want to perform common statistical analyses and learn a wide range of techniques without getting in over your head, this is your book. Learn basic concepts of measurement and probability theory, data management, and research design Discover basic statistical procedures, including correlation, the t-test, the chi-square and Fisher’s exact tests, and techniques for analyzing nonparametric data Learn advanced techniques based on the general linear model, including ANOVA, ANCOVA, multiple linear regression, and logistic regression Use and interpret statistics for business and quality improvement, medical and public health, and education and psychology Communicate with statistics and critique statistical information presented by others

This new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing. The book is comprised of four main parts spanning the field: Optimization Integration and Simulation Bootstrapping Density Estimation and Smoothing Within these sections, each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods. The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data. The book website now includes comprehensive R code for the entire book. There are extensive exercises, real examples, and helpful insights about how to use the methods in practice. Note: The ebook version does not provide access to the companion files.

Data Mining for Bioinformatics enables researchers to meet the challenge of mining vast amounts of biomolecular data to discover real knowledge. Covering theory, algorithms, and methodologies, as well as data mining technologies, it presents a thorough discussion of data-intensive computations used in data mining applied to bioinformatics. The book explains data mining design concepts to build applications and systems. Showing how to prepare raw data for the mining process, the text is filled with heuristics that speed the data mining process.

The step-by-step visual guide to spotting potential price movements and improving returns Bloomberg Visual Guide to Chart Patterns is a concise and accessible visual guide to identifying, understanding, and using chart patterns to predict the direction and extent of price moves. Packed with visual learning enhancements and exercises, this innovative book helps savvy investors and professionals alike master the essential skills of chart pattern recognition. Follow along as chart pattern expert Thomas Bulkowski teaches you to recognize important peaks and valleys that form patterns—footprints of the smart money. Nearly 200 color charts assist in providing a step-by-step approach to finding those footprints, interpreting them, and following them. Popular patterns such as head-and-shoulders, double tops and bottoms, triangles, gaps, flags, and pennants are just a few of the many patterns explored throughout the book. For the sophisticated trader or investor, the book also provides statistical research to support the claims of pattern behavior, trading signals, and setups, in an easy to understand way. Discusses chart pattern identification guidelines, psychology, variations, failures, and buy and sell signals Covers the most popular and common chart patterns as well as lesser-known ones like throwbacks, pullbacks, and busted patterns Incorporates quizzes, step-by-step exercises, enhanced graphics and video tutorials to immerse the reader in the world of chart patterns Designed for use by investors and traders, from beginners to experts looking for a practical, easy-to-use guide, comprehensive reference, Bloomberg Visual Guide to Chart Patterns provides a sophisticated introduction to the world of chart patterns.

Expert guidance for building an information communication and technology infrastructure that provides best in business intelligence Enterprise performance management (EPM) technology has been rapidly advancing, especially in the areas of predictive analysis and cloud-based solutions. Business intelligence caught on as a concept in the business world as the business strategy application of data warehousing in the early 2000s. With the recent surge in interest in data analytics and big data, it has seen a renewed level of interest as the ability of a business to find the valuable data in a timely—and competitive—fashion. Business Intelligence Applied reveals essential information for building an optimal and effective information and communication technology (ICT) infrastructure. Defines ICT infrastructure Examines best practices for documenting business change and for documenting technology recommendations Includes examples and cases from Europe and Asia Written for business intelligence staff, CIOs, CTOs, and technology managers With examples and cases from Europe and Asia, Business Intelligence Applied expertly covers business intelligence, a hot topic in business today as a key element to business and data analytics.

A complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including: Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison Different approaches to calculating asset volatility and various volatility models High-frequency financial data and simple models for price changes, trading intensity, and realized volatility Quantitative methods for risk management, including value at risk and conditional value at risk Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques. An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.