This book covers various cutting-edge computing technologies and their applications over data. It discusses in-depth knowledge on big data and cloud computing, quantum computing, cognitive computing, and computational biology with respect to different kinds of data analysis and applications. In this book, authors describe some interesting models in the cloud, quantum, cognitive, and computational biology domains that provide some useful impact on intelligent data (emotional, image, etc.) analysis. They also explain how these computing technologies based data analysis approaches used for various real-life applications. The book will be beneficial for readers working in this area.

Computer-Aided Qualitative Data Analysis offers an invaluable guide to the rapidly developing arena of research practice. It will be essential reading for researchers and graduate students across the social sciences who are engaged in qualitative research.

DATA SCIENCE IN THEORY AND PRACTICE EXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language. Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.

An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.

This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of "third variables" to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions.

This textbook provides an introduction to numerical computing and its applications in science and engineering. The topics covered include those usually found in an introductory course, as well as those that arise in data analysis. This includes optimization and regression-based methods using a singular value decomposition. The emphasis is on problem solving, and there are numerous exercises throughout the text concerning applications in engineering and science. The essential role of the mathematical theory underlying the methods is also considered, both for understanding how the method works, as well as how the error in the computation depends on the method being used. The codes used for most of the computational examples in the text are available on GitHub. This new edition includes material necessary for an upper division course in computational linear algebra.

Cloud Computing: Theory and Practice provides students and IT professionals with an in-depth analysis of the cloud from the ground up. Beginning with a discussion of parallel computing and architectures and distributed systems, the book turns to contemporary cloud infrastructures, how they are being deployed at leading companies such as Amazon, Google and Apple, and how they can be applied in fields such as healthcare, banking and science. The volume also examines how to successfully deploy a cloud application across the enterprise using virtualization, resource management and the right amount of networking support, including content delivery networks and storage area networks. Developers will find a complete introduction to application development provided on a variety of platforms. - Learn about recent trends in cloud computing in critical areas such as: resource management, security, energy consumption, ethics, and complex systems - Get a detailed hands-on set of practical recipes that help simplify the deployment of a cloud based system for practical use of computing clouds along with an in-depth discussion of several projects - Understand the evolution of cloud computing and why the cloud computing paradigm has a better chance to succeed than previous efforts in large-scale distributed computing

In this book, the following three approaches to data analysis are presented: - Test Theory, founded by Sergei V. Yablonskii (1924-1998); the first publications appeared in 1955 and 1958, - Rough Sets, founded by Zdzisław I. Pawlak (1926-2006); the first publications appeared in 1981 and 1982, - Logical Analysis of Data, founded by Peter L. Hammer (1936-2006); the first publications appeared in 1986 and 1988. These three approaches have much in common, but researchers active in one of these areas often have a limited knowledge about the results and methods developed in the other two. On the other hand, each of the approaches shows some originality and we believe that the exchange of knowledge can stimulate further development of each of them. This can lead to new theoretical results and real-life applications and, in particular, new results based on combination of these three data analysis approaches can be expected. - Logical Analysis of Data, founded by Peter L. Hammer (1936-2006); the first publications appeared in 1986 and 1988. These three approaches have much in common, but researchers active in one of these areas often have a limited knowledge about the results and methods developed in the other two. On the other hand, each of the approaches shows some originality and we believe that the exchange of knowledge can stimulate further development of each of them. This can lead to new theoretical results and real-life applications and, in particular, new results based on combination of these three data analysis approaches can be expected. These three approaches have much in common, but researchers active in one of these areas often have a limited knowledge about the results and methods developed in the other two. On the other hand, each of the approaches shows some originality and we believe that the exchange of knowledge can stimulate further development of each of them. This can lead to new theoretical results and real-life applications and, in particular, new results based on combination of these three data analysis approaches can be expected.

For those interested in finding a computer application well-suited for their own qualitative research or just learning more about the capabilities of the latest generation of computer software designed with text, Computer Programs for Qualitative Data Analysis by Eben A. Weitzman and Matthew B. Miles probably represents the single investment they can make. . . . In Computer Programs for Qualitative Data Analysis, Weitzman and Miles . . . provide a critical, in-depth look at 24 separate applications. The authors make an impressive team: Weitzman is a professor of social and organizational psychology with an extensive computer background, and Miles is a social psychologist who is well-known in the field of qualitative research for co-authoring a popular book on qualitative data analysis with A. Michael Huberman. Together, the two researchers have produced an informative, user-friendly sourcebook that can save readers a significant amount of time and money when shopping for a software program for qualitative data analysis. Weitzman and Miles clearly put a tremendous amount of work into Computer Programs for Qualitative Data Analysis; they write their reviews of each application in remarkably lucid and jargon-free language in a format reminiscent of Consumer Reports. The level of detail in the reviews reflects their careful and thoughtful field-testing of 24 software programs. Reviews average about 10 pages each and actually show you what each application can do, and every review includes a series of realistic visuals (complete with helpful captions) that display what the computer screen looks like when performing various functions with that specific computer program. In addition to describing the special features of each computer program, Weitzman and Miles discuss the strengths and weaknesses of every application and make explicit comparisons with other applications in the same ′family.′ "Sage Publications deserves special credit for their role in publishing Computer Programs for Qualitative Data Analysis. Instead of issuing this book in hardcover and attaching a hefty price tag, they released it as an oversized (81/2-by-11-inch) paperback and made this valuable information available at a modest cost. Computer Programs for Qualitative Data Analysis is the most comprehensive resource on its subject currently available, and is an excellent starting point for qualitative researchers interested in integrating computer technology more fully into their own data analysis strategies." --Harvard Educational Review "The book by Weitzman & Miles is one of many new books on computers and qualitative software and indeed a good one. . . . The book is truly a user′s book--one of the useful ones. . . . After this first feeling of self-confidence, I just kept on reading the book and found very thorough and illuminative reviews of no less that 24 computer programs for qualitative analysis. . . . Renata Tesch initiated the work of making it easier for us to survey the qualitative analysis methods--Weitzman & Miles carry on in the finest way." --Nyhedsbrer "Although the authors have a background in social and organizational psychology, their expertise on qualitative research methods is relevant to gerontologists. . . . The authors give a history of the use of computers in qualitative data analysis, describe the different types of programs, and suggest future directions, but the bulk of this book is reviews of the software out there. . . . Before you spend several hundred dollars on a software program, spend thirty and get this book." --T. L. Brink in Clinical Gerontologist "Eben A. Weitzman and Matthew B. Miles′s valuable sourcebook on computer programs is designed exclusively for those interested in qualitative data analysis. . . . For qualitative researchers who want to learn or update their knowledge of the use of computer software." --Choice "What program do I use to analyze my field notes? Eben A. Weitzman and Matthew B. Miles provide the information you need to make that decision intelligently: full descriptions, informed judgments, and helpful comparisons. Anyone who does fieldwork needs this book." --Howard Becker, Department of Sociology, University of Washington, Seattle "This will be the standard work of reference for several years to come. We owe the authors a considerable debt of gratitude for all the work they have put into reviewing such a comprehensive range of software. The result is incredibly lucid." --Paul Atkinson, University of Wales, Cardiff "The book is absolutely perfect for my situation. I don′t know how many readers will be in the position of purchasing software for respective labs, but those who are will be in for a real treat. The comprehensiveness of the reviews is more than adequate to determine whether a particular program meets the needs of an individual or group. . . . It is the most comprehensive book of its kind, for any kind of software, that I have ever seen." --Steven E. Wolfel, Research Publishing Consultant "Making decisions about choosing software for qualitative data analysis can be intimidating and I think this book will be an excellent resource for those of us who are involved in this type of research. I found this to be an extremely well-thought-out and informative resource book. The detail is wonderful." --Kathleen R. Gilbert, Indiana University, Bloomington Do you want to start, extend, or update your use of computer software for qualitative data analysis? If so, this clear and user-friendly guidebook is for you. Without assuming its reader has extensive computer experience, Computer Programs for Qualitative Data Analysis takes a critical yet practical look at the wide range of software currently available. It gives detailed reviews of 24 programs in five major categories (text retrievers, textbase managers, code-and-retrieve programs, code-based theory-builders, and conceptual network-builders) and gives ratings of more than 75 features per program. The authors also provide detailed guidance in operating each program. They help you to ask key questions about your computer use--the nature of your project, time line, analyses planned, and the worksheets required--to help you identify the programs best suited to your needs. Up-to-date and practical, Computer Programs for Qualitative Data Analysis is an absolute must-have book for any qualitative researcher who uses--or wants to use--computer programs in analyses.

The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows: • Part I: Data Science: Theory, Concepts, and Algorithms This part comprises five chapters on data Science theory, concepts, techniques and algorithms. • Part II: Data Design and Analysis This part comprises five chapters on data design and analysis. • Part III: Applications and New Trends in Data Science This part comprises four chapters on applications and new trends in data science.