Data Theory

Author : William G. Jacoby
Publisher : SAGE
ISBN 13 : 9780803941786
Total Pages : 100 pages
Book Rating : 4.81/5 ( download)

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Download or read book Data Theory and Dimensional Analysis written by William G. Jacoby and published by SAGE. This book was released on 1991 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many readers, data theory is probably unfamiliar. Data isn't usually the subject matter of theory in and of itself. However, in this volume, William Jacoby introduces a theory of data idea. It examines how real world observations are transformed into something to be analyzed that is, data. Jacoby explores some of the basic ideas of data theory, and considers their implications for research strategies in the social sciences. "Like others in the series, it is reassuringly slim. It is intended for a general social science readership and is a worthwhile read even for experienced data analysts. since it draws attention not only to often overlooked assumptions, but also to often ignored analysis possibilities." --Telephone Surveys "On the whole, this book contains a lot of useful information." --Journal of Classification

Author : Mike X Cohen
Publisher : MIT Press
ISBN 13 : 0262019876
Total Pages : 615 pages
Book Rating : 4.73/5 ( download)

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Download or read book Analyzing Neural Time Series Data written by Mike X Cohen and published by MIT Press. This book was released on 2014-01-17 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.

Author : Steve Y. Oudot
Publisher : American Mathematical Soc.
ISBN 13 : 1470434431
Total Pages : 218 pages
Book Rating : 4.34/5 ( download)

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Download or read book Persistence Theory: From Quiver Representations to Data Analysis written by Steve Y. Oudot and published by American Mathematical Soc.. This book was released on 2017-05-17 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Persistence theory emerged in the early 2000s as a new theory in the area of applied and computational topology. This book provides a broad and modern view of the subject, including its algebraic, topological, and algorithmic aspects. It also elaborates on applications in data analysis. The level of detail of the exposition has been set so as to keep a survey style, while providing sufficient insights into the proofs so the reader can understand the mechanisms at work. The book is organized into three parts. The first part is dedicated to the foundations of persistence and emphasizes its connection to quiver representation theory. The second part focuses on its connection to applications through a few selected topics. The third part provides perspectives for both the theory and its applications. The book can be used as a text for a course on applied topology or data analysis.

Author : Vladimir Cherkassky
Publisher : John Wiley & Sons
ISBN 13 : 9780470140512
Total Pages : 560 pages
Book Rating : 4.18/5 ( download)

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Download or read book Learning from Data written by Vladimir Cherkassky and published by John Wiley & Sons. This book was released on 2007-09-10 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Maria Cristina Mariani
Publisher : John Wiley & Sons
ISBN 13 : 1119674689
Total Pages : 404 pages
Book Rating : 4.89/5 ( download)

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Download or read book Data Science in Theory and Practice written by Maria Cristina Mariani and published by John Wiley & Sons. This book was released on 2021-10-12 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Thorsten Hennig-Thurau
Publisher : Springer
ISBN 13 : 3319892924
Total Pages : 865 pages
Book Rating : 4.24/5 ( download)

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Download or read book Entertainment Science written by Thorsten Hennig-Thurau and published by Springer. This book was released on 2018-08-01 with total page 865 pages. Available in PDF, EPUB and Kindle. Book excerpt: The entertainment industry has long been dominated by legendary screenwriter William Goldman’s “Nobody-Knows-Anything” mantra, which argues that success is the result of managerial intuition and instinct. This book builds the case that combining such intuition with data analytics and rigorous scholarly knowledge provides a source of sustainable competitive advantage – the same recipe for success that is behind the rise of firms such as Netflix and Spotify, but has also fueled Disney’s recent success. Unlocking a large repertoire of scientific studies by business scholars and entertainment economists, the authors identify essential factors, mechanisms, and methods that help a new entertainment product succeed. The book thus offers a timely alternative to “Nobody-Knows” decision-making in the digital era: while coupling a good idea with smart data analytics and entertainment theory cannot guarantee a hit, it systematically and substantially increases the probability of success in the entertainment industry. Entertainment Science is poised to inspire fresh new thinking among managers, students of entertainment, and scholars alike. Thorsten Hennig-Thurau and Mark B. Houston – two of our finest scholars in the area of entertainment marketing – have produced a definitive research-based compendium that cuts across various branches of the arts to explain the phenomena that provide consumption experiences to capture the hearts and minds of audiences. Morris B. Holbrook, W. T. Dillard Professor Emeritus of Marketing, Columbia University Entertainment Science is a must-read for everyone working in the entertainment industry today, where the impact of digital and the use of big data can’t be ignored anymore. Hennig-Thurau and Houston are the scientific frontrunners of knowledge that the industry urgently needs. Michael Kölmel, media entrepreneur and Honorary Professor of Media Economics at University of Leipzig Entertainment Science’s winning combination of creativity, theory, and data analytics offers managers in the creative industries and beyond a novel, compelling, and comprehensive approach to support their decision-making. This ground-breaking book marks the dawn of a new Golden Age of fruitful conversation between entertainment scholars, managers, and artists. Allègre Hadida, Associate Professor in Strategy, University of Cambridge

Author : Tom Johnston
Publisher : Newnes
ISBN 13 : 0124080553
Total Pages : 400 pages
Book Rating : 4.53/5 ( download)

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Download or read book Bitemporal Data written by Tom Johnston and published by Newnes. This book was released on 2014-08-19 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bitemporal data has always been important. But it was not until 2011 that the ISO released a SQL standard that supported it. Currently, among major DBMS vendors, Oracle, IBM and Teradata now provide at least some bitemporal functionality in their flagship products. But to use these products effectively, someone in your IT organization needs to know more than how to code bitemporal SQL statements. Perhaps, in your organization, that person is you. To correctly interpret business requests for temporal data, to correctly specify requirements to your IT development staff, and to correctly design bitemporal databases and applications, someone in your enterprise needs a deep understanding of both the theory and the practice of managing bitemporal data. Someone also needs to understand what the future may bring in the way of additional temporal functionality, so their enterprise can plan for it. Perhaps, in your organization, that person is you. This is the book that will show the do-it-yourself IT professional how to design and build bitemporal databases and how to write bitemporal transactions and queries, and will show those who will direct the use of vendor-provided bitemporal DBMSs exactly what is going on "under the covers" of that software. Explains the business value of bitemporal data in terms of the information that can be provided by bitemporal tables and not by any other form of temporal data, including history tables, version tables, snapshot tables, or slowly-changing dimensions Provides an integrated account of the mathematics, logic, ontology and semantics of relational theory and relational databases, in terms of which current relational theory and practice can be seen as unnecessarily constrained to the management of nontemporal and incompletely temporal data Explains how bitemporal tables can provide the time-variance and nonvolatility hitherto lacking in Inmon historical data warehouses Explains how bitemporal dimensions can replace slowly-changing dimensions in Kimball star schemas, and why they should do so Describes several extensions to the current theory and practice of bitemporal data, including the use of episodes, "whenever" temporal transactions and queries, and future transaction time Points out a basic error in the ISO’s bitemporal SQL standard, and warns practitioners against the use of that faulty functionality. Recommends six extensions to the ISO standard which will increase the business value of bitemporal data Points towards a tritemporal future for bitemporal data, in which an Aristotelian ontology and a speech-act semantics support the direct management of the statements inscribed in the rows of relational tables, and add the ability to track the provenance of database content to existing bitemporal databases This book also provides the background needed to become a business ontologist, and explains why an IT data management person, deeply familiar with corporate databases, is best suited to play that role. Perhaps, in your organization, that person is you

Author : Michele Knobel
Publisher : Myers Education Press
ISBN 13 : 1975502159
Total Pages : 236 pages
Book Rating : 4.57/5 ( download)

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Download or read book Data Analysis, Interpretation, and Theory in Literacy Studies Research written by Michele Knobel and published by Myers Education Press. This book was released on 2020-04-17 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Novice and early career researchers often have difficulty with understanding how theory, data analysis and interpretation of findings “hang together” in a well-designed and theorized qualitative research investigation and with learning how to draw on such understanding to conduct rigorous data analysis and interpretation of their analytic results. Data Analysis, Interpretation, and Theory in Literacy Studies Research demonstrates how to design, conduct and analyze a well put together qualitative research project. Using their own successful studies, chapter authors spell out a problem area, research question, and theoretical framing, carefully explaining their choices and decisions. They then show in detail how they analyzed their data, and why they took this approach. Finally, they demonstrate how they interpreted the results of their analysis, to make them meaningful in research terms. Approaches include interactional sociolinguistics, microethnographic discourse analysis, multimodal analysis, iterative coding, conversation analysis, and multimediated discourse analysis, among others. This book will appeal to beginning researchers and to literacy researchers responsible for teaching qualitative literacy studies research design at undergraduate and graduate levels. Perfect for courses such as: Literacy Research Seminar | Introduction to Qualitative Research | Advanced Research Methods | Studying New Literacies and Media | Research Perspectives in Literacy | Discourse Analysis | Advanced Qualitative Data Analysis | Sociolinguistic Analysis | Classroom Language Research

Author : Olaf Wolkenhauer
Publisher : John Wiley & Sons
ISBN 13 : 0471464104
Total Pages : 296 pages
Book Rating : 4.05/5 ( download)

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Download or read book Data Engineering written by Olaf Wolkenhauer and published by John Wiley & Sons. This book was released on 2004-04-07 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although data engineering is a multi-disciplinary field withapplications in control, decision theory, and the emerging hot areaof bioinformatics, there are no books on the market that make thesubject accessible to non-experts. This book fills the gap in thefield, offering a clear, user-friendly introduction to the maintheoretical and practical tools for analyzing complex systems. Anftp site features the corresponding MATLAB and Mathematical toolsand simulations. Market: Researchers in data management, electrical engineering,computer science, and life sciences.

Author : Qurban A Memon
Publisher : CRC Press
ISBN 13 : 0429554354
Total Pages : 345 pages
Book Rating : 4.53/5 ( download)

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Download or read book Data Science written by Qurban A Memon and published by CRC Press. This book was released on 2019-09-26 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.