Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject.
This book constitutes the refereed proceedings of the 7th International Conference on Intelligent Data Analysis, IDA 2007, held in Ljubljana, Slovenia. The 33 revised papers were carefully reviewed and selected from almost 100 submissions. The book covers all current aspects of this interdisciplinary field, including statistics, machine learning, data mining, classification and pattern recognition, clustering, applications, modeling, and interactive dynamic data visualization.
This book constitutes the refereed proceedings of the Second International Symposium on Computational Life Sciences, CompLife 2006, held in Cambridge, UK, in September 2006.The 25 revised full papers presented were carefully reviewed and selected from 56 initial submissions. The papers are organized in topical sections on genomics, data mining, molecular simulation, molecular informatics, systems biology, biological networks/metabolism, and computational neuroscience.
Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now – at least in principle – solve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of “drowning in information, but starving for knowledge” the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following one’s exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Höppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany.
Pediatric Neuro-Ophthalmology, 3rd edition provides the single authoritative resource on the pathophysiology, diagnostic evaluation, and treatment of neuro-ophthalmologic disorders in children. This book is encyclopedic in scope, incorporating extensive references for each condition, numerous diagrams and pictures, and a detailed analysis of the clinical disorders included in the differential diagnosis of each condition. The third edition builds upon this format to incorporate new discoveries about mechanisms of disease, new diagnostic modalities, advances in treatment in the field of pediatric neuro-ophthalmology, and updated neuroimaging figures.
Succinct yet comprehensive, Aminoff's Diagnosis of Neuromuscular Disorders, 4th Edition, provides expert information and guidance on the clinical, electrodiagnostic, and imaging aspects of neuromuscular disorders. Fully updated, it reflects important advances in the field, including an improved understanding of the molecular and genetic basis of neuromuscular disease, an expanded use of next-generation genetic testing, refinements in electrodiagnosis, and the use of ultrasound imaging techniques to evaluate the peripheral nervous system. In-depth discussions of disease states, as well as the evidence supporting the use of electrodiagnostic and imaging techniques, moves this reference from simply answering the question "How?" to also answering "Why?" - Describes the range of clinical manifestations of individual neuromuscular diseases; the power and limitations of electrodiagnostic techniques as they relate to neuromuscular disorders; the place of genetic studies in the diagnosis and prognostication of these diseases; and the scope and utility of newer imaging procedures in detecting and localizing the underlying pathologic process. - Reviews neuromuscular physiology and the pathology of major diseases. - Provides a readable, well-illustrated synthesis of clinical and investigative techniques in diagnosing neuromuscular diseases, with concise guidance on how to conduct clinical, electrodiagnostic, and ultrasound evaluations and the findings in various disease states. - Covers a wide variety of electrodiagnostic and ultrasound procedures, including techniques for evaluating different individual nerves. - Features video clips of waveforms and of ultrasound images to illustrate key concepts. - An ideal resource for neuromuscular specialists, clinical neurologists, physiatrists, rehabilitation specialists, clinical neurophysiologists, and electromyographers, as well as trainees and those preparing for board certification in neurology, PM&R, neuromuscular medicine, clinical neurophysiology, and electrodiagnostic medicine. - Any additional digital ancillary content may publish up to 6 weeks following the publication date.
Written with contributions from experts who have critically evaluated the state of the art and the prospects of isolated adults cardiac myocytes in cardiological research, this two-volume publication summarizes the current knowledge of this technology. The methodology, structure, and metabolism of cardiomyocytes are presented, along with metabolism and cation homeostasis and energy metabolism and hypoxic injury. Additionally, cell isolation and culture and the structure and biochemistry of the sarcolemma is explained. Electrophysiology and contractile function with ionic currents and membrane channels is discussed. Also, the electro-physiological effects of transmitters and hormones are presented, with excitation-contraction coupling and the use of cardiomyocytes in drug design and research explained.
This book is the definitive record of election results in all states' gubernatorial races from 1932 to 1952 for every candidate who received at least one percent of the total vote. It offers the reader both state and county level voting details of the highest directly elected office in the nation. Virtually all candidates are identified by party affiliation. The returns are presented in two parts. The first section provides an annual summary of gubernatorial votes by year, organized alphabetically by state. The second section provides returns by county for all candidates receiving at least one percent of the state vote. State totals are given for all candidates. Data are based on official election returns.
Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now – at least in principle – solve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of “drowning in information, but starving for knowledge” the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following one’s exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Höppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany.
Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject.
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