Frontiers in Data Science deals with philosophical and practical results in Data Science. A broad definition of Data Science describes the process of analyzing data to transform data into insights. This also involves asking philosophical, legal and social questions in the context of data generation and analysis. In fact, Big Data also belongs to this universe as it comprises data gathering, data fusion and analysis when it comes to manage big data sets. A major goal of this book is to understand data science as a new scientific discipline rather than the practical aspects of data analysis alone.
Frontiers in Data Science deals with philosophical and practical results in Data Science. A broad definition of Data Science describes the process of analyzing data to transform data into insights. This also involves asking philosophical, legal and social questions in the context of data generation and analysis. In fact, Big Data also belongs to this universe as it comprises data gathering, data fusion and analysis when it comes to manage big data sets. A major goal of this book is to understand data science as a new scientific discipline rather than the practical aspects of data analysis alone.
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
The primary goal of this volume is to present cutting-edge examples of mining large and naturalistic datasets to discover important principles of cognition and to evaluate theories in a way that would not be possible without such scale. It explores techniques that have been underexploited by cognitive psychologists and explains how big data from numerous sources can inform researchers with different research interests and shed further light on how brain, cognition and behavior are interconnected. The book fills a major gap in the literature and has the potential to rapidly advance knowledge throughout the field. It is essential reading for any cognitive psychology researcher.
CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.
The field of computer graphics combines display hardware, software, and interactive techniques in order to display and interact with data generated by applications. Visualization is concerned with exploring data and information graphically in such a way as to gain information from the data and determine significance. Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. Expanding the Frontiers of Visual Analytics and Visualization provides a review of the state of the art in computer graphics, visualization, and visual analytics by researchers and developers who are closely involved in pioneering the latest advances in the field. It is a unique presentation of multi-disciplinary aspects in visualization and visual analytics, architecture and displays, augmented reality, the use of color, user interfaces and cognitive aspects, and technology transfer. It provides readers with insights into the latest developments in areas such as new displays and new display processors, new collaboration technologies, the role of visual, multimedia, and multimodal user interfaces, visual analysis at extreme scale, and adaptive visualization.
The use of standard and reliable measurements is essential in many areas of life, but nowhere is it of more crucial importance than in the world of science, and physics in particular. This book contains 20 contributions presented as part of Course 206 of the International School of Physics Enrico Fermi on New Frontiers for Metrology: From Biology and Chemistry to Quantum and Data Science, held in Varenna, Italy, from 4 -13 July 2019. The Course was the 7th in the Enrico Fermi series devoted to metrology, and followed a milestone in the history of measurement: the adoption of new definitions for the base units of the SI. During the Course, participants reviewed the decision and discussed how the new foundation for metrology is opening new possibilities for physics, with several of the lecturers reflecting on the implications for an easier exploration of the unification of quantum mechanics and gravity. A wide range of other topics were covered, from measuring color and appearance to atomic weights and radiation, and including the application of metrological principles to the management and interpretation of very large sets of scientific data and the application of metrology to biology. The book also contains a selection of posters from the best of those presented by students at the Course. Offering a fascinating exploration of the latest thinking on the subject of metrology, this book will be of interest to researchers and practitioners from many fields.
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
This book constitutes the proceedings of the PAKDD Workshops 2008, namely ALSIP 2008, DMDRM 2008, and IDM 2008. The workshops were held in conjunction with the PAKDD conference in Osaka, Japan, during May 20-23, 2008. The 17 papers presented were carefully reviewed and selected from 38 submissions. The International Workshop on Algorithms for Large-Sale Information Processing in Knowledge Discovery (ALSIP) focused on exchanging fresh ideas on large-scale data processing in the problems of data mining, clustering, machine learning, statistical analysis, and other computational aspects of knowledge discovery problems. The Workshop on Data Mining for Decision Making and Risk Management (DMDRM) covered data mining and machine learning approaches, statistical approaches, chance discovery, active mining and application of these techniques to medicine, marketing, security, decision support in business, social activities, human relationships, chemistry and sensor data. The Workshop on Interactive Data Mining Overview (IDM) discussed various interactive data mining researches such as interactive information retrieval, information gathering sysetms, personalization systems, recommendation systems, user interfaces.
