Books: 'Big data training'

1

Big learning data. Alexandria, VA: ASTD Press, 2014.

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Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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Nimatulaev, Magomedhan. Information technology in professional activities. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1031122.

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The textbook is intended for studying of discipline "Information technologies in professional activity". Discusses key issues of forming of information society, basic notions and definitions of Informatization of various types and levels of professional activity, the analysis of information systems and technologies to solve economic and management problems. Meets the requirements of Federal state educational standards of higher education of the last generation. It is recommended that students enrolled in the bachelor in the direction of training "Management", as well as postgraduate and graduate students to update knowledge and skills in the application of information systems and technologies in the context of big data Analytics and managerial decision-making.

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Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.

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Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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5

Masie, Elliott. Big Learning Data. American Society for Training & Development, 2013.

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6

Training Students to Extract Value from Big Data. Washington, D.C.: National Academies Press, 2014. http://dx.doi.org/10.17226/18981.

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Training Students to Extract Value from Big Data: Summary of a Workshop. National Academies Press, 2015.

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8

Committee on Applied and Theoretical Statistics, National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, and Maureen Mellody. Training Students to Extract Value from Big Data: Summary of a Workshop. National Academies Press, 2015.

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9

Committee on Applied and Theoretical Statistics, National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, and Maureen Mellody. Training Students to Extract Value from Big Data: Summary of a Workshop. National Academies Press, 2015.

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Committee on Applied and Theoretical Statistics, National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, and Maureen Mellody. Training Students to Extract Value from Big Data: Summary of a Workshop. National Academies Press, 2015.

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11

The Talent Equation Big Data Lessons For Navigating The Skills Gap And Building A Competitive Workforce. McGraw-Hill Education - Europe, 2013.

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12

Chen, Cheng. Reform and Innovation of Training Mode of Accounting Personnel in Vocational Colleges in the Period of Big Data: ??????????????????????? LONGMAN PRESS LTD, 2022.

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13

Flynn, Maurice. Big Mo's Guides : GDPR PREP PLAN - Step 12 : Training and Auditing: Data Privacy and Protection Policies, Processes, Plans and Templates. Independently Published, 2018.

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14

Alger, Bradley E. Defense of the Scientific Hypothesis. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190881481.001.0001.

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This book explains and defends the scientific hypothesis. Explanation is needed to counteract the misinformation and misunderstanding about the hypothesis that even scientists have concerning its nature and place in the tapestry of modern science. A survey revealed that most biological scientists receive little or no formal training in scientific thinking. Defense is needed because the hypothesis is under attack by critics who claim it is irrelevant to science. Defense is important, too, because the hypothesis is perhaps the major element in scientific thinking, and familiarity with it is necessary for an understanding of modern science and scientific thinking. The public needs to understand the hypothesis in order to appreciate and evaluate scientific controversies (e.g., global climate change, vaccine safety, etc.). The first chapters thoroughly describe and analyze in elementary terms the scientific hypothesis and examine various kinds of science. Following chapters that review the hypothesis in the context of the Reproducibility Crisis and present survey data, two chapters assess cognitive matters that affect the hypothesis. In a series of chapters, the book makes practical and policy recommendations for teaching and learning about the hypothesis. The final chapter considers two possible futures for the hypothesis in science as the Big Data revolution looms: in one scenario, the hypothesis is displaced by the Big Data Mindset that forgoes understanding in favor of correlation and prediction. In the other, robotic science incorporates the hypothesis into mechanized laboratories guided by artificial intelligence. An epilogue envisions a third way—the Centaur Scientist, a symbiotic relationship of human scientists and computers.

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15

Das-Munshi, Jayati, Tamsin Ford, Matthew Hotopf, Martin Prince, and Robert Stewart, eds. Practical Psychiatric Epidemiology. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198735564.001.0001.

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This long-awaited second edition of Practical Psychiatric Epidemiology covers all of the considerable new developments in psychiatric epidemiology that have occurred since the first edition was published in 2003. It includes new content on key topics such as life course epidemiology, gene–environment interactions, bioethics, patient and public involvement in research, mixed methods research, new statistical methods, case registers, policy, and implementation. Looking to the future of this rapidly evolving scientific discipline and how it will respond to the emerging opportunities and challenges posed by ‘big data’, new technologies, open science, and globalization, this new edition will serve as an invaluable reference for clinicians in practice and in training. It will also be of interest to researchers in mental health and people studying or teaching psychiatric epidemiology at undergraduate or postgraduate level.

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Anderson, Raymond A. Credit Intelligence & Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.001.0001.

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This book, “Forest Paths” for short, started as a detailed guide for the construction of predictive models for credit and other risk assessment, for use in big-bank retail lending. It became a textbook covering credit processes (from marketing through to fraud), bureau and rating agencies, and various tools. Included are detailed histories (economics, statistics, social science}, which much referencing. It is unique in the field, with chatpers’-end questions. The primary target market is corporate and academic, but much would be of interest to a broader audience. There are eight modules: 1) an introduction to credit risk assessment and predictive modelling; 2) micro-histories of credit, credit intelligence, credit scoring, plus industrial revolutions, economic ups and downs, and both personal registration and identification; 4) mathematical and statistical tools used to develop and assess predictive models; 5) project management and data assembly; 6) data preparation from sampling to reject inference; 7) model training through to implementation; and 8) appendices, including an extensive glossary, bibliography, and index. Although the focus is credit risk, especially in the retail consumer and small-business segments, many concepts are common across disciplines as diverse as psychology, biology, engineering, and computer science, whether academic research or practical use. It also covers issues relating to the use of machine learning for credit risk assessment. Most of the focus is on traditional modelling techniques, but the increasing use of machine learning is recognised, as are its limitations. It is hoped that the contents will inform both camps.

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Misra, Girishwar, ed. Psychology: Volume 4. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780199498871.001.0001.

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This survey of research on psychology in five volumes is a part of a series undertaken by the ICSSR since 1969, which covers various disciplines under social science. Volume Four of this survey, Themes in Contemporary Mental Health Research, deals with mental health, which constitutes one of the fascinating and important areas of psychological applications. With the rise in incidence and complexity of health-related challenges in the contemporary period, there is a growing need to understand the pattern of disorders and ways to improve health and well-being of the people. Psychological research in this area has moved beyond a medical model and has embraced a bio-psycho-social perspective on health, which suggests that the biological mechanisms alone are insufficient to maintain or promote health. Additionally, there is growing evidence that psychological knowledge can contribute substantially to health promotion. The emerging subfield of health psychology encompasses the strategies for health promotion and making preventive health measures more effective. Against this backdrop, the contributors focus on a set of psychological disorders, their treatment, and a critical analysis of the development of the field of health psychology. The six chapters of this volume look at the most recent perspectives in the fields of mental health and psychotherapy in India. They offer up-to-date assessment of the status of practice, training, and research in psychotherapy as well as examine the shift to critical and interpretive approaches in the disciplines of health and community psychology. Additionally, it evaluates some concepts of preventive mental health as applicable to children in India.

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Books on the topic 'Big data training'

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