Training Students To Extract Value From Big Data
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Training Students To Extract Value From Big Data
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Author : National Research Council
language : en
Publisher: National Academies Press
Release Date : 2015-01-16
Training Students To Extract Value From Big Data written by National Research Council and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-16 with Mathematics categories.
As the availability of high-throughput data-collection technologies, such as information-sensing mobile devices, remote sensing, internet log records, and wireless sensor networks has grown, science, engineering, and business have rapidly transitioned from striving to develop information from scant data to a situation in which the challenge is now that the amount of information exceeds a human's ability to examine, let alone absorb, it. Data sets are increasingly complex, and this potentially increases the problems associated with such concerns as missing information and other quality concerns, data heterogeneity, and differing data formats. The nation's ability to make use of data depends heavily on the availability of a workforce that is properly trained and ready to tackle high-need areas. Training students to be capable in exploiting big data requires experience with statistical analysis, machine learning, and computational infrastructure that permits the real problems associated with massive data to be revealed and, ultimately, addressed. Analysis of big data requires cross-disciplinary skills, including the ability to make modeling decisions while balancing trade-offs between optimization and approximation, all while being attentive to useful metrics and system robustness. To develop those skills in students, it is important to identify whom to teach, that is, the educational background, experience, and characteristics of a prospective data-science student; what to teach, that is, the technical and practical content that should be taught to the student; and how to teach, that is, the structure and organization of a data-science program. Training Students to Extract Value from Big Data summarizes a workshop convened in April 2014 by the National Research Council's Committee on Applied and Theoretical Statistics to explore how best to train students to use big data. The workshop explored the need for training and curricula and coursework that should be included. One impetus for the workshop was the current fragmented view of what is meant by analysis of big data, data analytics, or data science. New graduate programs are introduced regularly, and they have their own notions of what is meant by those terms and, most important, of what students need to know to be proficient in data-intensive work. This report provides a variety of perspectives about those elements and about their integration into courses and curricula.
Deep Learning Convergence To Big Data Analytics
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Author : Murad Khan
language : en
Publisher: Springer
Release Date : 2018-12-30
Deep Learning Convergence To Big Data Analytics written by Murad Khan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-30 with Computers categories.
This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.
Big Data Analytics
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Author : Parag Kulkarni
language : en
Publisher: PHI Learning Pvt. Ltd.
Release Date : 2016-07-07
Big Data Analytics written by Parag Kulkarni and has been published by PHI Learning Pvt. Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-07 with Language Arts & Disciplines categories.
The book is an unstructured data mining quest, which takes the reader through different features of unstructured data mining while unfolding the practical facets of Big Data. It emphasizes more on machine learning and mining methods required for processing and decision-making. The text begins with the introduction to the subject and explores the concept of data mining methods and models along with the applications. It then goes into detail on other aspects of Big Data analytics, such as clustering, incremental learning, multi-label association and knowledge representation. The readers are also made familiar with business analytics to create value. The book finally ends with a discussion on the areas where research can be explored.
Critical Thinking For Strategic Intelligence
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Author : Katherine Hibbs Pherson
language : en
Publisher: CQ Press
Release Date : 2016-10-14
Critical Thinking For Strategic Intelligence written by Katherine Hibbs Pherson and has been published by CQ Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-14 with Political Science categories.
The Second Edition of Critical Thinking for Strategic Intelligence provides a basic introduction to the critical thinking skills employed within the intelligence community. This easy-to-use handbook is framed around twenty key questions that all analysts must ask themselves as they prepare to conduct research, generate hypotheses, evaluate sources of information, draft papers, and ultimately present analysis. Drawing upon their decades of teaching and analytic experience, Katherine Hibbs Pherson and Randolph H. Pherson have updated the book with useful graphics that diagram and display the processes and structured analytic techniques used to arrive at the best possible analytical product.
Refining The Concept Of Scientific Inference When Working With Big Data
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Author : National Academies of Sciences, Engineering, and Medicine
language : en
Publisher: National Academies Press
Release Date : 2017-02-24
Refining The Concept Of Scientific Inference When Working With Big Data written by National Academies of Sciences, Engineering, and Medicine and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-24 with Mathematics categories.
The concept of utilizing big data to enable scientific discovery has generated tremendous excitement and investment from both private and public sectors over the past decade, and expectations continue to grow. Using big data analytics to identify complex patterns hidden inside volumes of data that have never been combined could accelerate the rate of scientific discovery and lead to the development of beneficial technologies and products. However, producing actionable scientific knowledge from such large, complex data sets requires statistical models that produce reliable inferences (NRC, 2013). Without careful consideration of the suitability of both available data and the statistical models applied, analysis of big data may result in misleading correlations and false discoveries, which can potentially undermine confidence in scientific research if the results are not reproducible. In June 2016 the National Academies of Sciences, Engineering, and Medicine convened a workshop to examine critical challenges and opportunities in performing scientific inference reliably when working with big data. Participants explored new methodologic developments that hold significant promise and potential research program areas for the future. This publication summarizes the presentations and discussions from the workshop.
Data Science For Undergraduates
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Author : National Academies of Sciences, Engineering, and Medicine
language : en
Publisher: National Academies Press
Release Date : 2018-11-11
Data Science For Undergraduates written by National Academies of Sciences, Engineering, and Medicine and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-11 with Education categories.
Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.
Advances In Brain Inspired Cognitive Systems
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Author : Jinchang Ren
language : en
Publisher: Springer
Release Date : 2018-10-05
Advances In Brain Inspired Cognitive Systems written by Jinchang Ren and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-05 with Computers categories.
This book constitutes the refereed proceedings of the 9th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2018, held in Xi’an, China, in July 2018. The 83 papers presented in this volume were carefully reviewed and selected from 137 submissions. The papers were organized in topical sections named: neural computation; biologically inspired systems; image recognition: detection, tracking and classification; data analysis and natural language processing; and applications.
Machine Learning For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2023-03-08
Machine Learning For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-08 with Computers categories.
MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning —also known as data mining or data analytics— is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using R An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.
Learning And Collaboration Technologies Novel Learning Ecosystems
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Author : Panayiotis Zaphiris
language : en
Publisher: Springer
Release Date : 2017-06-28
Learning And Collaboration Technologies Novel Learning Ecosystems written by Panayiotis Zaphiris and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-28 with Computers categories.
The two-volume set LNCS 10295 and 10296 constitute the refereed proceedings of the 4th International Conference on Learning and Collaboration Technologies, LCT 2017, held as part of the 19th International Conference on Human-Computer Interaction, HCII 2017, in Vancouver, BC, Canada, in July 2017, in conjunction with 15 thematically similar conferences. The 1228 papers presented at the HCII 2017 conferences were carefully reviewed and selected from 4340 submissions. The papers cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The papers included in this volume are organized in the following topical sections: multimodal and natural interaction for learning; learning and teaching ecosystems; e-learning, social media and MOOCs; beyond the classroom; and games and gamification for learning.
Research Anthology On Developing Effective Online Learning Courses
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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2020-12-18
Research Anthology On Developing Effective Online Learning Courses written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-18 with Education categories.
In the current educational environment, there has been a shift towards online learning as a replacement for the traditional in-person classroom experience. With this new environment comes new technologies, benefits, and challenges for providing courses to students through an entirely digital environment. With this shift comes the necessary research on how to utilize these online courses and how to develop effective online educational materials that fit student needs and encourage student learning, motivation, and success. The optimization of these online tools requires a deeper look into curriculum, instructional design, teaching techniques, and new models for student assessment and evaluation. Information on how to create valuable online course content, engaging lesson plans for the digital space, and meaningful student activities online are only a few of many current topics of interest for promoting student achievement through online learning. The Research Anthology on Developing Effective Online Learning Courses provides multiple perspectives on how to develop engaging and effective online learning courses in the wake of the rapid digitalization of education. This book includes topics focused on online learners, online course content, effective online instruction strategies, and instructional design for the online environment. This reference work is ideal for curriculum developers, instructional designers, IT consultants, deans, chairs, teachers, administrators, academicians, researchers, and students interested in the latest research on how to create online learning courses that promote student success.