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Big Data and Social Science, Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kre


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Автор: Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kre   (Ян Фостер)
Название:  Big Data and Social Science
Перевод названия: Ян Фостер: Большие данные и социальные науки
ISBN: 9780367568597
Издательство: Taylor&Francis
Классификация:


ISBN-10: 0367568594
Обложка/Формат: Paperback
Страницы: 390
Вес: 0.86 кг.
Дата издания: 16.11.2020
Серия: Chapman & hall/crc statistics in the social and behavioral sciences
Язык: English
Издание: 2 ed
Иллюстрации: 19 tables, black and white; 64 illustrations, color
Размер: 23.11 x 19.05 x 2.29 cm
Читательская аудитория: Tertiary education (us: college)
Подзаголовок: Data science methods and tools for research and practice
Ссылка на Издательство: Link
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Поставляется из: Европейский союз
Описание: This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners.

Statistical Analysis with Missing Data, Third Edit ion

Автор: Little
Название: Statistical Analysis with Missing Data, Third Edit ion
ISBN: 0470526793 ISBN-13(EAN): 9780470526798
Издательство: Wiley
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Цена: 84430.00 T
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Описание: Reflecting new application topics, Statistical Analysis with Missing Data offers a thoroughly up-to-date, reorganized survey of current methodology for handling missing data problems. The third edition reviews historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing values.

Bayesian Data Analysis, Third Edition

Автор: Gelman
Название: Bayesian Data Analysis, Third Edition
ISBN: 1439840954 ISBN-13(EAN): 9781439840955
Издательство: Taylor&Francis
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Цена: 73920.00 T
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Описание: Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Statistics for Big Data for Dummies

Автор: Consumer Dummies, Anderson Alan
Название: Statistics for Big Data for Dummies
ISBN: 1118940016 ISBN-13(EAN): 9781118940013
Издательство: Wiley
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Цена: 17940.00 T
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Описание: Does the subject of data analysis make you dizzy? This book features introduction to exploratory data analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and programming languages. It helps you to identify valid, useful, and understandable patterns in data.

Computational and Statistical Methods for Analysing Big Data with

Автор: Shen Liu
Название: Computational and Statistical Methods for Analysing Big Data with
ISBN: 0128037326 ISBN-13(EAN): 9780128037324
Издательство: Elsevier Science
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Цена: 77470.00 T
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Описание:

Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration.

"Computational and Statistical Methods for Analysing Big Data with Applications" starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data.

Advanced computational and statistical methodologies for analysing big data are developed.

Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable.

Case studies are discussed to demonstrate the implementation of the developed methods.

Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation.

Computing code/programs are provided where appropriate.

Big Data Analytics

Автор: Pyne
Название: Big Data Analytics
ISBN: 8132236262 ISBN-13(EAN): 9788132236269
Издательство: Springer
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Цена: 102480.00 T
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Описание: This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.

Big Data Systems

Автор: Shamsi
Название: Big Data Systems
ISBN: 1498752705 ISBN-13(EAN): 9781498752701
Издательство: Taylor&Francis
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Цена: 112290.00 T
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Описание: Big Data Systems encompass massive challenges related to data diversity, storage mechanisms, and requirements of massive computational power. The book`s purpose is to provide a detailed overview of big data systems. It adopts a challenge-centric approach in which platforms are evaluated based on their capabilities to solve specific challenges.

Emerging Technology and Architecture for Big-data Analytics

Автор: Anupam Chattopadhyay; Chip Hong Chang; Hao Yu
Название: Emerging Technology and Architecture for Big-data Analytics
ISBN: 3319548395 ISBN-13(EAN): 9783319548395
Издательство: Springer
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Цена: 139750.00 T
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Описание: Part I State-of-the-Art Architectures and Automation for Data-analytics.- Chapter 1. Scaling the Java Virtual Machine on a Many-core System.- Chapter 2.Scaling the Java Virtual Machine on a Many-core System.- Chapter 3.Least-squares based Machine Learning Accelerator for Big-data Analytics in Smart Buildings.- Chapter 4.Compute-in-memory Architecture for Data-Intensive Kernels.- Chapter 5. New Solutions for Cross-Layer System-Level and High-Level Synthesis.- Part II New Solutions for Cross-Layer System-Level and High-Level Synthesis.- Chapter 6.Side Channel Attacks and Efficient Countermeasures on Residue Number System Multipliers.- Chapter 7. Ultra-Low-Power Biomedical Circuit Design and Optimization: Catching The Don't Cares.- Chapter 8.Acceleration of MapReduce Framework on a Multicore Processor.- Chapter 9. Adaptive dynamic range compression for improving envelope-based speech perception: Implications for cochlear implants.- Part III Emerging Technology, Circuits and Systems for Data-analytics.- Chapter 10. Emerging Technology, Circuits and Systems for Data-analytics.- Chapter 11. Energy Efficient Spiking Neural Network Design with RRAM Devices.- Chapter 12. Efficient Neuromorphic Systems and Emerging Technologies - Prospects and Perspectives.- Chapter 13. In-memory Data Compression Using ReRAMs.- Chapter 14. In-memory Data Compression Using ReRAMs.- Chapter 15.Data Analytics in Quantum Paradigm - An Introduction.


Operations Research and Big Data

Автор: Ana Paula Ferreira Dias Barbosa P?voa; Joao Luis d
Название: Operations Research and Big Data
ISBN: 3319241524 ISBN-13(EAN): 9783319241524
Издательство: Springer
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Цена: 121890.00 T
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Описание: A model to minimize costs and promote species persistence under climate change.- A Fix-and-Relax algorithm for solving parallel and sequential versions of a multi-period multi- product closed loop supply chain design and operation planning model.- Efficiency in Increasing Returns of Scale Frontier.- Modeling inter-sector health policy options and health gains in a long-term care network: a location-allocation stochastic planning approach.- Optimization of construction and demolition waste management: Application to the Lisbon Metropolitan Area.

Big Data Analytics in Genomics

Автор: Wong
Название: Big Data Analytics in Genomics
ISBN: 3319412787 ISBN-13(EAN): 9783319412788
Издательство: Springer
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Цена: 158380.00 T
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Описание:

This contributed volume explores the emerging intersection between big data analytics and genomics. Recent sequencing technologies have enabled high-throughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace. To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be sufficient or scalable, forcing the need for big data analytics to be developed for genomics. The computational methods addressed in the book are intended to tackle crucial biological questions using big data, and are appropriate for either newcomers or veterans in the field.
This volume offers thirteen peer-reviewed contributions, written by international leading experts from different regions, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA. In particular, the book surveys three main areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein function prediction, and perspectives on machine learning techniques in big data mining of cancer. Self-contained and suitable for graduate students, this book is also designed for bioinformaticians, computational biologists, and researchers in communities ranging from genomics, big data, molecular genetics, data mining, biostatistics, biomedical science, cancer research, medical research, and biology to machine learning and computer science. Readers will find this volume to be an essential read for appreciating the role of big data in genomics, making this an invaluable resource for stimulating further research on the topic.


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