Computational and Statistical Methods for Analysing Big Data with Applications

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  • Author : Shen Liu
  • Publisher : Academic Press
  • Pages : 206 pages
  • ISBN : 0081006519
  • Rating : 5/5 from 4 reviews
CLICK HERE TO GET THIS BOOK >>>Computational and Statistical Methods for Analysing Big Data with Applications

Download or Read online Computational and Statistical Methods for Analysing Big Data with Applications full in PDF, ePub and kindle. this book written by Shen Liu and published by Academic Press which was released on 20 November 2015 with total page 206 pages. We cannot guarantee that Computational and Statistical Methods for Analysing Big Data with Applications book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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

Computational and Statistical Methods for Analysing Big Data with Applications

Computational and Statistical Methods for Analysing Big Data with Applications
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  • Publisher : Academic Press
  • Release : 20 November 2015
GET THIS BOOK Computational and Statistical Methods for Analysing Big Data with Applications

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

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  • Publisher : Springer
  • Release : 30 November 2016
GET THIS BOOK Advanced Statistical Methods in Data Science

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GET THIS BOOK Combining Soft Computing and Statistical Methods in Data Analysis

Over the last forty years there has been a growing interest to extend probability theory and statistics and to allow for more flexible modelling of imprecision, uncertainty, vagueness and ignorance. The fact that in many real-life situations data uncertainty is not only present in the form of randomness (stochastic uncertainty) but also in the form of imprecision/fuzziness is but one point underlining the need for a widening of statistical tools. Most such extensions originate in a "softening" of classical

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GET THIS BOOK Big Data Analysis New Algorithms for a New Society

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  • Publisher : Springer
  • Release : 24 October 2016
GET THIS BOOK Big Data Analytics in Genomics

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

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GET THIS BOOK Statistics for Data Science

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  • Release : 03 August 2016
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This book offers a snapshot of the state-of-the-art in classification at the interface between statistics, computer science and application fields. The contributions span a broad spectrum, from theoretical developments to practical applications; they all share a strong computational component. The topics addressed are from the following fields: Statistics and Data Analysis; Machine Learning and Knowledge Discovery; Data Analysis in Marketing; Data Analysis in Finance and Economics; Data Analysis in Medicine and the Life Sciences; Data Analysis in the Social, Behavioural,

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  • Publisher : Academic Press
  • Release : 09 May 2021
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The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text

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  • Publisher : Springer
  • Release : 05 February 2016
GET THIS BOOK Techniques and Environments for Big Data Analysis

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