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Titlebook: Data Science and Predictive Analytics; Biomedical and Healt Ivo D. Dinov Textbook 20181st edition Ivo D. Dinov 2018 big data.R.statistical

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發(fā)表于 2025-3-21 19:26:10 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Data Science and Predictive Analytics
副標題Biomedical and Healt
編輯Ivo D. Dinov
視頻videohttp://file.papertrans.cn/264/263104/263104.mp4
概述A novel transdisciplinary treatise of predictive health analytics.Complete and self-contained treatment of the theory, experimental modeling, system development, and validation of predictive health an
圖書封面Titlebook: Data Science and Predictive Analytics; Biomedical and Healt Ivo D. Dinov Textbook 20181st edition Ivo D. Dinov 2018 big data.R.statistical
描述Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. There are three reasons for this shortfall. First, the volume of data is increasing much faster than the corresponding rise of our computational processing power (Kryder’s law > Moore’s law). Second, traditional discipline-bounds inhibit expeditious progress. Third, our education and training activities have fallen behind the accelerated trend of scientific, information, and communication advances. There are very few rigorous instructional resources, interactive learning materials, and dynamic trainingenvironments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pi
出版日期Textbook 20181st edition
關鍵詞big data; R; statistical computing; predictive analytics; data science; health analytics; machine learning
版次1
doihttps://doi.org/10.1007/978-3-319-72347-1
isbn_softcover978-3-030-10187-9
isbn_ebook978-3-319-72347-1
copyrightIvo D. Dinov 2018
The information of publication is updating

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Linear Algebra & Matrix Computing,is generally challenging to visualize complex data, e.g., large vectors, tensors, and tables in n-dimensional Euclidian spaces (.?≥?3). Linear algebra allows us to mathematically represent, computationally model, statistically analyze, synthetically simulate, and visually summarize such complex data
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Dimensionality Reduction,ber of features when modeling a very large number of variables. Dimension reduction can help us extract a set of “uncorrelated” principal variables and reduce the complexity of the data. We are not simply picking some of the original variables. Rather, we are constructing new “uncorrelated” variable
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Decision Tree Divide and Conquer Classification,les. In some cases, we need to specify well stated rules for our decisions, just like a scoring criterion for driving ability or credit scoring for loan underwriting. The decisions in many situations actually require having a clear and easily understandable decision tree to follow the classification
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Forecasting Numeric Data Using Regression Models, this Chapter, we will focus on specific model-based statistical methods providing forecasting and classification functionality. Specifically, we will (1) demonstrate the predictive power of multiple linear regression; (2) show the foundation of regression trees and model trees; and (3) examine two
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