Making Sense of Data II 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Making Sense of Data II

简体网页||繁体网页
Glenn J. Myatt 作者
John Wiley & Sons Inc
译者
2009-3-10 出版日期
291 页数
$79.95 价格
Paperback
丛书系列
9780470222805 图书编码

Making Sense of Data II 在线电子书 图书标签: 数据可视化  DataMining  统计  数据分析  Visualization  科普  数据处理  原始/图形思维   


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Making Sense of Data II 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Making Sense of Data II 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Making Sense of Data II 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



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一直没搞明白Management Science和Data Mining的共同点在哪里。。

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一直没搞明白Management Science和Data Mining的共同点在哪里。。

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579rmb, 只有200页正文,太贵了!

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一直没搞明白Management Science和Data Mining的共同点在哪里。。

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一直没搞明白Management Science和Data Mining的共同点在哪里。。

Making Sense of Data II 在线电子书 著者简介


Making Sense of Data II 在线电子书 图书目录


Making Sense of Data II 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Making Sense of Data II 在线电子书 图书描述

A hands-on guide to making valuable decisions from data using advanced data mining methods and techniques

This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data. Delving into more technical topics, this book equips readers with advanced data mining methods that are needed to successfully translate raw data into smart decisions across various fields of research including business, engineering, finance, and the social sciences.

Following a comprehensive introduction that details how to define a problem, perform an analysis, and deploy the results, Making Sense of Data II addresses the following key techniques for advanced data analysis:

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Data Visualization reviews principles and methods for understanding and communicating data through the use of visualization including single variables, the relationship between two or more variables, groupings in data, and dynamic approaches to interacting with data through graphical user interfaces.

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Clustering outlines common approaches to clustering data sets and provides detailed explanations of methods for determining the distance between observations and procedures for clustering observations. Agglomerative hierarchical clustering, partitioned-based clustering, and fuzzy clustering are also discussed.

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Predictive Analytics presents a discussion on how to build and assess models, along with a series of predictive analytics that can be used in a variety of situations including principal component analysis, multiple linear regression, discriminate analysis, logistic regression, and Naïve Bayes.

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Applications demonstrates the current uses of data mining across a wide range of industries and features case studies that illustrate the related applications in real-world scenarios.

Each method is discussed within the context of a data mining process including defining the problem and deploying the results, and readers are provided with guidance on when and how each method should be used. The related Web site for the series (www.makingsenseofdata.com) provides a hands-on data analysis and data mining experience. Readers wishing to gain more practical experience will benefit from the tutorial section of the book in conjunction with the TraceisTM software, which is freely available online.

With its comprehensive collection of advanced data mining methods coupled with tutorials for applications in a range of fields, Making Sense of Data II is an indispensable book for courses on data analysis and data mining at the upper-undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who are interested in learning how to accomplish effective decision making from data and understanding if data analysis and data mining methods could help their organization.

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