Data Analysis 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Data Analysis

简体网页||繁体网页
Peter J. Huber 作者
Wiley
译者
2011-4-19 出版日期
234 页数
USD 120.00 价格
Hardcover
丛书系列
9781118010648 图书编码

Data Analysis 在线电子书 图书标签: 计算机  Statistics  MachineLearning  DataMining  DataAnalysis_ExperimentDesign  Data   


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发表于2024-07-03


Data Analysis 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Data Analysis 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Data Analysis 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



Data Analysis 在线电子书 用户评价

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The War theroy in Data Analysis Chapter 2 and Chapter 6 is excellent. The metaphor of war is a nice touch. In the data analysisi, we really need more strategy thinking to coulpe with the complexity of the data.

评分

The War theroy in Data Analysis Chapter 2 and Chapter 6 is excellent. The metaphor of war is a nice touch. In the data analysisi, we really need more strategy thinking to coulpe with the complexity of the data.

评分

The War theroy in Data Analysis Chapter 2 and Chapter 6 is excellent. The metaphor of war is a nice touch. In the data analysisi, we really need more strategy thinking to coulpe with the complexity of the data.

评分

The War theroy in Data Analysis Chapter 2 and Chapter 6 is excellent. The metaphor of war is a nice touch. In the data analysisi, we really need more strategy thinking to coulpe with the complexity of the data.

评分

The War theroy in Data Analysis Chapter 2 and Chapter 6 is excellent. The metaphor of war is a nice touch. In the data analysisi, we really need more strategy thinking to coulpe with the complexity of the data.

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Data Analysis 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Data Analysis 在线电子书 图书描述

This book explores the many provocative questions concerning the fundamentals of data analysis. It is based on the time-tested experience of one of the gurus of the subject matter. Why should one study data analysis? How should it be taught? What techniques work best, and for whom? How valid are the results? How much data should be tested? Which machine languages should be used, if used at all? Emphasis on apprenticeship (through hands-on case studies) and anecdotes (through real-life applications) are the tools that Peter J. Huber uses in this volume. Concern with specific statistical techniques is not of immediate value; rather, questions of strategy – when to use which technique – are employed. Central to the discussion is an understanding of the significance of massive (or robust) data sets, the implementation of languages, and the use of models. Each is sprinkled with an ample number of examples and case studies. Personal practices, various pitfalls, and existing controversies are presented when applicable. The book serves as an excellent philosophical and historical companion to any present-day text in data analysis, robust statistics, data mining, statistical learning, or computational statistics.

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