Probability and Statistics for Data Science: Math + R + Data
暫譯: 數據科學的機率與統計：數學 + R + 數據

Matloff, Norman

Probability and Statistics for Data Science: Math + R + Data

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商品描述

This book covers "math stat"--distributions, expected value, estimation etc.--but takes the phrase "Data Science" in the title quite seriously:

 

* Real datasets are used extensively.

 

* All data analysis is supported by R coding.

 

* Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.

 

* Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture."

 

* Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner.

 

Prerequisites are calculus, some matrix algebra, and some experience in programming.

 

Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.

 

 

商品描述(中文翻譯)

這本書涵蓋了「數學統計」——分佈、期望值、估計等——但對於書名中的「數據科學」這個詞非常認真：

* 實際數據集被廣泛使用。

* 所有數據分析都由 R 語言編碼支持。

* 包含許多數據科學應用，例如主成分分析（PCA）、混合分佈、隨機圖模型、隱馬可夫模型、線性和邏輯回歸以及神經網絡。

* 引導學生批判性地思考統計的「如何」和「為何」，並「看到全局」。

* 不是以「定理/證明」為導向，但概念和模型以數學精確的方式陳述。

先修課程包括微積分、一些矩陣代數和一些編程經驗。

**Norman Matloff** 是加州大學戴維斯分校的計算機科學教授，曾任該校的統計學教授。他是《統計軟件期刊》（Journal of Statistical Software）和《R期刊》（The R Journal）的編輯委員會成員。他的書籍《統計回歸與分類：從線性模型到機器學習》（Statistical Regression and Classification: From Linear Models to Machine Learning）於2017年獲得《Technometrics》最佳書籍評價的 Ziegel 獎。他是其大學的傑出教學獎獲得者。

作者簡介

Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.

作者簡介(中文翻譯)

諾曼·馬特洛夫是加州大學戴維斯分校的計算機科學教授，曾擔任該校的統計學教授。他是Journal of Statistical Software和The R Journal的編輯委員會成員。他的著作Statistical Regression and Classification: From Linear Models to Machine Learning於2017年獲得Technometrics最佳書籍評價的齊格爾獎。他也是該大學的傑出教學獎得主。

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