Probability and Statistics for Data Science: Math + R + Data
Matloff, Norman
- 出版商: CRC
- 出版日期: 2019-06-20
- 售價: $2,310
- 貴賓價: 9.5 折 $2,195
- 語言: 英文
- 頁數: 376
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1138393290
- ISBN-13: 9781138393295
-
相關分類:
R 語言、機率統計學 Probability-and-statistics、資料科學
-
相關翻譯:
概率與統計:數據科學視角 (簡中版)
-
其他版本:
Probability and Statistics for Data Science: Math + R + Data
立即出貨 (庫存 < 3)
買這商品的人也買了...
-
$1,390$1,362 -
$296R語言實用教程
-
$327生成對抗網絡入門指南 (Generative adversarial Networks)
-
$500統計學習方法, 2/e
-
$780$616 -
$403Python機器學習及實踐
-
$422TensorFlow深度學習及實踐
-
$414$393 -
$454深度學習 — 從神經網絡到深度強化學習的演進
-
$768$730
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.
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.