Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide—now including examples in Python as well as R—explains how to apply various statistical methods to data science, tells you how to avo...(展开全部)

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide—now including examples in Python as well as R—explains how to apply various statistical methods to data science, tells you how to avo...(展开全部)

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide—now including examples in Python as well as R—explains how to apply various statistical methods to data science, tells you how to avo...(展开全部)

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide—now including examples in Python as well as R—explains how to apply various statistical methods to data science, tells you how to avo...(展开全部)

彼得·布鲁斯（Peter Bruce），知名统计学家，Statistics.com统计学教育学院的创立者兼院长，重采样统计软件的开发者。曾在美国马里兰大学和各种短训班教授重采样统计课程。 安德鲁·布鲁斯（Andrew Bruce），华盛顿大学统计学博士，拥有30多年的统计学和数据科学经验，在多家知名学术期刊上发表过多篇论文。

彼得·布鲁斯（Peter Bruce），知名统计学家，Statistics.com统计学教育学院的创立者兼院长，重采样统计软件的开发者。曾在美国马里兰大学和各种短训班教授重采样统计课程。 安德鲁·布鲁斯（Andrew Bruce），华盛顿大学统计学博士，拥有30多年的统计学和数据科学经验，在多家知名学术期刊上发表过多篇论文。

This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful.

Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnos...(展开全部)

David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science, School of Operations Research and Information Engineering, Cornell University, where he teaches statistics and financial engineering and is a member of the Program in Financial Engineering. His research areas include asymptotic theory, semiparametric regression, functional data ...(展开全部)

Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnos...(展开全部)