统计方法是数据科学的关键部分，但鲜有数据科学家接受过正规的统计培训。基础统计学的相关课程和书籍很少从数据科学的角度来介绍这个主题。这本广受欢迎的指南手册的第二版添加了Python综合示例，提供了将统计方法应用于数据科学的实用指导，告诉你如何避免误用这些方法，并就什么重要，什么不重要给出了建议。许多数据科学资源都引入了统计方法，但是缺乏更深层次的统计视角。如果你熟悉R或Python编程语言，对统计学有所了解，那么这本快速参考将以其通俗易懂的格式弥补这种空白。
Preface
1. Exploratory Data Analysis
Elements of Structured Data
Further Reading
Rectangular Data
Data Frames and Indexes
Nonrectangular Data Structures
Further Reading
Estimates of Location
Mean
Median and Robust Estimates
Example: Location Estimates of Population and Murder Rates
Further Reading
Estimates of Variability
Standard Deviation and Related Estimates
Estimates Based on Percentiles
Example: Variability Estimates of State Population
Further Reading
Exploring the Data Distribution
Percentiles and Boxplots
Frequency Tables and Histograms
Density Plots and Estimates
Further Reading
Exploring Binary and Categorical Data
Mode
Expected Value
Probability
Further Reading
Correlation
Scatterplots
Further Reading
Exploring Two or More Variables
Hexagonal Binning and Contours (Plotting Numeric Versus Numeric Data)
Two Categorical Variables
Categorical and Numeric Data
Visualizing Multiple Variables
Further Reading
Summary
2. Data and Sampling Distributions
Random Sampling and Sample Bias
Bias
Random Selection
Size Versus Quality: When Does Size Matter?
Sample Mean Versus Population Mean
Further Reading
Selection Bias
Regression to the Mean
Further Reading
Sampling Distribution of a Statistic
Central Limit Theorem
Standard Error
Further Reading
The Bootstrap
Resampling Versus Bootstrapping
Further Reading
Confidence Intervals
Further Reading
Normal Distribution
Standard Normal and QQ-Plots
Long-Tailed Distributions
Further Reading
Student's t-Distribution
Further Reading
Binomial Distribution
Further Reading
Chi-Square Distribution
Further Reading
F-Distribution
……
3. Statistical Experiments and Significance Testing
4. Regression and Prediction
5. Classification
6. Statistical Machine Learning
7. Unsupervised Learning
Bibliography
Index
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