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Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

With this book, you’ll learn:

• Why exploratory data analysis is a key preliminary step in data science
• How random sampling can reduce bias and yield a higher quality dataset, even with big data
• How the principles of experimental design yield definitive answers to questions
• How to use regression to estimate outcomes and detect anomalies
• Key classification techniques for predicting which categories a record belongs to
• Statistical machine learning methods that “learn” from data
• Unsupervised learning methods for extracting meaning from unlabeled data

1. Preface
2. 1. Exploratory Data Analysis
1. Elements of Structured Data
2. Rectangular Data
3. Estimates of Location
4. Estimates of Variability
5. Exploring the Data Distribution
6. Exploring Binary and Categorical Data
7. Correlation
8. Exploring Two or More Variables
9. Summary
3. 2. Data and Sampling Distributions
1. Random Sampling and Sample Bias
2. Selection Bias
3. Sampling Distribution of a Statistic
4. The Bootstrap
5. Confidence Intervals
6. Normal Distribution
7. Long-Tailed Distributions
8. Student’s t-Distribution
9. Binomial Distribution
10. Poisson and Related Distributions
11. Summary
4. 3. Statistical Experiments and Significance Testing
1. A/B Testing
2. Hypothesis Tests
3. Resampling
4. Statistical Significance and P-Values
5. t-Tests
6. Multiple Testing
7. Degrees of Freedom
8. ANOVA
9. Chi-Square Test
10. Multi-Arm Bandit Algorithm
11. Power and Sample Size
12. Summary
5. 4. Regression and Prediction
1. Simple Linear Regression
2. Multiple Linear Regression
3. Prediction Using Regression
4. Factor Variables in Regression
5. Interpreting the Regression Equation
6. Testing the Assumptions: Regression Diagnostics
7. Polynomial and Spline Regression
8. Summary
6. 5. Classification
1. Naive Bayes
2. Discriminant Analysis
3. Logistic Regression
4. Evaluating Classification Models
5. Strategies for Imbalanced Data
6. Summary
7. 6. Statistical Machine Learning
1. K-Nearest Neighbors
2. Tree Models
3. Bagging and the Random Forest
4. Boosting
5. Summary
8. 7. Unsupervised Learning
1. Principal Components Analysis
2. K-Means Clustering
3. Hierarchical Clustering
4. Model-Based Clustering
5. Scaling and Categorical Variables
6. Summary
9. Bibliography
10. Index