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Practical Statistics for Data Scientists
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Practical Statistics for Data Scientists
1. Exploratory data analysis
Elements of structured data
Estimates of location
Estimates of variability
Exploring the data distribution
Exploring binary and categorical data
Correlation
Exploring two or more variables
2. Data distributions
Random sampling and sample bias
Selection bias
Sampling distribution of a statistic
The bootstrap
Confidence intervals
Normal distribution
Long-tailed distributions
Student's t-distribution
Binomial distribution
Poisson and related distributions
3. Statistical experiments
A/B testing
Hypothesis tests
Resampling
Statistical significance and p-values
t-Tests
Multiple testing
Degrees of freedom
ANOVA
Chi-squre test
Multi-arm bandit algorithm
Power and sample size
4. Regression
Simple linear regression
Multiple linear regression
Prediction using regression
Factor variables in regression
Interpreting the regression equation
Testing the assumptions: regression diagnostics
Polynomial and spline regression
5. Classification
Naive Bayes
Discriminant analysis
Logistic regression
Evaluating classification models
Strategies for imbalanced data
6. Statistical ML
K-nearest neighbours
Tree models
Bagging and random forest
Boosting
7. Unsupervised learning
Principal components analysis
K-means clustering
Hierarchical clustering
Model-based clustering
Scaling and categorical variables
6. Statistical ML
Boosting
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