Practical Statistics for Data Scientists

Pages
- 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