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

Exploring two or more variables

2. Data distributions

Random sampling and sample bias

Sampling distribution of a statistic

Confidence intervals

Normal distribution

Long-tailed distributions

Student's t-distribution

Binomial distribution

Poisson and related distributions

3. Statistical experiments

Hypothesis tests

Statistical significance and p-values

Multiple testing

Degrees of freedom

Multi-arm bandit algorithm

Power and sample size

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

Discriminant analysis

Logistic regression

Evaluating classification models

Strategies for imbalanced data

6. Statistical ML

K-nearest neighbours

Bagging and random forest

7. Unsupervised learning

Principal components analysis

K-means clustering

Hierarchical clustering

Model-based clustering

Scaling and categorical variables

1. Exploratory data analysis

Exploring two or more variables

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