Independent and identically distributed variables

Parametric distributions

Stochastic processes


Stochastic methods



Stochastic calculus

Exploratory data analysis

Distance metrics and outliers

Association rules

Data cleaning

Summary statistics

Data visualisation

Dimensionality reduction


Estimating probability distributions

Non-parametric estimation of probability distributions

Parametric estimation of probability distributions

Estimating population moments

Choosing parametric probability distributions

Parametric models for dependent variables

Latent variable models

Hypothesis testing

Linear regression

Ordinary Least Squares for prediction

Regularising linear regression for prediction

Ordinary Least Squares for inference

Generalised Least Squares

General Linear Models

Generalised linear models

Machine learning


Classification And Regression Trees (CART)

Support Vector Machines (SVM)

Other machine learning classifiers

Non-parametric regression

Ensemble methods

Advanced inference

Instrumental Variables and the General Method of Moments

Missing data

Semi-parametric regression

Homogeneous treatment effects (h3 on using semi-parametric here)

Heterogeneous treatment effects (LATE, causal tree (from CART))

Time series

Markov models

Univariate forecasting

Multivariate forecasting

Inference with time series

Neural networks

Feedforward neural networks

Recurrent neural networks

Generative neural networks