The two most popular courses are:

Course 1: Data exploration, regression, GLM and GAM. With an introduction to R.
Course 2: Introduction to mixed modelling and GLMM. We provide three versions of this course; a frequentist version, a Bayesian version with JAGS and a Bayesian version with INLA. The frequentist course is easier, but the Bayesian courses are more versatile.

If you do not have spatial or temporal data, then we recommend to attend the following courses within a time span of 3 years.

Introduction to data exploration, regression, GLM and GAM. With introduction to R.
Introduction to mixed effects models and GLMM (frequentist version).
Depending on whether you have zero-inflation and/or non-linear relationships you can then attend the ‘Introduction to zero-inflated models’ and/or ‘Introduction to GAM and GAMM’ courses (frequentist version).

If you have spatial or temporal data, then we recommend the following courses.

Introduction to data exploration, regression, GLM and GAM. With introduction to R.
Introduction to mixed effects models and GLMM (INLA version).
Introduction to spatial and spatial-temporal models with R-INLA.
Depending on whether you have zero-inflation, non-linear relationships and/or time series data, you can then attend the ‘Introduction to zero-inflated models’, ‘Introduction to GAM and GAMM’ and/or the ‘Time series analysis using R-INLA’ courses (INLA version).

It is possible to skip the ‘Introduction to mixed effects models and GLMM’ course before taking the INLA courses, but this requires some preparation.
For more specialised GLMs and GLMMs (without spatial or temporal correlation) we recommend the JAGS version instead of INLA.

Our courses are non-technical and are less suited for people interested in the underlying mathematics.