Ready to take your statistical analysis to the next level?
This advanced course is designed for participants who already have experience using R and are familiar with linear models but wish to develop a deeper understanding of modern statistical approaches used in research, environmental science, ecology, healthcare and applied data analysis.
Through a combination of live online teaching, practical exercises, worked examples and self-paced learning materials, you will learn how to analyse more complex datasets and address the challenges commonly encountered in real-world research and professional practice.
Each session combines tutor-led demonstrations with hands-on exercises, allowing participants to build and interpret advanced statistical models using R. Participants are encouraged to bring their own datasets and analytical questions for discussion throughout the course.
As part of the course, participants receive:
Five live online teaching sessions led by an experienced data analyst
A comprehensive course handbook and reference guide
All R code used during the course
Example datasets and worked case studies
Additional self-paced learning materials and exercises between sessions
Opportunities to discuss and apply techniques to your own data
Topics covered include:
Revisiting statistical modelling principles and model selection
Generalised Linear Models (GLMs): choosing appropriate error structures
Generalised Linear Mixed Models (GLMMs) for hierarchical and repeated-measures data
Generalised Additive Models (GAMs) for non-linear relationships
Zero-Inflated Models for count data with excess zeros
Model diagnostics and validation
Interpreting and communicating model outputs
Visualising complex model results
Selecting appropriate modelling approaches for different data types and research questions
By the end of the course, you will be able to build, interpret and critically evaluate a range of advanced statistical models in R, understand their assumptions and limitations, and confidently apply them to your own research or professional projects.
Prerequisites: Participants should be comfortable using R and RStudio and have a basic understanding of linear modelling. Completion of our Introduction to Data Analysis with R course, or equivalent experience, is recommended.
Fee: £275 + VAT (£330)
Ready to take your statistical analysis to the next level?
This advanced course is designed for participants who already have experience using R and are familiar with linear models but wish to develop a deeper understanding of modern statistical approaches used in research, environmental science, ecology, healthcare and applied data analysis.
Through a combination of live online teaching, practical exercises, worked examples and self-paced learning materials, you will learn how to analyse more complex datasets and address the challenges commonly encountered in real-world research and professional practice.
Each session combines tutor-led demonstrations with hands-on exercises, allowing participants to build and interpret advanced statistical models using R. Participants are encouraged to bring their own datasets and analytical questions for discussion throughout the course.
As part of the course, participants receive:
Five live online teaching sessions led by an experienced data analyst
A comprehensive course handbook and reference guide
All R code used during the course
Example datasets and worked case studies
Additional self-paced learning materials and exercises between sessions
Opportunities to discuss and apply techniques to your own data
Topics covered include:
Revisiting statistical modelling principles and model selection
Generalised Linear Models (GLMs): choosing appropriate error structures
Generalised Linear Mixed Models (GLMMs) for hierarchical and repeated-measures data
Generalised Additive Models (GAMs) for non-linear relationships
Zero-Inflated Models for count data with excess zeros
Model diagnostics and validation
Interpreting and communicating model outputs
Visualising complex model results
Selecting appropriate modelling approaches for different data types and research questions
By the end of the course, you will be able to build, interpret and critically evaluate a range of advanced statistical models in R, understand their assumptions and limitations, and confidently apply them to your own research or professional projects.
Prerequisites: Participants should be comfortable using R and RStudio and have a basic understanding of linear modelling. Completion of our Introduction to Data Analysis with R course, or equivalent experience, is recommended.
Fee: £275 + VAT (£330)
Advanced Data Analysis with R Statistical Software Course
Fee: £275 per person +VAT (£330) (includes tuition and all teaching materials - guidebook, template script files, datasets, PowerPoint materials, etc.)
