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Ideal for ecologists, environmental consultants, conservation professionals, researchers, students and anyone wishing to develop practical data analysis skills.
Learn how to handle, analyse and visualise data using R, one of the most powerful and widely used tools in scientific research, ecology, environmental consultancy and data science.
This practical, hands-on course is designed for complete beginners and assumes no prior experience with R. Through a combination of live online teaching, guided exercises, supporting resources and independent practice materials, you will develop the skills and confidence to undertake your own data analysis projects.
Each session includes live tutor-led demonstrations and opportunities to ask questions, work through exercises and discuss analytical approaches. Between sessions, participants will have access to additional learning materials, worked examples and exercises to reinforce their understanding at their own pace.
As part of the course, participants receive:
Four 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 for practice
Additional self-paced learning materials and exercises
The opportunity to apply techniques to your own datasets and receive guidance during the course
Topics covered include:
Introduction to R and RStudio
Understanding scripts and reproducible workflows
Installing and managing packages
Importing, organising and cleaning data
Exploring data using summary statistics and visualisation
Creating publication-quality graphs
Introduction to statistical testing
Linear models and interpreting outputs
Generalised Linear Models (GLMs) for measurement and count data
Communicating and presenting analytical results
By the end of the course, you will be able to import, manage, analyse and visualise data in R, understand the principles behind common statistical approaches, and apply these techniques confidently to your own research or professional projects.
Fee: £220 + VAT (£264)
Ideal for ecologists, environmental consultants, conservation professionals, researchers, students and anyone wishing to develop practical data analysis skills.
Learn how to handle, analyse and visualise data using R, one of the most powerful and widely used tools in scientific research, ecology, environmental consultancy and data science.
This practical, hands-on course is designed for complete beginners and assumes no prior experience with R. Through a combination of live online teaching, guided exercises, supporting resources and independent practice materials, you will develop the skills and confidence to undertake your own data analysis projects.
Each session includes live tutor-led demonstrations and opportunities to ask questions, work through exercises and discuss analytical approaches. Between sessions, participants will have access to additional learning materials, worked examples and exercises to reinforce their understanding at their own pace.
As part of the course, participants receive:
Four 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 for practice
Additional self-paced learning materials and exercises
The opportunity to apply techniques to your own datasets and receive guidance during the course
Topics covered include:
Introduction to R and RStudio
Understanding scripts and reproducible workflows
Installing and managing packages
Importing, organising and cleaning data
Exploring data using summary statistics and visualisation
Creating publication-quality graphs
Introduction to statistical testing
Linear models and interpreting outputs
Generalised Linear Models (GLMs) for measurement and count data
Communicating and presenting analytical results
By the end of the course, you will be able to import, manage, analyse and visualise data in R, understand the principles behind common statistical approaches, and apply these techniques confidently to your own research or professional projects.
Fee: £220 + VAT (£264)