Field Data Experts
Educational Enjoyment
At Eco-Explore, we're more than just educators; we're life scientists passionate about the environment and the natural world. We understand that for many aspiring and current ecologists, the world of data analysis can be as challenging as the terrain we wish to protect. That's why our specialised training programs are crafted to empower you with the analytical tools necessary for ground-breaking work in science and conservation. Every course you take with us not only sharpens your professional edge but also supports the environment directly. Profits from our programs are reinvested into local conservation initiatives—your growth aids the growth of our planet.
Check out our projects to see the tangible impacts of your contributions and how Eco-Explore is turning data analysis into a force for ecological advancement.
Our Courses
Introduction to Data Analysis with R
4 DAY COURSE | Tuesdays 09:00am-12:00pm
Dates: 3rd, 10th, 17th & 24th September 2026
Fee: £220 + VAT (£264)
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.
Advanced Data Analysis in R
5 DAY COURSE| Tuesdays 09:00am-12:00pm
Dates: 14th, 21st, 28th October and 4th & 11th November 2026
£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.
“Incredibly knowledgeable teachers with amazing experience of R”
“Excellent step-by-step walkthroughs and explanation of key concepts!”
“Just too good to be true! Already applying these codes to my data set”
“Had not used R before and stats was very rusty, Feel that I can now use R and its similarities/differences to Python and how much better it is a graph visualisation.” –
Guidebook
Our 150-page guidebook is designed to help you quickly to become familiar with R and to explore its potential as a powerful tool for analysing your data, whatever your field of research. The guidebook covers the simple things (getting started with R, plotting graphs, simple statistical tests) as well as more complex topics (e.g. GLM, GAM, GLMM, multivariate data exploration, time-series and survival analysis, spatial analysis).