This course is delivered by Dr Jake Aylmer, Professor Ted Shepherd and Dr Ben Harvey. It runs from October to December 2025.
You will learn statistical methods and reasoning relevant to environmental science. You’ll also gain experience in the proper use of statistics for the analysis of weather and climate data. Practical classes use Python. The topics are:
- Introduction to statistics: basic concepts, history
- Exploratory data analysis: summary statistics
- Forecast verification: skill scores
- Linear regression: correlation
- Multiple regression: confounders, causality
- Time series analysis: autocorrelation
- Concepts of probability: Bayes theorem
- Probability distributions: lots of different distributions!
- Parameter estimation: confidence intervals
- Hypothesis testing: significance tests, p-value.
There are practical assignments each week (10 in total) and four of these will be assessed.
Prerequisites
This course assumes familiarity with some basic mathematical concepts and scientific techniques:
- Linear equations and functions
- Basic trigonometry (sine and cosine functions)
- Summation notation
- Basic calculus
- Representing data in line and scatter plots and tables.
The interactive classes use Python. Learners should have prior experience using this or a similar programming language (such as R or MATLAB) for scientific data analysis. Support will be available for those new to Python.
This training is suitable for professional meteorologists, consultants, STEM students, graduates and academics seeking to improve their range of skills and knowledge. The courses may be suitable CPD for chartered meteorologists.
We welcome applications and inquiries from people in the UK and around the world.
What to expect
- Each week, new lecture notes with videos.
- Weekly live online interactive sessions.
- Exercises and assessments.
- The opportunity to ask for help at live online sessions and on class discussion boards.
- To study between four and eight hours per week.
- Feedback on your work.
- A certificate when you successfully complete the course.
