These courses are intended to provide a strong foundation in practical statistics and data analysis.
The underlying philosophy of the courses is to treat statistics as a practical skill rather than as a theoretical subject
Core Statistics using R
In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory.
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Experimental Design for statistical analysis
This one-day course is primarily aimed at life science researchers, but covers many topics that are applicable to other fields. It combines key theoretical knowledge with practical application, which will aid researchers in designing effective experiments. The focus throughout the course is to link experimental design to a clear analysis strategy. This ensures that the collected data will be suitable for statistical analysis.
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Linear mixed effect models
This course gives an introduction to linear mixed effects models, also called multi-level models or hierarchical models, for the purposes of using them in your own research or studies.
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Generalised linear models
This course aims to introduce generalised linear models, using the R software environment. Similar to Core statistics using R this course addresses the practical aspects of using these models, so you can explore real-life issues in the biological sciences.
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Introduction to Bayesian Inference
This course is aimed to provide the tools to conduct Bayesian inference in common situations.
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