Online courses in Applied Statistics, Genomics, Bioinformatics, Ecology and Social Sciences
Learn from leading experts, over 350 courses delivered since 2014 across 60 diverse subjects
Understand the data behind the science
Expert-Led Training
Live and recorded access
Beginner - Advanced
Discuss your own data
Introduction to Generalised Linear Models using R (COURSE FULL)
Introduction to Generalised Linear Models using R (COURSE FULL)
Learn Generalised Linear Models (GLMs) in R with this live online course. Covers Poisson regression, logistic regression, multinomial, ordinal, Gamma models, mixed-effects, and Bayesian GLMs. Ideal for researchers, postgraduate students, and data analysts.
Duration: 5 Days, 6 hours per day- Next Date: January 12-16, 2026
- Format: Live Online Format
£450Registration Fee
View DetailsIntroduction to Generalised Linear Models using R (COURSE FULL)
Learn Generalised Linear Models (GLMs) in R with this live online course. Covers Poisson regression, logistic regression, multinomial, ordinal, Gamma models, mixed-effects, and Bayesian GLMs. Ideal for researchers, postgraduate students, and data analysts.
Introduction to Python for Ecologists and Evolutionary Biologists
Introduction to Python for Ecologists and Evolutionary Biologists
Beginner Python course for biologists. Learn file handling, loops, and bioinformatics-focused coding in Python.
- Duration: 4 Days, 7 hours per day
- Next Date: January 26-29, 2026
- Format: Live Online Format
£480Registration Fee
View DetailsIntroduction to Python for Ecologists and Evolutionary Biologists
Beginner Python course for biologists. Learn file handling, loops, and bioinformatics-focused coding in Python.
RNA-Seq Analysis
RNA-Seq Analysis
RNA-Seq analysis training – live online course covering experiment design, data QC, alignment, gene expression, DESeq2 differential expression, PCA, visualisation, and functional analysis.
- Duration: 4 Days, 3.5 hours per day
- Next Date: January 26-29, 2026
- Format: Live Online Format
£350Registration Fee
View DetailsRNA-Seq Analysis
RNA-Seq analysis training – live online course covering experiment design, data QC, alignment, gene expression, DESeq2 differential expression, PCA, visualisation, and functional analysis.
Species Distribution Modelling With Bayesian Statistics (Registration deadline January 13th)
Species Distribution Modelling With Bayesian Statistics (Registration deadline January 13th)
Model species distributions using BART in R. Covers uncertainty, variable selection, and full Bayesian workflow.
- Duration: 3 Days, 4 hours per day
- Next Date: January 27-29, 2026
- Format: Live Online Format
£385Registration Fee
View DetailsSpecies Distribution Modelling With Bayesian Statistics (Registration deadline January 13th)
Master Bayesian multilevel models in R with brms. Learn GLMs, priors, spatial/temporal autocorrelation, and species distribution modelling.
Introduction to Generalised Linear Mixed Models for Ecologists
Introduction to Generalised Linear Mixed Models for Ecologists
Learn to build and interpret linear, generalised linear, and multilevel models for ecological data using R, lme4, and rstanarm in this five day applied training course.
- Duration: 5 Days, 6 hours per day
- Next Date: February 2-6, 2026
- Format: Live Online Format
£450Registration Fee
View DetailsIntroduction to Generalised Linear Mixed Models for Ecologists
Learn to build and interpret linear, generalised linear, and multilevel models for ecological data using R, lme4, and rstanarm in this five day applied training course.
Python for Data Science and Statistical Computing
Python for Data Science and Statistical Computing
Learn Python for data science and statistical computing. Build skills in NumPy, Pandas and visualisation across two days of hands-on training for researchers and analysts.
- Duration: 2 Days, 6 hours per day
- Next Date: February 9-10, 2026
- Format: Live Online Format
£300Registration Fee
View DetailsPython for Data Science and Statistical Computing
Learn Python for data science and statistical computing. Build skills in NumPy, Pandas and visualisation across two days of hands-on training for researchers and analysts.
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we’ve found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
I have attended six different courses organised by PR Stats. When I was midway through my PhD it was apparent that statistical ecological science was inevitably evolving faster than the knowledge of some of my university tutors. Attending courses with PR Stats became a vital fundamental way of learning advanced statistical and spatial analysis. Since then, I have continued attending courses covering new material, mixed effects models, Bayesian statistics, and including recently introduced R packages and their functionality. The courses have also given me exposure to some world leaders in their field. I have appreciated discussing with them different modelling approaches, which have been informative for my research. I can count four of my published papers as having been directly influenced by courses from PR Stats. My most recent work benefitted from modelling advice on sample design and model accuracy evaluation and can be seen here.
Carlos P.E. Bedson
Quantitative Spatial Ecology, Ecology and Environment Research Centre, Manchester Metropolitan University, United Kingdom
Courses attended
ADVR08, ENMR03, BMIN02, ISBD01, BADA01, SDMB06
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we’ve found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07