Dear MARMaM users,

We’ve been posting here a bit more frequently recently, and the response has been fantastic—so thank you to the admins for allowing us to share our courses, and to everyone who has taken a course or helped spread the word.

As a thank you to the group, PR Stats would like to offer 10–20% off all live courses and 15% off all recorded courses.

This offer is valid until 14 February. A full list of available courses and the corresponding discounts can be found below.

If you have any questions, please don’t hesitate to get in touch at oliver@prstats.org

20% off (use ‘MAM20’)

Analysing Ecological Data with Detection Error

Learn to analyse ecological field data with detection error using R. Work with point counts, ARU data, N-mixture models, distance sampling and time-removal methods.

Introduction to Generalised Linear Mixed Models for Ecologists (MMIE02)

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.

Bayesian Statistical Modelling with Stan and brms (BMSB01)

Bayesian Statistical Modelling with Stan and brms is an advanced R course for researchers covering Bayesian model building, diagnostics, and interpretation using Stan and brms.

Machine Learning for Ecological Time Series (METR01)

Machine Learning for Ecological Time Series is an applied R course teaching ecologists how to analyse, model, and predict ecological time series data.

Machine Learning for Time Series (MLTP01)

Machine Learning for Time Series is a practical Python course teaching how to model, analyse, and forecast time series data using machine learning methods.

Deep Learning using R (DLUR01)

Learn deep learning in R using the torch ecosystem. Build MLPs, CNNs and transformer models through hands-on coding and gain practical skills for real research workflows.

Interactive Data Applications with Shiny (SHID01)

Interactive Data Applications with Shiny is a practical R Shiny course for researchers focused on building, customising, and deploying interactive web applications from data analyses.

Python for Data Science and Statistical Computing (PYDS01)

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.

Deep Learning Using Python (DLUP01)

Deep learning course using Python and PyTorch. Learn neural networks, CNNs and transformers through hands-on coding and real data across two intensive training days.

Advanced Python for Ecologists and Evolutionary Biologists

Take your Python skills further. Learn OOP, testing, and optimisation for complex bioinformatics tasks.

Python for Biological Data Exploration and Visualization

Explore and visualise biological data in Python using pandas and seaborn. Ideal for applied researchers.

Single cell RNA-Seq analysis

Learn single cell RNA-Seq analysis with Seurat, 10x Genomics, and advanced QC methods. Gain cell type-specific insights in this live online course.

Introduction to Processing and Analysis of Spatial Multiplexed Proteomics Data (SPMP02)

Learn spatial multiplexed proteomics data analysis with CODEX, CycIF, and MACSIMA. Master image processing, segmentation, phenotyping, and spatial analysis in R and Python.

10% off (use ‘MAM10’)

Bayesian Modelling Using R-INLA

Learn Bayesian modelling with the R-INLA package. Build, fit, and interpret INLA models, define priors and latent effects, and apply INLA to real data in a five day course.

Multivariate Analysis of Ecological Communities Using VEGAN (VGNR09)

Analyse ecological community data in R using VEGAN. Learn ordination, clustering, and multivariate statistics with real datasets.

Movement Ecology (the Analysis of Movement Data)

Learn to analyse animal movement data using spatial methods, home range estimation, interaction metrics and resource or step selection models through hands-on training in R.

Species Distribution Modelling (SDMs) and Ecological Niche Modelling (ENMs) (SDMR07)

Learn ENM and SDM modelling in R. Apply tools like Maxent and Biomod2 to predict species distributions and environmental niches.

Bioacoustics Data Analysis (BIAC06)

Analyse animal acoustic signals in R. Learn spectrograms, annotations, and bioacoustic workflows.

Network Analysis for Ecologists (NWAE02)

Use R to analyse ecological networks. Learn metrics, simulation, and visualisation with igraph.