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 <https://prstats.org/course/analysing-ecological-data-with-detection-error-aedd01/>* 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) <https://prstats.org/course/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) <https://prstats.org/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) <https://prstats.org/course/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) <https://prstats.org/course/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) <https://prstats.org/course/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) <https://prstats.org/course/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) <https://prstats.org/course/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) <https://prstats.org/course/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 <https://prstats.org/course/advanced-python-for-biologists-apyb01/>* Take your Python skills further. Learn OOP, testing, and optimisation for complex bioinformatics tasks. *Python for Biological Data Exploration and Visualization <https://prstats.org/course/python-for-biological-data-exploration-and-visualization-pybd01/>* Explore and visualise biological data in Python using pandas and seaborn. Ideal for applied researchers. *Single cell RNA-Seq analysis <https://prstats.org/course/single-cell-rna-seq-analysis-scrn02/>* 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) <https://prstats.org/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 <https://prstats.org/course/bayesian-modelling-using-r-inla-bmin03/>* 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) <https://prstats.org/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) <https://prstats.org/course/movement-ecology-the-analysis-of-movement-data-move09/>* 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) <https://prstats.org/course/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) <https://prstats.org/course/bioacoustics-data-analysis-biac06/>* Analyse animal acoustic signals in R. Learn spectrograms, annotations, and bioacoustic workflows. *Network Analysis for Ecologists (NWAE02) <https://prstats.org/course/network-analysis-for-ecologists-nwae02/>* Use R to analyse ecological networks. Learn metrics, simulation, and visualisation with igraph. And 15% off all recorded courses <https://prstats.org/recorded-courses/> (use MAM15) -- Oliver Hooker PhD. PR stats