Introduction to Generalised Linear Models for Ecologists
Delivered remotely (United Kingdom) Western European Time Zone, United KingdomMaster the fundamentals of Generalised Linear Models in R
Master the fundamentals of Generalised Linear Models in R
Field Mapping and Species Identification for Ecologists – hands-on training in field data collection, GIS integration, and ecological survey methods.
£0Registration Fee
View DetailsLearn R and RStudio from scratch with this free 6-hour recorded course—no coding experience needed.
£0Registration Fee
View DetailsOnline course introducing Generalised Linear Models (GLMs) in R. Ideal for ecologists and applied scientists working with binary or count data.
£480Registration Fee
View DetailsMaster ecological spatial visualisation using R: remote sensing, species distributions, color-safe maps, and more.
£450Registration Fee
View DetailsLearn remote sensing data analysis in R. Gain hands-on skills in coding, spatial data, ecosystem monitoring, and ecological change detection.
£450Registration Fee
View DetailsMaster GLMMs and LMMs in R with this 40-hour on-demand course. Ideal for ecologists handling grouped or hierarchical data. Learn at your own pace.
£480Registration Fee
View DetailsLearn advanced SDM and ENM techniques in R. Includes Maxent tuning, MESS and null models, and building mechanistic models and virtual species.
£480Registration Fee
View DetailsLearn advanced SDM and ENM techniques in R. Includes Maxent tuning, MESS and null models, and building mechanistic models and virtual species.
£480Registration Fee
View DetailsMovement Ecology in R: Analyse GPS, VHF, and Path Data Across Species
£400Registration Fee
View DetailsLearn how to analyse movement, acceleration, and other ecological time series using HMMs in R with moveHMM and momentuHMM.
£450Registration Fee
View DetailsOnline SEM and causal inference course for biologists. Learn path analysis, d-separation, latent variables, and multigroup models using R.
£225Registration Fee
View DetailsLearn how to analyse species interactions and ecological networks using R and igraph in this focused 6-hour course.
£400Registration Fee
View DetailsAnalyse animal acoustic signals in R. Learn spectrograms, annotations, and bioacoustic workflows.
£450Registration Fee
View DetailsLearn to analyse avian point-count data with R, accounting for detection error using models like N-mixture, distance sampling, and time-removal.
£250Registration Fee
View DetailsBayesian Regression & Mixed Models in R with brms – On-Demand Course
£350Registration Fee
View DetailsBayesian Data Analysis in R – On-Demand Course Using brms
£0Registration Fee
View DetailsRecorded course on spatial data visualisation and mapping using tmap in R, led by the package author. Learn mapping skills at your own pace.
£350Registration Fee
View DetailsCreate Beautiful Maps in R – Static, Interactive & Animated
£350Registration Fee
View DetailsLearn Bayesian hierarchical modelling with R, JAGS & Stan. A 24 hour on-demand course ideal for scientists and analysts working with complex structured data.
£450Registration Fee
View DetailsVisualize spatial data in R with tmap. Learn to create static and interactive maps using sf, terra, and stars in this 20-hour on-demand course.
£450Registration Fee
View DetailsLearn Bayesian multilevel modeling for ecological data in R using brms. Covers GLMs, priors, spatial/temporal models, species distributions in this 40 hour on-demand course.
£250Registration Fee
View DetailsLearn Stan from scratch and build custom Bayesian models with rstan and cmdstanr in R.
£350Registration Fee
View DetailsUpdate Your R Spatial Skills: From sp/raster to sf/terra/PROJ7
£450Registration Fee
View DetailsLearn machine learning in R with practical, hands-on instruction. Covers supervised, unsupervised models, interpretability, and evaluation in 40 hours.
£450Registration Fee
View DetailsAdvance your machine learning skills in R with deep learning, Bayesian methods, transformer models, clustering, and anomaly detection in this 28-hour live online course.
£450Registration Fee
View DetailsMachine learning using Python: supervised and unsupervised learning, neural networks, and practical model building with scikit-learn and TensorFlow.
£450Registration Fee
View DetailsMachine vision using Python: apply deep learning and computer vision with OpenCV and TensorFlow for real-world image classification and ecological data applications.