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SDMR06

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

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

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

  • Duration: 5 Days, 8 hours per day
  • Next Date: September 22-26 2025
  • Format: Live Online Format

£480Registration Fee

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

Delivered remotely (Portugal) , Portugal

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

£430.00 – £480.00
SSTM02

Spatial and Spatial-Temporal Modelling Using R-INLA

Spatial and Spatial-Temporal Modelling Using R-INLA

Bayesian modelling of spatial data using R-INLA. Learn to fit, interpret, and visualise spatio-temporal models.

  • Duration: 5 Days, 7 hours per day
  • Next Date: September 22-26, 2025
  • Format: Live Online Format

£500Registration Fee

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Spatial and Spatial-Temporal Modelling Using R-INLA

Delivered remotely (United Kingdom) Western European Time, United Kingdom

Bayesian modelling of spatial data using R-INLA. Learn to fit, interpret, and visualise spatio-temporal models.

BIAC05

Bioacoustics Data Analysis

Bioacoustics Data Analysis

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

  • Duration: 5 Days, 4 hours per day
  • Next Date: October 6-10, 2025
  • Format: Live Online Format

£400Registration Fee

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Bioacoustics Data Analysis

Delivered remotely (United Kingdom) Western European Time, United Kingdom

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

£360.00 – £400.00
SNKM01

Introduction to Snakemake

Introduction to Snakemake

Learn Snakemake to automate data workflows. Build reproducible, scalable pipelines for research with hands-on training in this 4-day live online course.

  • Duration: 4 Days, 3 hours per day
  • Next Date: October 6-17, 2025
  • Format: Live Online Format

£350Registration Fee

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Introduction to Snakemake

Delivered remotely (United Kingdom) Western European Time Zone, United Kingdom

Learn Snakemake to automate data workflows. Build reproducible, scalable pipelines for research with hands-on training in this 4-day live online course.

IMLR04

Introduction to Machine Learning

Introduction to Machine Learning

Learn machine learning in R with hands-on training. Covers supervised and unsupervised models, tuning, evaluation, and interpretability.

  • Duration: 5 Days, 8 hours per day
  • Next Date: October 13-17, 2025
  • Format: Live Online Format

£450Registration Fee

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Introduction to Machine Learning

Delivered remotely (United Kingdom) Western European Time Zone, United Kingdom

Learn machine learning in R with hands-on training. Covers supervised and unsupervised models, tuning, evaluation, and interpretability.

£450.00
BMME01

Bayesian Multilevel Modelling using brms for Ecologists (COURSE FULL)

Bayesian Multilevel Modelling using brms for Ecologists (COURSE FULL)

Master Bayesian multilevel models in R with brms. Learn GLMs, priors, spatial/temporal autocorrelation, and species distribution modelling.

  • Duration: 10 Days, 4 hours per day
  • Next Date: October 20-24 & 27-31, 2025
  • Format: Live Online Format

£450Registration Fee

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Bayesian Multilevel Modelling using brms for Ecologists (COURSE FULL)

Delivered remotely (United Kingdom) Western European Time Zone, United Kingdom

Learn SEM and causal inference in R. Use DAGs, latent variables, and path models for ecological analysis.

£450.00

MSMS05

Model Selection and Model Simplification (COURSE FULL)

Model Selection and Model Simplification (COURSE FULL)

Model Selection and Simplification in R – a live online course covering model fit, nested model comparison, cross-validation, information criteria (AIC/BIC), and variable selection methods including stepwise, ridge, Lasso, and elastic net.

  • Duration: 2 Days, 4 hours per day
  • Next Date: November 3-4, 2025
  • Format: Live Online Format

£250Registration Fee

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Model Selection and Model Simplification (COURSE FULL)

Delivered remotely (United Kingdom) Western European Time Zone, United Kingdom

Model Selection and Simplification in R – a live online course covering model fit, nested model comparison, cross-validation, information criteria (AIC/BIC), and variable selection methods including stepwise, ridge, Lasso, and elastic net.

£250.00
NWAE01

Network Analysis for Ecologists

Network Analysis for Ecologists

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

  • Duration: 2 Days, 3 hours per day
  • Next Date: November 4-5, 2025
  • Format: Live Online Format

£250Registration Fee

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Network Analysis for Ecologists

Delivered remotely (United Kingdom) Western European Time Zone, United Kingdom

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

£250.00
VSED01

Visualizing Spatial Ecological Data

Visualizing Spatial Ecological Data

Learn to visualise spatial ecological data in R. Explore remote sensing, species distributions, temporal patterns, and colour-safe scientific graphics.

  • Duration: 5 Days, 8 hours per day
  • Next Date: November 17-21, 2025
  • Format: Live Online Format

£480Registration Fee

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Visualizing Spatial Ecological Data

Delivered remotely (Portugal) , Portugal

Learn to visualise spatial ecological data in R. Explore remote sensing, species distributions, temporal patterns, and colour-safe scientific graphics.

APCD01

Analysis of Avian Point-Count Data in the Presence of Detection Error

Analysis of Avian Point-Count Data in the Presence of Detection Error

Analyse bird point-count data in R. Learn N-mixture, time-removal, and distance sampling models.

  • Duration: 3 Days, 4 hours per day
  • Next Date: November 18-20, 2025
  • Format: Live Online Format

£300Registration Fee

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Analysis of Avian Point-Count Data in the Presence of Detection Error

Delivered remotely (United Kingdom) Western European Time, United Kingdom

Analyse bird point-count data in R. Learn N-mixture, time-removal, and distance sampling models.

ZIMR01

Zero-Inflated Models (COURSE FULL)

Zero-Inflated Models (COURSE FULL)

A one-day live online course on zero-inflated models in R. Learn to model count data with excess zeros using ZIP, ZINB, and hurdle approaches, plus model diagnostics and interpretation.

  • Duration: 1 Days, 6 hours per day
  • Next Date: December 1, 2025
  • Format: Live Online Format

£150Registration Fee

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Zero-Inflated Models (COURSE FULL)

Delivered remotely (United Kingdom) Western European Time Zone, United Kingdom

A one-day live online course on zero-inflated models in R. Learn to model count data with excess zeros using ZIP, ZINB, and hurdle approaches, plus model diagnostics and interpretation.

TBMR01

Tree-Based Models (COURSE FULL)

Tree-Based Models (COURSE FULL)

Learn decision trees, random forests, and boosted models in R. This one-day live online course covers CART, bagging, boosting, and model interpretation for applied data analysis.

  • Duration: 1 Days, 6 hours per day
  • Next Date: December 2, 2025
  • Format: Live Online Format

£150Registration Fee

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Tree-Based Models (COURSE FULL)

Delivered remotely (United Kingdom) Western European Time Zone, United Kingdom

Learn decision trees, random forests, and boosted models in R. This one-day live online course covers CART, bagging, boosting, and model interpretation for applied data analysis.

£150.00
MMIE01

Introduction to Generalised Linear Mixed Models for Ecologists

Introduction to Generalised Linear Mixed Models for Ecologists

Model hierarchical ecological data using GLMMs in R. Covers lme4, brms, and Bayesian methods for ecologists.

  • Duration: 5 Days, 8 hours per day
  • Next Date: December 8-12, 2025
  • Format: Live Online Format

£450Registration Fee

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Introduction to Generalised Linear Mixed Models for Ecologists

Delivered remotely (United Kingdom) Western European Time Zone, United Kingdom

Model hierarchical ecological data using GLMMs in R. Covers lme4, brms, and Bayesian methods for ecologists.