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.
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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.
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£300.00
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1 event,
Machine Learning for Ecological Time Series is an applied R course teaching ecologists how to analyse, model, and predict ecological time series data.
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£450.00
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2 events,
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.
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£300.00
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2 events,
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.
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£350.00
Analyse ecological community data in R using VEGAN. Learn ordination, clustering, and multivariate statistics with real datasets.
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£485.00
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3 events,
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.
£300.00
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4 events,
Species Distribution and Ecological Niche Modelling is an applied R course teaching standard workflows for building, evaluating, and interpreting SDMs.
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£275.00
Learn mechanistic species distribution and ecological niche modelling with NicheMapR in R. Hands-on live online course with microclimate modelling.
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£150.00
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2 events,
Causal Inference for Ecologists is an applied R course teaching researchers how to identify and estimate causal effects in ecological and environmental data.
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£400.00
Stable Isotope Mixing Models in R is an applied course teaching ecologists how to use SIBER, SIAR, and MixSIAR for dietary analysis.
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£480.00
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3 events,
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.
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£300.00
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