prstats

Specialist training in applied statistical methods for researchers in ecology, biology, and environmental sciences.
Courses are taught by leading academics

[prstats @pr_stats]

Live online course on time series modelling & forecasting, taught by Mark Andrews, Senior Lecturer & experienced instructor in Bayesian data analysis & statistical computing

11–15 May
https://t.co/KwBpeQB6ZA

#PhDStudents #TimeSeries #MachineLearning #DataScience #RStats

[prstats @pr_stats]

Live online course on movement ecology & the analysis of telemetry data, taught by leading ecologists working at the interface of behaviour, habitat selection, & biodiversity dynamics

11–15 May
https://t.co/NcbItCREUe
#MovementEcology #QuantitativeEcology #SpatialEcology #RStats

[prstats @pr_stats]

Live online course on Bayesian statistical modelling with Stan & brms, taught by Mark Andrews, Senior Lecturer & experienced instructor in applied Bayesian methods

5–7 May 2026
Details: https://t.co/yncySc9BzM

#PhDStudents #BayesianStatistics #Stan #RStats #DataScience

[prstats @pr_stats]

Live online course on scalable Bayesian modelling using INLA, taught by Virgilio Gómez-Rubio, author of Bayesian Inference with INLA and leading contributor to Bayesian & spatial modelling in R

4–8 May
https://t.co/BtNSRP2zzb

#BayesianStatistics #INLA #RStats #SpatialStatistics

[prstats @pr_stats]

Turn your R analyses into interactive research
Live course building interactive data applications with Shiny, taught by Mark Andrews, Senior Lecturer in Bayesian modelling & statistical computing

28–29 Apr
https://t.co/M1Bl5mHFyr

#RStats #Shiny #DataVisualization #DataScience

[prstats @pr_stats]

Live online course on stable isotope mixing models using SIBER, SIAR, & MixSIAR, taught by Andrew Parnell, Hamilton Professor of Statistics with extensive expertise in Bayesian modelling.

27–30 Apr
https://t.co/CWks3bAhr0

#StableIsotopes #BayesianStatistics #Ecology #RStats

[prstats @pr_stats]

Live online course on workflows for species distribution & ecological niche modelling, taught by Neftalí Sillero, a spatial ecologist with over 70 peer reviewed publications

23–24 Apr
https://t.co/uVHaeUDBiL

#SpeciesDistributionModelling #SpatialEcology #GIS #Biodiversity

[prstats @pr_stats]

Strengthen your ecological inference

Live course on analysing ecological data with imperfect detection, taught by Péter Solymos, developer of widely used R tools for ecological modelling

20–23 Apr
https://t.co/SEIQwz3fAQ

#Ecology #RStats #EcologicalStatistics #Biodiversity

[prstats @pr_stats]

Live online course on single cell RNA-seq analysis, taught by Frances Turner, a bioinformatician at the University of Edinburgh with experience supporting real research projects

16–19 March
https://t.co/4WxtgKC0ae

#PhDStudents #SingleCell #Bioinformatics #Genomics #DataScience

[prstats @pr_stats]

Live online course on advanced Python for biological research, taught by Martin Jones, founder of Python for Biologists and former Lecturer in Bioinformatics at University of Edinburgh

23–26 Mar

https://t.co/Oz7q9S5VSC
#PhDStudents #Python #Bioinformatics #ComputationalBiology

[prstats @pr_stats]

Live online course on deep learning using R, taught by Mark Andrews, Senior Lecturer in Psychology with extensive experience in Bayesian modelling, statistical computing, and machine learning

21–22 Apr
https://t.co/PnAEMIXyMI

#DeepLearning #RStats #MachineLearning #DataScience

[prstats @pr_stats]

Live online course on community ecology analysis using vegan in R, taught by Antoine Becker-Scarpitta, a community and forest ecologist with extensive experience in applied ecological modelling.

20–24 April
https://t.co/WIgn1tsKp3

#PhDStudents #Ecology #RStats #Biodiversity

[prstats @pr_stats]

Deep learning with real data

Live online course on deep learning using Python, taught by Mark Andrews, an experienced instructor in applied modelling and computational methods

14–15 Apr
https://t.co/7TUKisX4TO

#PhDStudents #DeepLearning #Python #MachineLearning #DataScience

[prstats @pr_stats]

Python for data science and statistical computing in PhD research.

Taught by Mark Andrews, Senior Lecturer in Psychology and long standing instructor in applied Bayesian data analysis.

7–8 April
https://t.co/tKrb4CXyhe

#PhDStudents #Python #DataScience #BayesianStatistics

[prstats @pr_stats]

Live online Python course for biological data exploration and visualisation, taught by Martin Jones, founder of Python for Biologists and former Lecturer in Bioinformatics at Edinburgh University

2–5 Mar
https://t.co/S4iOWpcZaj

#PhDStudents #Python #Bioinformatics #DataScience

[prstats @pr_stats]

Live online course on Bayesian modelling with R-INLA, taught by Virgilio Gómez-Rubio, author of Bayesian Inference with INLA and a leading contributor to the R Bayesian ecosystem.

23–27 Feb
Details: https://t.co/fOkZva63YY

#PhDStudents #BayesianStatistics #RStats #DataScience

[prstats @pr_stats]

Deep Learning Python course 12–13 Feb. A practical introduction to neural networks, model training, and applied deep learning workflows. Led by Dr Mark Andrews, an instructor in data science and machine learning.

https://t.co/7TUKisWx4g

#DeepLearning #Python #MachineLearning

[prstats @pr_stats]

Spatial Multiplexed Proteomics Data course 9 to 13 Feb

Intro to processing fluorescence microscopy taught by Dr Victor Perez Meza, an image analysis specialist focused on artefact correction
https://t.co/PR1EiGbNmE

#SpatialProteomics #SpatialOmics #ImageAnalysis #Bioinformatics

[prstats @pr_stats]

Our Python for Data Science and Statistical Computing course runs 9 to 10 Feb. A practical introduction to data manipulation, visualisation, and statistical workflows

Taught by Dr Mark Andrews.

https://t.co/tKrb4CXyhe

#Python #DataScience #StatisticalComputing #ResearchSkills

[prstats @pr_stats]

GLMM for Ecologists course 2-6 Feb.

An introduction to building mixed models in R. Taught by Dr Andrew MacDonald, a statistician focused on connecting ecological theory with expressive statistical models

https://t.co/Z20fsSAQAr

#GLMM #Ecology #RStats #MixedModels #DataAnalysis