Course Overview: This course will provide an introduction to working with real-life data typical of those encountered in the field of evolutionary biology and ecology. The course will be delivered by Dr. Luc Bussiere, Dr. Tom Houslay and Dr. Ane Timenes Laugen who are all practicing academics in the field of evolutionary biology.  This five day course will consist of series of modules (each lasting roughly half a day) covering model selection and simplification, generalised linear models, mixed effects models,  and non-linear models. Along…

Course Overview: Acoustic telemetry has become increasingly popular as a method of choice for monitoring the movements and behaviour of aquatic animals globally.  Increasingly smaller tags along with improvements in battery technology have allowed for tagging a wide variety of species and life stages, enabling monitoring of individuals as small as salmon smolts and as large as whale sharks for periods from 30 days to 10 years.  Receiver technology also continues to evolve and increasingly allows for data to be…

Course Overview: The term ‘landscape genetics’ has been applied studies that integrate ecological context and intervening landscape into population genetic analyses of contemporary processes such as gene flow and migration.  This course will cover the basics of both quantitative landscape ecology and population genetics, focusing on how we develop and evaluate spatial/genetic analyses using the R platform. Intended Audience This course is suitable for graduate students, postdoctoral researchers, and primary investigators interested in learning how to integrate landscape ecological and…

Course Overview: This course will provide an introduction to working with real-life data typical of those encountered in the field of psychology. The course will be delivered by Dr. Dale Barr and Dr. Luc Bussière, who are practicing academics in the fields of psychology and evolutionary biology respectively, with many years of expertise with R and statistical modelling as both scientists and instructors.  This five-day course will consist of series of modules (each lasting roughly half a day) covering topics including the…

Course Overview: This workshop will provide attendees with the opportunity to learn how (1) to understand; (2) run simulations of readily available Individual Based Models (IBMs); (3) analyze the outputs of IBMs; (4) parameterize IBMs, and (5) code relatively basic IBMs in R and NetLogo. Examples and applications will include cases from diverse domains and scientific disciplines such as social sciences, biology, and environmental sciences. Intended Audience This workshop is ideal for any scientists seeking an introduction as well as…

Course Overview: This workshop will provide attendees with the opportunity to learn how to use machine learning to analyze data coming from diverse data domains and scientific disciplines and without necessarily prior knowledge of the scientific discipline where the data are coming from. Attendees will learn how to use R and several R packages including classification and regression trees, ensembles and bagging, random forests, neural networks, and boosting (learning from mistakes). These techniques will be used for data classification, determining…

Course Overview: Python is a dynamic, readable language that is a popular platform for all types of bioinformatics work, from simple one-off scripts to large, complex software projects. It gives an overview of the language with an emphasis on practical problem-solving, using examples and exercises drawn from various aspects of bioinformatics work. After completing the workshop, students should be in a position to (1) apply the skills they have learned to tackle problems in their own research and (2) continue…

Course overview: Python is a dynamic, readable language that is a popular platform for all types of bioinformatics work, from simple one-off scripts to large, complex software projects. This workshop is aimed at people who already have a basic knowledge of Python and are interested in using the language to tackle larger problems. In it, we will look in detail at the parts of the language which are particularly useful in scientific programming, and at the tools Python offers for…

Course Overview: This course will provide a theoretical, analytical and practical introduction to QIIME 2 (canonically pronounced ‘chime two’), which stands for Quantitative Insights into Microbial Ecology 2, and Qiita (canonically pronounced ‘cheetah’), a multiomics and multi-study online tool. QIIME 2 and Qiita are open source software packages for comparison and analysis of microbial communities, primarily based on high-throughput amplicon sequencing data (such as SSU rRNA) generated on a variety of platforms, but also supporting analysis of other types of…

Course Overview: This course will introduce and explain the different applications for bioacoustics to answer ecological questions. Starting with a detailed overview of the correct and most efficient methods of data collecting in the field, this course will then go on to show delegates cutting edge methods for analysing and interpreting different types of bioacoustic data. By the end of this 5-day practical course, attendees will have the capacity to set up and deploy recording devices, download acoustic data, how…

Course Overview: This course will cover introductory hierarchical modelling for real-world data sets from a Bayesian perspective. These methods lie at the forefront of statistics research and are a vital tool in the scientist’s toolbox. The course focuses on introducing concepts and demonstrating good practice in hierarchical models. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using the Bayesian modelling software Jags and Stan through the R…

Course Overview: To find out more or to book online via our sister company (PS statistics) use the link below… https://www.psstatistics.com/course/generalised-linear-glm-nonlinear-nlglm-and-general-additive-models-gam-gnam01/ “This was by far the most useful statistic-related course I’ve done to date and I enjoyed all aspects of the course. The course was useful because it put into prospective the ecology aspect within the statistics. All parameters were explained in an ecological manner which made the math aspect much clearer. It helped to change the view I’ve got…

Course Overview: To find out more or to book online via our sister company (PS statistics) use the link below… https://www.psstatistics.com/course/structural-equation-modelling-and-path-analysis-smpa01/ “This was by far the most useful statistic-related course I’ve done to date and I enjoyed all aspects of the course. The course was useful because it put into prospective the ecology aspect within the statistics. All parameters were explained in an ecological manner which made the math aspect much clearer. It helped to change the view I’ve got…

Course Overview: Coming soon... To find out more or to book online via our sister company (PS statistics) use the link below… https://www.psstatistics.com/course/data-science-analytics-using-python-dsap01/ “This was by far the most useful statistic-related course I’ve done to date and I enjoyed all aspects of the course. The course was useful because it put into prospective the ecology aspect within the statistics. All parameters were explained in an ecological manner which made the math aspect much clearer. It helped to change the view…

Course Overview: This 5-day course will cover the concepts, methods, and R tools that can be used to analyse community ecology data. The course will review data processing techniques relevant to multivariate data sets. We will cover diversity and null-model analysis, distance measures and distance based multivariate methods, clustering, classification, and dimension reduction techniques using the vegan R extension package. We will use real world data sets to motivate the analyses, e.g. describing patterns along environmental or anthropogenic disturbance gradients,…

Course Overview: This 5-day course will involve a combination of lectures and practical sessions. Students will learn to build and fit custom models for analysing behavioural data using maximum likelihood techniques in R. This flexible approach allows a researcher to a) use a statistical model that directly represents their hypothesis, in cases where standard models are not appropriate and b) better understand how standard statistical models (e.g. GLMs) are fitted, many of which are fitted by maximum likelihood. Students will…

Course Overview: This application-driven course will provide a founding in the basic theory & practice of Bayesian statistics, with a focus on MCMC modeling for ecological & epidemiological problems. Starting from a refresher on probability & likelihood, the course will take students all the way to cutting-edge applications such as state-space population modelling & spatial point-process modelling. By the end of the week, you should have a basic understanding of how common MCMC samplers work and how to program them,…