WEST specializes in the design, conduct, and analysis of ecological studies. We have offered workshops and trainings internationally on topics ranging from ecological sampling and study design, fundamental and sophisticated statistical analyses, and data management and computer programming for practitioners. WEST is flexible in adapting the format and content of training to your specific interests and needs.
See our list of upcoming workshops, or reach out to firstname.lastname@example.org to arrange a workshop not currently on the calendar.
Bayesian methods are increasingly used to analyze data in ecology and related disciplines and have some advantages over classical (frequentist) methods many scientists are familiar with. The aim of this workshop is to introduce participants to Bayesian statistics, with the expectation that participants will become better-equipped consumers and producers of scientific inference based on Bayesian methods of data analysis. The workshop will focus on applied tutorials built around simple and familiar statistical models. Discussions of the underlying philosophy, computational methods, and mathematical theory will be brief. Participants will use Bayesian methods to calculate basic summary statistics (mean, proportion), then advance to fitting Bayes regression models (linear regression, generalized linear models [GLM], and mixed models [GLMM]. Hands-on exercises will be run in Program R using its interface to JAGS. We expect that participants will leave with an increased fluency to evaluate research based on Bayesian statistics and with an increased capacity to learn and apply Bayesian methods in their own research.
Dr. Andrew Tredennick, Biometrician
Dr. Jared Studyvin, Statistican
Dr. Jason Carlisle, Research Biometrican
Students and practicing data scientists, especially those familiar with basic statistical concepts but with little to no experience with Bayesian statistics. Participants that already know how to fit and interpret non-Bayesian linear regression models in Program R will benefit the most. Because this workshop is sponsored by the Wyoming EPSCoR Program, registration is only open to current students, faculty, or staff of the University of Wyoming or a Wyoming community college.
Live workshop conducted over Zoom; 8 hours total (4 sessions, each 2 hours)
Free. Because this workshop is sponsored by the Wyoming EPSCoR Program, registration is only open to current students, faculty, or staff of the University of Wyoming or a Wyoming community college.
Artificial neural networks (ANNs) present powerful tools for analyzing data, but their use in ecology is limited. This workshop will provide participants with the background and skills necessary to program ANNs and train and evaluate them. Theoretical instruction will provide and introduction to the mechanisms behind neural networks and the steps to training and evaluation. Hands-on instruction will allow participants to program their own ANNs and understand how network architectures can be represented in python code. Participants will learn to program using a Pytorch (pytorch.org) framework, which is considered state-of-the-art for programming ANNs. The skills provided in this workshop will lay the groundwork for participants to begin advanced programming of ANNs including deep learning and computer vision.
Designed for those who have completed the “Introduction to Machine Learning with Python for Ecologists” workshop, or comfort with python and experience with machine learning
Program R is a free software environment for statistical computing and graphics (https://www.r-project.org/), and R is increasingly popular among scientists across many disciplines including environmental and ecological sciences. R is notorious for having a steep learning curve, so we offer this 4-hour workshop to provide a very basic introduction to Program R and its uses, and to guide beginner R users through what may be their first encounter with R via RStudio, a program that makes the power of R more approachable (http://www.rstudio.com/). Each participant must provide his/her own computer (with software-installation privileges), and instructions will be sent to participants in advance to download the free software used in the workshop. Example datasets and materials will be distributed ahead of the workshop. Multiple assistant instructors will be on hand to assist with troubleshooting any issues that arise.
Designed for pre-beginner R users (those who may have never seen Program R) and beginner R users (those who may have dabbled lightly and would like to learn more fundamentals)
Machine learning is a useful tool for ecologists to manage and analyze data. This introductory workshop will provide the skills needed to begin working with data, and training and evaluating machine learning algorithms in python. We will include a brief introduction to python and anaconda, data management, regression, classification, random forests, model selection, and more. While theoretical introductions to each topic will be provided, the main focus will be on practical applications, as workshop participants will be programming along with instructors. Following this workshop, participants will be ready for our next workshop in artificial neural networks. The instructors plan to be available for an extra 30 minutes after each session as needed.
Designed for those with at least some programming experience (in any language) and some familiarity with basic statistics (like linear regression).