Statistics and Data Management Course Series at SMSC in 2026 (Deadlines approaching!)
NZCBI-SCBI Training
The application deadline (Dec 8) is fast approaching for the first of our 2026 course offerings at the Smithsonian-Mason School of Conservation. We expect these courses to fill to capacity before the application deadline, so we encourage anyone interested to apply and register as soon as possible to secure your seat. Remember that only registration (not course acceptance) guarantees your place in the course. Scholarships are available for applicants of less-developed nations. Just tick the relevant box in your application and if you are eligible you will be considered. You statement of interest will be considered a major factor in your scholarship application review.
Format: All three courses are taught in an asynchronous format over either 8 or 15 weeks, with weekly opportunities for live feedback and instructor interaction over Zoom. This format allows for maximum flexibility for participants juggling complicated work schedules, classes and other commitments. All three courses are available for professional training, but can also be taken for 3 graduate credits at additional cost through George Mason University.
Description: An understanding of statistics and study design is essential to success in the fields of ecology and conservation. However, many of the analyses of greatest utility for ecological data are not addressed in introductory courses, while advanced courses often delve deeply into a limited set of techniques. This course, now available to professionals, helps to bridge this gap: by covering both traditional core statistical techniques as well as newer advancements such as mixed effects models, all focusing on common data types in these disciplines. Participants will develop skills in data manipulation and analyses using the R statistical computing environment, a popular and powerful tool commonly used in ecology.
Website: https://smconservation.gmu.edu/programs/graduate-and-professional-2/ecological_data-ol/
Description: Wrangling and manipulating datasets can be one of the biggest hurdles to overcome in analyzing and publishing your research. In many cases, researchers and students spend more time organizing, formatting and cleaning data then they do actually using it to answer their research questions. Yet there are many tools available, particularly in the R environment, to make this stage of your research efficient, and error-free. In addition, R provides numerous tools to streamline and professionalize communication of your results. As big data increasingly becomes a component of ecological study, there is a developing need for understanding how to maintain large and complex datasets, prepare data for analysis, and develop a reproducible workflow.
Dr. Brian Evans has designed this course to create a flexible toolbox for ecologists and environmental scientists who seek to better manage and use data. Over 8 weeks participants will explore the management of ecological data using Program R. They will focus on the structure and linguistics of data in R, how to integrate R into a modern data science workflow, and explore how to think about ecological data in new ways. Participants will gain an in-depth understanding of the tidyverse package. Through this process, participants will develop a flexible skillset for managing and exploring data. Each weekly module will consist of recorded lectures and guided lab activities using real world ecological applications. R packages used in the course will include tidyr, dplyr, lubridate, stringr, and purr among others. See our course page for a complete list of course topics. Note that this course is not recommended for those not familiar with working in the R environment.
Generalized Linear and Mixed Models in Ecology and Conservation Biology - ONLINE
Dates: March 16 – May 10, 2026
Website: https://smconservation.gmu.edu/programs/graduate-and-professional-2/glm_ecology-ol/
Cost: 600.00 USD
Deadline: February 2, 2026
Description: This course provides an overview of modern regression-based statistical analysis techniques relevant to ecological research and applied conservation, starting with basic linear models and moving quickly to generalized linear models (GLMs) and mixed models. The course aims to provide a robust understanding of the wide range of regression approaches available, the assumptions associated with each, and the circumstances under which each should be applied. Models covered enjoy widespread use in ecology and conservation biology and can be applied to a huge diversity of data types, study designs, and research questions. Emphasis is placed not only on proper implementation of models, but also on interpretation and explanation of results, recognizing uncertainty, and model limitations. Participants will conduct all exercises using R, a free software environment for statistical computing and graphics which has now become the standard in this field. All exercises and demos will use real ecological data sets and participants will complete an independent analysis project on a unique assigned dataset during the last week of the course. Some basic familiarity with R is recommended before attending.