This course is taught as a set of modules. Each module utilizes long-term, high-frequency, and sensor-based datasets from sources like the Global Lakes Ecological Observatory Network, the United States Geological Survey, and the Long Term Ecological Research Network, and the National Ecological Observatory Network. Modules can be adapted for any skill level to enhance students’ understanding of macrosystems ecology, computational skills, and ability to conduct inquiry-based studies.
Live Modules include:
This is a short-course style learning opportunity covering statistics in the context of soil science. Topics covered include: data and R (tabular and spatial data), exploratory data analysis, sampling, numerical taxonomy, uncertainty & validation, linear regression, generalized linear models, and tree-based models. This course provides lecture notes, exercises and projects for the user to learn and practice these topics.
Click here to begin learning!
Statistical Inference via Data Science: A ModernDive into R and the tidyverse is an intro to stats textbook that teaches students how to wrangle data. It provides students the whole data analysis pipe line, incorporates contemporary, user-friendly R packages directly into text, and emphasizes models that prepare students for our multivariate world. R packages such as dplyr, tidyverse, ggplot2, and infer are focused on in this course.
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This website, developed by the University of New South Wales, offers a brief introduction to techniques for data organization, graphics, and analyses. Topics covered include: an introduction to R, data manipulation, coding skills, graphical techniques, and statistics.
To access these materials, click here.
To access the raw markdown files on GitHub, click here.
This course compiles the lectures, tutorials, and materials of PLS205 taught by Daniel Runcie and focuses on implementing analyses in R. The goal of this course is to introduce graduate students in the biological sciences to the fundamental concepts and introductory statistical methods necessary to plan, conduct, and interpret experiments. It will be most accessible to students who have taken an introductory stats course (like PLS120 or STA 100.) The material is grouped into weekly blocks consisting of two lectures (1.5 hours each), one lab (1 hour), and one homework.
Biology 501 is a graduate level course taught at the University of British Columbia on quantitative methods for data analysis in ecology and evolution. Composed of lectures and discussions, this content covers methodological topics and practical workshops using the R package.
These R video tutorials are organized into 5 different series with the overall goal of using R to implement standard sets of analytic procedures learned in introductory and intermediate applied statistics course.
A guided analysis tutorial using the Seurat clustering workflow– featuring new computational methods for single-cell datasets. Implements QC and data filtration, calculation of high-variance genes, dimensional reduction, graph-based clustering and the identification of cluster markers.
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today’s model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated.
Book available for purchase here
More information about the book
Dr. Richard McElreath’s resourceful YouTube lecture series focuses on providing an in-depth walk through of Bayesian statistics.