curated by UC Davis College of Biological Sciences
Powered by Coursera, this approximately 60 hour online course will guide students through programming and problem solving in R. Statistical analysis of data sets, debugging, and code organization are just a few topics that are covered in this in-depth program.
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 repository contains many of the course materials (for free!) from the Bio720 course taught by Brian Golding and Ian Dworkin at McMaster University in 2018. While this course is structured for graduate students, anyone who has a working knowledge of basic molecular biology and genetics should be able to use this course. The goal of this course is to allow the user to develop fundamental computational skills that will prepare them to further develop skills appropriate for bioinformatics and genomics.
To learn more about this course, click here.
This is a set of lectures, exercises, and challenges using R. Topics covered include: an introduction to comparative methods and R, Brownian motion, PGLS, ancestral state reconstruction, trait evolution, diversification with BAMM and RPanda, state dependent diversification, Mk models and character correlations, and placing fossils/recently extinct taxa on a tree.
Arbor is an R software package that can be used to build workflows for phylogenetic comparative analysis. This tutorial set showcases how to use Arbor and how to run analyses using Arbor. Common analyses for which you can use Arbor include: phylogenetic signal, ancestral state reconstruction, independent contrasts, comparative model fitting, PGLS, fitting birth-death models, community phylogenetics, BiSSE/MuSSE/QuaSSE/GeoSSE, and cospeciation. Click here to begin!
Here is a collection of RStudio “Cheatsheets” that provide an at-a-glance look at R packages and their related functions. View cheatsheets for dplyr, ggplot2, RStudio IDE and more.
These course notes emphasize understanding the underlying structure of generalized linear mixed models including regression, ANOVA, animal models, threshold models, meta-analysis, MANCOVA and random regression.This course also aims to show how these models can be fitted in a Bayesian framework using Markov chain Monte Carlo (MCMC) methods in the MCMCglmm R package.
Access course note 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.
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