Inspired by the Using Julia in the classroom page and suggestion by @Peter_Griffin's post on RStudio Community as well as the Learn the tidyverse page.
This is a user curated list and is bound to be non-comprehensive at all times. If you have suggestions for courses to add, please submit a pull request or add an issue.
- Statistics with R Coursera Specialization; Mine Çetinkaya-Rundel, Merlise Clyde, Colin Rundel, David Banks. 5 courses: Introduction to Data and Probability, Inferential Statistics, Linear Regression and Modeling, Bayesian Statistics, and Capstone.
- DataCamp's R offerings: DataCamp offers interactive R (and Python, Sheets, SQL, and shell) courses on topics in data science, statistics and machine learning following a "learn by doing" philosophy. Courses run interactively in the browser.
- Better Living with Data Science - Duke University; Mine Çetinkaya-Rundel. Data Science course for first year undergraduates with little to no computing background. Combines techniques from statistics, math, computer science, and social sciences, to learn how to use data to understand natural phenomena, explore patterns, model outcomes, and make predictions. Data wrangling, exploratory data analysis, predictive modeling, data visualization, and effective communication of results. Discussions around reproducibility, data sharing, data privacy.
- Data Science for Public Management - Brigham Young University; Andrew Heiss. Data science and statistics class for Master of Public Administration (MPA) students with little math or computing experience. Uses ModernDive, DataCamp, R for Data Science, and OpenIntro Statistics to cover tidyverse data wrangling, inference, and hypothesis testing. All projects and in-class examples use data related to public affairs, administration, and policy.
- Data Visualization - Brigham Young University; Andrew Heiss. Data visualization class for Master of Public Administration (MPA) students with some experience with R. Uses Alberto Cairo's The Truthful Art: Data, Charts, and Maps for Communication, Kieran Healy's Data Visualization: A Practical Introduction, Claus Wilke's Fundamentals of Data Visualization, and R for Data Science to cover principles of graphic design and fundamentals of visualizing data with ggplot2.
- Reproducible & Collaborative Data Science - UC Berkeley, Carl Boettiger. Data Science course for first year graduate students in both the natural and social sciences. A modular, flipped-classroom approach that combines reading, exercises and videos based on R for Data Science and DataCamp with more open-ended assignments to replicate, extend, and sometimes challenge key results from the scientific literature on global change. Note: an upper-division undergraduate version of the course is also being developed under the title Data Science for Global Change Ecology