University Courses

  • Data Challenge Lab – Stanford University; Hadley Wickham and Bill Behrman. This is a 5-unit course using a flipped classroom. The curriculum is designed to cover each main thread of R4DS multiple times, diving a little deeper at each pass.
  • Better Living with Data Science – Duke University; Mine Cetinkaya-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.
  • Statistical Computing – Duke University; Colin Rundel. MS level statistical computing course focusing on Best practices and software development for reproducible results, selecting topics from: use of markup languages, understanding data structures, design of graphics, object oriented programming, vectorized code, scoping, documenting code, profiling and debugging, building modular code, and version control-all in contexts of specific applied statistical analyses.
  • Computing for the Social Sciences – University of Chicago; Benjamin Soltoff. This is an applied course for social scientists with little-to-no programming experience who wish to harness growing digital and computational resources. The focus of the course is on generating reproducible research through the use of programming languages and version control software. Major emphasis is placed on a pragmatic understanding of core principles of programming and packaged implementations of methods. Students will leave the course with basic computational skills implemented through many computational methods and approaches to social science; while students will not become expert programmers, they will gain the knowledge of how to adapt and expand these skills as they are presented with new questions, methods, and data.
  • Applied Media Analytics – Elon University; Brian Walsh. An Undergraduate introduction to R programming for Media Analytics majors. Students learn ggplot2, dplyr, and lubridate, as well as basic sentiment analysis, Twitter insights, and Google Analytics.
  • Intro to R Bootcamp – Intro to R Bootcamp by Brad Boehmke.
  • Data Wrangling with R – Data Wrangling with R by Justin Jodrey.