Cornell Carpentries Curriculum

This is a custom curriculum for Carpentries workshops at Cornell which is borrowed and adapted from lessons by the University of Michigan Carpentries. While it is based on The Carpentries curriculum and teaching practices, it is not officially overseen by The Carpentries organization. We welcome any feedback or questions to us through GitHub.

The curriculum is developed for complete beginners interested in learning reproducible data science techniques and integrates instruction of R for data cleaning, analysis, and visualization; the Unix shell; and git and GitHub. For more details, please see Introduction to the Workshop.

If you are interested in contributing to the curriculum, please read over The Carpentries Curriculum Development Handbook and our contribution guidelines. If you would like to set up your machine to preview changes locally, please see the setup instructions.

Schedule

	Setup	Download files required for the lesson
00:00	1. Introduction to the Workshop	What is The Carpentries? What will the workshop cover? What else do I need to know about the workshop?
00:15	2. R for Plotting	What are R and R Studio? How do I write code in R? What is the tidyverse? How do I read data into R? What are geometries and aesthetics? How can I use R to create and save professional data visualizations?
03:05	3. The Unix Shell	What is a command shell and why would I use one? How can I move around on my computer? How can I see what files and directories I have? How can I specify the location of a file or directory on my computer? How can I create, copy, and delete files and directories? How can I edit files?
04:50	4. Intro to Git & GitHub	What is version control and why should I use it? How do I get set up to use Git? How do I share my changes with others on the web? How can I use version control to collaborate with other people?
06:50	5. R for Data Analysis	How can I summarize my data in R? How can R help make my research more reproducible? How can I combine two datasets from different sources? How can data tidying facilitate answering analysis questions?
09:35	6. Writing Reports with R Markdown	How can I make reproducible reports using R Markdown? How do I format text using Markdown?
11:50	7. Conclusion	What do I do after the workshop to apply what I learned and keep learning more? Where can I learn more coding skills? How do I deal with coding errors (i.e. debug)? What other coding resources do we have at Cornell? What other coding concepts should I learn?
12:05	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.