Introduction to GitHub

GitHub is a place where millions of developers gather every day to collaborate on open source software. It hosts billions of lines of code. This course will give a step-by-step tutorial for beginners to get started with git and Github. Learn all the important pieces of GitHub that you should know.

Colors in Data Visualization

This workshop will discuss how to select colors that complement the message of your data visualization without sacrificing readability. We will also review best practices for using color to encode numerical values and explore how color can be used to accent information and establish a clear visual hierarchy.

Using Rice’s Private VM Cloud

Learn about ORION, Rice’s private VM research computing cloud. ORION allows you to develop custom disk images, perform custom compute jobs that do not rise to the level of requiring supercomputing resources, and even host web applications. ORION also works very effectively with Rice’s other networked computing resources, such as networked storage and our supercomputing clusters. In this workshop, we will introduce you to the infrastructure, to its web interface for provisioning resources, and to some use cases such as post-processing data and hosting custom websites.


Pandas is one of the most useful Python libraries, used for the creation, manipulation, and analysis of data tables. Attendees will learn the fundamentals of table creation and manipulation in Pandas.


Introduction to Data Management

Drowning in data? Not sure how to organize and back it up? This hands-on, interactive workshop will share tips for effectively organizing, documenting and storing research data. Participants will walk away with ideas for completing a data management plan, naming and organizing files, and safely storing data. We’ll also explore some of the features of Box, a cloud-based storage and collaboration platform used by Rice University.


R Visualization and Data Manipulation

This course will build upon the basics of R and introduce basic forms of data visualization techniques in R as well as more advanced forms of data manipulation that weren't covered in the introductory class through use of for loops and if statements. Individuals will be able to perform data analysis and visualization processes on large data sets as well as clean data to perform more efficient analysis by the end of this course.