Data Literacy


Introduction to Python

The UVA Library’s Research Data Services Team and Research Computing are partnering with UVA’s PhD Plus program to offer Introduction to Programming in Python, a series to build data analysis skills. 

Check for more python training opportunities.


Data Foundations for Non-Specialists

This six-part workshop series is meant to give non-specialists a practical foundation for working with data and understanding approaches to answering questions with data that are rooted in statistical thinking. Topics covered: Data formatting, Intro to graphs, Intro to statistical thinking, Experimental design, Storytelling with your data, and Bridge to R. 


Essentials in R

This series is developed through collaboration with UVA Library's Research Data Services Group. Seminars are scheduled to meet weekly through lecturing, hands-on exercises, and discussions to help participants become familiar and comfortable in using R for research activities and working with data. Topics include: Getting Started with R, Preparing Data for Analysis, Visualizing Data, Essential Statistics, Models and Machine Learning, Creating Deliverables


Previous Offerings:

Statistical Analysis in R

In this series, developed through collaboration with UVA Health’s Health Sciences Library, participants learn to conduct and interpret output from basic statistical approaches using the statistical programming language, R. Using real research data from the life sciences, we will introduce the concept of each technique, discuss its assumptions, learn what to do when assumptions are not met, evaluate the model fit, interpret the output, and visualize the results. Participants from any discipline are welcome to register and attend. Learn: Linear regression, ANOVA, Assumptions of linear regression, Logistic regression, and Linear mixed effects models. 


Manage Your Research Data

Offered in partnership with the UVA Health Sciences Library, this 90-minute interactive workshop can make your life easier by helping you adopt good data practices early in a project. Using real-life examples, this workshop covers: Recommended approaches to data organization, versioning, documentation, and storage; “Tidy data” practices to enable efficient data entry, preparation, and analysis; and UVA research data support services and resources