Schedule of workshop events
Introduction to Git and GitHub
Version control software such as Git is essential for collaboration and version tracking on data science projects, however the learning curve for Git is quite steep. In this workshop we will introduce and practice common workflows using Git and GitHub.
Introduce yourself online
A professional web presence is a great way to share information about yourself on the internet. In this workshop we will learn how to build a few different types of websites using R and R Markdown.
Interpreting and explaining machine learning models
Machine learning algorithms are widely used in data science applications and have significant potential to improve predictions and understanding of social scientific processes. In many applications researchers need to be able to explain why the model made one prediction over another. In this workshop we introduce several techniques for interpreting black box models using model-agnostic techniques.
Introduction to R for the Python user
A quick-start guide for Python users to get up to speed with the R programming language.
Literate programming with R Markdown
Literate programming is a technique for meshing source code with a human-readable explanation of its logic. This approach is used widely in data science and the social sciences to enable reproducible research and open access. In this workshop, we introduce the R Markdown document format and practice developing notebooks interweaving source code, output, and written text.
Optimizing selection of color palettes
Researchers frequently use color to communicate numeric values in a data visualization. Most software will implement a default algorithm to select a color palette, and these palettes frequently are hard-to-interpret or are inaccessible to individuals with vision impairments. In this workshop we will develop approaches to readily generate optimized color palettes for continuous and categorical variables.
Geospatial visualizations with R
R contains many different packages for importing, structuring, and visualizing geospatial datasets. In this workshop we will consider two workflows for drawing geospatial data visualizations (aka data maps) using pre-generated mapping tiles as well as spatial vector datasets. These methods integrate directly into tidy workflows and visualization methods such as `ggplot2`.
Data visualization with ggplot2
An introduction to data visualization and the grammar of graphics in R.