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.
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.
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.
A quick-start guide for Python users to get up to speed with the R programming language.
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.
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.
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`.
An introduction to data visualization and the grammar of graphics in R.
The Computational Social Science Technical Skills Workshop (or Computation Skills Workshop for short) trains participants in technical skills and methods which are relevant to computationally-driven research, but are not typically taught in for-credit courses. It provides an explicit introduction to core software environments and interfaces which impose a barrier to access for individuals lacking prior exposure to programming, and facilitates a community of computational researchers across the Division of the Social Sciences. Individual workshops are designed for a range of technical backgrounds, including purely introductory methods as well as intermediate or advanced training in specific software or computational techniques. Workshops are designed for hands-on learning, and will include substantial live-coding exercises.
The workshop is organized by the Masters Program in Computational Social Science at the University of Chicago and meets biweekly on Tuesdays from 3:30-5:20pm in room 295 at 1155 E 60th St. All members of the University community are invited to participate. Due to the ongoing public health crisis, all participants will be required to register in advance for each workshop.
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