Data Science Hub
Intro to Deep Learning with Keras
Discovery Building 330 North Orchard Street, Madison, WI, United Stateshttps://uw-madison-datascience.github.io/2025-05-19-uwmadison-DeepLearningKeras/
REU / Undergrad Only – Data Carpentry
Discovery Building 330 North Orchard Street, Madison, WI, United Stateshttps://uw-madison-datascience.github.io/2025-06-02-uwmadison-reudc/Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research. Its target audience is researchers who have little to no prior computational experience, and its lessons are domain specific, building on learners' existing knowledge to enable them to quickly apply skills learned to their own research. Participants will be encouraged to help one another and to apply what they have learned to their own research problems.
Geospatial Data Carpentry Workshop
Discovery Building 330 North Orchard Street, Madison, WI, United Stateshttps://uw-madison-datascience.github.io/2025-06-02-uwmadison-dc-geospatial/The goal of this workshop is to provide an introduction to core geospatial data concepts and dive into working with raster/vector data, including how to open, work with, and plot vector and raster-format spatial data in R. Additional topics include working with spatial metadata (extent and coordinate reference systems), reprojecting spatial data, and working with raster time series data. This lesson assumes you have some knowledge of R. If you’ve never used R before, or need a refresher, start with our Introduction to R for Geospatial Data lesson webpage.
REU / Undergrad Only – Software Carpentry
Discovery Building 330 North Orchard Street, Madison, WI, United Stateshttps://uw-madison-datascience.github.io/2025-06-09-uwmadison-reuswc/Software Carpentry aims to help researchers get their work done in less time and with less pain by teaching them basic research computing skills. This hands-on workshop will cover basic concepts and tools, including program design, version control, data management, and task automation. Participants will be encouraged to help one another and to apply what they have learned to their own research problems.