Data Science Hub
Calendar of Events
M Mon
T Tue
W Wed
T Thu
F Fri
S Sat
S Sun
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
0 events,
1 event,
Research Bazaar
Research Bazaar
Data Science Research Bazaar: AI and ML in Research: Navigating Opportunities and Boundaries. Save the date for the the 6th annual Data Science Research Bazaar, March 19-20, 2025 at the Discovery Building. This year’s theme is AI and ML in Research: Navigating Opportunities and Boundaries. CONTACT: facilitator@datascience.wisc.edu URL: https://bazaar.datascience.wisc.edu/
1 event,
Research Bazaar
Research Bazaar
Data Science Research Bazaar: AI and ML in Research: Navigating Opportunities and Boundaries. Save the date for the the 6th annual Data Science Research Bazaar, March 19-20, 2025 at the Discovery Building. This year’s theme is AI and ML in Research: Navigating Opportunities and Boundaries. CONTACT: facilitator@datascience.wisc.edu URL: https://bazaar.datascience.wisc.edu/
0 events,
0 events,
0 events,
2 events,
Intermediate Research Software Development with Python
https://uw-madison-datascience.github.io/2025-03-24-uwmadison-intermedpython/Intermediate Research Software Development with Python aims to teach a core set of established, intermediate-level software development skills and best practices for working as part of a team in a research environment using Python as an example programming language (see detailed learning objectives). The core set of skills we teach is not a comprehensive set of all-encompassing skills, but a selective set of tried-and-tested collaborative development skills that forms a firm foundation for continuing on your learning journey.
Health Sciences Data Carpentry
Health Sciences Data Carpentry
https://uw-madison-datascience.github.io/2025-03-24-uwmadison-dc/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.