Python is a very popular language for computational and data science, and it has many powerful capabilities for visualizing data.  Data visualization plays an essential role in representing data so that its underlying patterns might be conveyed and understood.  The second part of this series will explore various ways that Python can be used to make interactive graphics for exploring data and conceptualizing trends and dependencies.  We’ll dive into Python libraries for making interactive widgets, plots, and dashboards (ipywidgets, bqplot, voila, plotly, altair, and bokeh).  These will be covered in interactive exercises so that attendees can gain direct experience in using these libraries.

Basic familiarity with Python will be useful.  Please contact Ben Winjum if you have questions (or would like advice on getting up to speed before the workshop).

Registration required: https://ucla.zoom.us/meeting/register/tJwscu6opzkpH9Zozd0G_SZJW_aU1dmJWrW4