Data Visualization with Altair: a grammar of graphics for Python

In our first PyData Madison, we will learn about Altair, a declarative statistical visualization library for Python, based on Vega and Vega-Lite.

Altair provides an elegant and consistent API for statistical graphics. Altair is built on top of the Vega-Lite high-level grammar for interactive graphics which is based on the "grammar of graphics" idea proposed by Leland Wilkinson. Altair's key strength is the provision of a clear mental model based on a set of graphical primitives and carefully designed combinatoric rules, that yield an ample space of graphical displays, avoiding the constraints of chart taxonomies.

Speaker: Pablo Caceres is a PhD student in Psychology at UW-Madison, and a LUCID (https://lucid.wisc.edu/) trainee, working at the intersection of cognitive science, machine learning, and data science. He graduated with a BA in Sociology and an MA in Public Policy and Economics at the University of Chile. He has multiple years of experience in statistical analysis, data visualization, and machine learning, in both academia and industry. Pablo did a Data Science internship at VEDA Data Solutions in summer 2019 and will start his second Data Science internship at Airbnb in San Francisco, CA, in summer 2020.

As our first PyData-Madison meeting, Alyssa, Sean, and Pablo will say a few welcome words and talk about our structure, goals, and code of conduct.

LOCATION: Health Sciences Learning Center, UW-Madison, Room 1220

Modality: talk and interactive guided tutorial
Duration: ~ 75 min
Level: all levels, beginner-friendly
What to bring: if you want to follow along, bring a laptop with a Python 3.7 installation
Food: taco/nacho bar for people that RSVP

More info about Altair can be found at:
https://altair-viz.github.io/

Grammar of graphics:
https://www.jstatsoft.org/article/view/v017b03/v17b03.pdf