09-14, 12:00–13:00 (Europe/Amsterdam), Hello, World! (Tutorials)
Learn how to visualize uncertainty in parameters or predictions using mutiple visualizations adapted to your data and task
This tutorial will cover 4 different plots designed to visualize uncertainty: quantile dotplots, empirical cumulative density functions, histograms and kernel density estimates. Finding optimal solutions in data visualization requires adapting the plots to both the data and task at hand, so we will start explaining each of them, their main pros and cons, how to interpret and how to generate each of those plot with ArviZ. We will then cover multiple data visualization tasks on several datasets to put those skills in practice and extend and deepen our "visualization space", both what you know and what you are comfortable enough to use.
No previous knowledge expected
Oriol (he/him/ell) is a computational statistician with a passion for open source, teaching and community building. He currently works as open source maintainer of ArviZ and PyMC, both Python libraries related to Bayesian modeling and sponsored by NumFOCUS. He is also an instructor at Intuitive Bayes and has taught a couple undergrad courses on maths and statistics as external lecturer.
He has led both virtual and in-person workshops and talks at Data Umbrella, PyDataBCN and PyData Global. He is also involved in PyMCon organization.