I have two questions for you: Are you a self-learner? Then how do you stay up to date? What should you focus on if you’re a beginner, or if you’re more advanced?
And here is my second question: Are you working in biomedicine? And if you do, are you using Bayesian tools? Then how do you get your co-workers more used to posterior distributions than p-values? In other words, how do you change behaviors in a large organization?
In this episode, Eric Ma will answer all these questions and even tell us his favorite modeling techniques, which problems he encountered with these models, and how he solved them. He’ll also share with us the software-engineering workflow he uses at Novartis to share his work with colleagues.
Eric is a data scientist at the Novartis Institutes for Biomedical Research, where he focuses on Bayesian statistical methods to make medicines for patients. Eric is also a prolific open source developer: he led the development of pyjanitor, an API for cleaning data in Python, and nxviz, a visualization package for NetworkX. He also contributes to PyMC3, matplotlib and bokeh.
This is « Learning Bayesian Statistics », episode 5, recorded October 21, 2019.
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !
Links from the show:
- Eric's website: https://ericmjl.github.io/
- Eric on Twitter: https://twitter.com/ericmjl
- Bayesian analysis recipes: https://github.com/ericmjl/bayesian-analysis-recipes
- Bayesian deep learning demystified: https://github.com/ericmjl/bayesian-deep-learning-demystified
- Causality repo: https://github.com/ericmjl/causality
- Pyjanitor - Convenient data cleaning routines for repetitive tasks: https://pyjanitor.readthedocs.io/
- PyMC3 - Probabilistic Programming in Python: https://docs.pymc.io/
- Panel - A high-level app and dashboarding solution for Python: https://panel.pyviz.org/
- Nxviz - Visualization Package for NetworkX: https://nxviz.readthedocs.io/en/latest/