Learn how Bayesian Statistics can be more powerful and interpretable than any other data modeling approach from Dr. Thomas Wiecki, a Core Developer of PyMC — the leading Bayesian software library for Python.

Thomas:
• Has been a Core Developer of PyMC for over eight years.
• Is Co-Founder and CEO of PyMC Labs, which solves commercial problems with Bayesian data models.
• Previously, he worked as VP Data Science at Quantopian Inc.
• Holds a PhD in Computational Neuroscience from Brown University.

Today’s episode is more on the technical side so will appeal primarily to practicing data scientists.

In this episode, Thomas details:
• What Bayesian statistics is.
• Why Bayesian statistics can be more powerful and interpretable than any other data modeling approach.
• How PyMC was developed and how it trains models so efficiently.
• Case studies of large-scale Bayesian stats applied commercially.
• The extra flexibility of *hierarchical* Bayesian models.
• His top resources for learning Bayesian stats yourself.
• How to build a successful company culture.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.