How Data Happened: A History, with Columbia Prof. Chris Wiggins

Today, Chris Wiggins — Chief Data Scientist at The New York Times and faculty at Columbia University — provides an enthralling, witty and rich History of Data Science. Chris is an extraordinarily gifted orator; don't miss this episode!

Chris:
• Is an Associate Professor of Applied Math at Columbia University.
• Has been CDS at The NY Times for nearly a decade.
• Co-authored two fascinating recently-published books: "How Data Happened: A History from the Age of Reason to the Age of Algorithms" and "Data Science in Context: Foundations, Challenges, Opportunities"

The vast majority of this episode will be accessible to anyone. There are just a couple of questions near the end that cover content on tools and programming languages that are primarily intended for hands-on practitioners.

In the episode, Chris magnificently details: 
• The history of data and statistics from its infancy centuries ago to the present.
• Why it’s a problem that most data scientists have limited exposure to the humanities.
• How and when Bayesian statistics became controversial.
• What we can do to address the key issues facing data science and ML today.
• His computational biology research at Columbia.
• The tech stack used for data science at the globally revered New York Times.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

PyMC for Bayesian Statistics in Python

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.

Bayesian Statistics

Expert Rob Trangucci joins me this week to provide an introduction to Bayesian Statistics, a uniquely powerful data-modeling approach.

If you haven't heard of Bayesian Stats before, today's episode introduces it from the ground up. It also covers why in many common situations, it can be more effective than other data-modeling approaches like Machine Learning and Frequentist Statistics.

Today's episode is a rich resource on:
• The centuries-old history of Bayesian Stats
• Its particular strengths
• Real-world applications, including to Covid epidemiology (Rob's particular focus at the moment)
• The best software libraries for applying Bayesian Statistics yourself
• Pros and cons of pursuing a PhD in the data science field

Rob is a core developer on the open-source STAN project — a leading Bayesian software library. Having previously worked as a statistician in renowned professor Andrew Gelman's lab at Columbia University in the City of New York, Rob's now pursuing a PhD in statistics at the University of Michigan.

Listen or watch here.