bayesian statistics

We welcome Dr. Jennifer Hill, Professor of Applied Statistics at New York University, to the podcast this week for a discussion that covers causality, correlation, and inference in data science.

This episode is brought to you by Pachyderm, the leader in data versioning and MLOps pipelines and by Zencastr (zen.ai/sds), the easiest way to make high-quality podcasts.

In this episode you will learn:
• How causality is central to all applications of data science [4:32]
• How correlation does not imply causation [11:12]
• What is counterfactual and how to design research to infer causality from the results confidently [21:18]
• Jennifer’s favorite Bayesian and ML tools for making causal inferences within code [29:14]
• Jennifer’s new graphical user interface for making causal inferences without the need to write code [38:41]
• Tips on learning more about causal inference [43:27]
• Why multilevel models are useful [49:21]

Additional materials: www.superdatascience.com/607

In this episode, Dr. Thomas Wiecki, Core Developer of the PyMC Library and CEO of PyMC Labs, joins Jon for a masterclass in Bayesian statistics. Tune in to hear about PyMC, and discover why Bayesian statistics can be more powerful and interpretable than any other data modeling approach.

In this episode you will learn:
• What Bayesian statistics is [7:30]
• Why Bayesian statistics can be more powerful and interpretable than any other data modeling approach [17:20]
• How PyMC was developed [20:41]
• Commercial applications of Bayesian stats [43:07]
• How to build a successful company culture [1:03:14]
• What Thomas looks for when hiring [1:11:13]
• Thomas’s top resources for learning Bayesian stats yourself [1:13:57]

Additional materials: www.superdatascience.com/585

In this week's episode Greg and Patrick discuss the critical distinction between sample distributions and sampling distributions, and explore all the different ways in which sampling distributions are foundational to how we conduct research. Along the way they also mention Starbucks jazz, one item tests, hot pockets, delusions of grandeur, Tetris and Pong, drawing inappropriate distributions, magical properties, texting pictures of kindle pages, Roman arches, 1970s graphics, never saying never, mumbling, Greenday, ignoring Roy Levy, real life bootstrap, and Goodnight Gracie. 

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What exciting data science problems emerge when you try to forecast an election? Many, it turns out!

We're very excited to turn our DataCafé lens on the current Presidential race in the US as an exemplar of statistical modelling right now. Typically state election polls are asking around 1000 people in a state of maybe 12 million people how they will vote (or even if they have voted already) and return a predictive result with an estimated polling error of about 4%.

In this episode, we look at polling as a data science activity and discuss how issues of sampling bias can have dramatic impacts on the outcome of a given poll. Elections are a fantastic use-case for Bayesian modelling where pollsters have to tackle questions like "What's the probability that a voter in Florida will vote for President Trump, given that they are white, over 60 and college educated".

There are many such questions as each electorate feature (gender, age, race, education, and so on) potentially adds another multiplicative factor to the size of demographic sample needed to get a meaningful result out of an election poll.

Finally, we even hazard a quick piece of psephological analysis ourselves and show how some naive Bayes techniques can at least get a foot in the door of these complex forecasting problems. (Caveat: correlation is still very important and can be a source of error if not treated appropriately!)

Further reading:

• Article: Ensemble Learning to Improve Machine Learning Results (https://bit.ly/34MW3HO via statsbot.co)
• Paper: Combining Forecasts: An Application to Elections (https://bit.ly/3efx5nm via researchgate.net)
• Interactive map: Explore The Ways Trump Or Biden Could Win The Election (https://53eig.ht/2TIlAvh via fivethirtyeight.com)
• Podcast: 538 Politics Podcast (https://53eig.ht/2HSkwCA via fivethirtyeight.com)
• Update US polling map: Consensus Forecast Electoral Map (https://bit.ly/2HY1FWk via 270towin.com)

Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.

Recording date: 30 October 2020
Intro music by Music 4 Video Library (Patreon supporter) 

Thanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.

I am beyond excited to share this first episode of the PyData podcast with you. The idea is to have a free-form discussion with interesting guests which does not shy away from more advanced topics.

In this episode I talk to Chris Fonnesbeck: Professor for biostatistics at Vanderbilt University and, as of recent, Data Scientist at the New York Yankees. We start off this discussion by talking about Bayesian statistics, probabilistic programming. Chris then talks about the history of PyMC and what the current status of PyMC4 is.

We then dive more into his background and how he moved from marine biology to become a data scientist in sports analytics and the lessons he learned along the way.

Special thanks to my Patreons Andrew Ng, Daniel Gerlanc, and Richard Craib.

If you would like to support the podcast go to: https://patreon.com/twiecki

Follow Chris on Twitter: https://twitter.com/fonnesbeck

The Book of Mormon is one in a billion.  Actually, more accurately, it's one in one thousand billion, billion, billion, billion.  Through Bayesian statistical analysis, Distinguished Professor Bruce E Dale from Michigan State University explains the historical and ancient authenticity of the Book of Mormon with a direct response to critics and prescribing a real world geographical setting in ancient Mesoamerica.  Read his research article here https://bit.ly/31WKOrJ for your personal studies.  For more information, please visit us online at www.BookofMormonHistory.com