stan – paulvanderlaken.com

For a year now, this course on Bayesian statistics has been on my to-do list. So without further ado, I decided to share it with you already.

Richard McElreath is an evolutionary ecologist who is famous in the stats community for his work on Bayesian statistics.

At the Max Planck Institute for Evolutionary Anthropology, Richard teaches Bayesian statistics, and he was kind enough to put his whole course on Statistical Rethinking: Bayesian statistics using R & Stan open access online.

You can find the video lectures here on Youtube, and the slides are linked to here:

Richard also wrote a book that accompanies this course:

For more information abou the book, click here.

For the Python version of the code examples, click here.

PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The communities approach data science using many languages, including (but not limited to) Python, Julia, and R.

April 2018, a PyData conference was held in London, with three days of super interesting sessions and hackathons. While I couldn’t attend in person, I very much enjoy reviewing the sessions at home as all are shared open access on YouTube channel PyDataTV!

In the following section, I will outline some of my favorites as I progress through the channel:

Winning with simple, even linear, models:

One talk that really resonated with me is Vincent Warmerdam‘s talk on “Winning with Simple, even Linear, Models“. Working at GoDataDriven, a data science consultancy firm in the Netherlands, Vincent is quite familiar with deploying deep learning models, but is also midly annoyed by all the hype surrounding deep learning and neural networks. Particularly when less complex models perform equally well or only slightly less. One of his quote’s nicely sums it up:

“Tensorflow is a cool tool, but it’s even cooler when you don’t need it!”

— Vincent Warmerdam, PyData 2018

In only 40 minutes, Vincent goes to show the finesse of much simpler (linear) models in all different kinds of production settings. Among others, Vincent shows: