Getting Bayesian: Probabilistic Programming in Python with PyMC3
Getting Bayesian: Probabilistic Programming in Python with PyMC3


If you can write a basic model in Python's scikit-learn library, you can make the leap to Bayesian inference with PyMC3, a user-friendly intro to probabilistic programming in Python! The only requisite background for this workshop is minimal familiarity with Python, preferably with some exposure to building a model in sklearn.

Probabilistic programming (PP) means building models where the building blocks are probability distributions. We can use PP to do Bayesian inference easily. Bayesian inference is historically a fairly established method but it’s gaining prominence in data science because it’s now easier than ever to use Python to do the math. Bayesian inference is a different paradigm of statistics than maybe we’re used to, but it also allows us to solve problems that aren't otherwise tractable with classical methods. In this workshop, we'll work through actual examples of models using PyMC3, including hierarchical models.

By the end of this presentation, you'll know the following:
* What probabilistic programming is and why it's necessary for Bayesian inference
* What Bayesian inference is, how it's different from classical frequentist inference, and why it's becoming so relevant for applied data science in the real world
* How to write your own Bayesian models in the Python library PyMC3, including metrics for judging how well the model is performing
* How to go about learning more about the topic of Bayesian inference and how to bring it to your current data science job


Lara is a data science instructor at Metis, a 12-week accredited data science bootcamp. She teaches a wide-ranging curriculum, starting from linear regression and ending at deep learning. She comes to Metis from McKinsey, where she worked with financial institutions on risk modeling. Prior to settling into the practical world of consulting, Lara received a master’s from the University of Chicago, where she had the privilege of thinking about abstract problems while drinking too much coffee. If forced to snap out of her mathematical reveries, she’s probably doing yoga or riding a bike.

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