I’m not sure who scheduled ODSC and PyConUS during the same week, but I am unhappy with their decisions. Last Tuesday I presented a talk and co-presented a workshop at ODSC, and on Thursday I presented a tutorial at PyCon.<\/p>\n\n\n\n
If you would like to follow along with my very busy week, here are the resources:<\/p>\n\n\n\n
Co-presented with Alex Fengler<\/a> for ODSC East 2025<\/p>\n\n\n\n In this tutorial, we explore Bayesian regression using PyMC \u2013 the primary library for Bayesian sampling in Python \u2013 focusing on survey data and other datasets with categorical outcomes. Starting with logistic regression, we\u2019ll build up to categorical and ordered logistic regression, showcasing how Bayesian approaches provide versatile tools for developing and evaluating complex models. Participants will leave with practical skills for implementing Bayesian regression models in PyMC, along with a deeper appreciation for the power of Bayesian inference in real-world data analysis. Participants should be familiar with Python, the SciPy ecosystem, and basic statistics, but no experience with Bayesian methods is required.<\/p>\n<\/blockquote>\n\n\n\n The repository for this tutorial is here<\/a>; it includes notebooks where you can run the examples, and there’s a link to the slides.<\/p>\n\n\n\n And then later that day I presented…<\/p>\n\n\n\n Time series analysis provides essential tools for modeling and predicting time-dependent data, especially data exhibiting seasonal patterns or serial correlation. This tutorial covers tools in the StatsModels library including seasonal decomposition and ARIMA. As examples, we’ll look at weather data and electricity generation from renewable sources in the United States since 2004 — but the methods we’ll cover apply to many kinds of real-world time series data. Outline Introduction to time series Overview of the data Seasonal decomposition, additive model Seasonal decomposition, multiplicative model Serial correlation and autoregression ARIMA Seasonal ARIMA<\/p>\n<\/blockquote>\n\n\n\n This talk is based on Chapter 12 of the new edition of Think Stats<\/em><\/a>. Here are the slides<\/a>.<\/p>\n\n\n\n Unfortunately there’s no video from the talk, but I presented related material in this workshop for PyData Global 2024<\/a>:<\/p>\n\n\n\n\n
Mastering Time Series Analysis with StatsModels: From Decomposition to ARIMA<\/h2>\n\n\n\n
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