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Introduction to Statistical Modeling with Python


This intermediate-level tutorial will provide students with hands-on experience applying practical statistical modeling methods on real data. Unlike many introductory statistics courses, we will not be applying "cookbook" methods that are easy to teach, but often inapplicable; instead, we will learn some foundational statistical methods that can be applied generally to a wide variety of problems: maximum likelihood, bootstrapping, linear regression, and other modern techniques. The tutorial will start with a short introduction on data manipulation and cleaning using pandas, before proceeding on to simple concepts like fitting data to statistical distributions, and how to use Monte Carlo simulation for data analysis. Slightly more advanced topics include bootstrapping (for estimating uncertainty around estimates) and flexible linear regression methods using Bayesian methods. By using and modifying hand-coded implementations of these techniques, students will gain an understanding of how each method works. Students will come away with knowledge of how to deal with very practical statistical problems, such as how to deal with missing data, how to check a statistical model for appropriateness, and how to properly express the uncertainty in the quantities estimated by statistical methods.


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