Introduction to PyMC3 for Bayesian Modeling and Inference

Go to Course: https://www.coursera.org/learn/introduction-to-pymc3

Introduction

# Course Review: Introduction to PyMC3 for Bayesian Modeling and Inference on Coursera ## Overview In the realm of data science and probabilistic modeling, Bayesian methods have become increasingly important. "Introduction to PyMC3 for Bayesian Modeling and Inference" is a specialized course on Coursera that effectively introduces students to PyMC3—an intuitive and flexible Python library for Bayesian analysis. This course is particularly designed to build foundational knowledge and practical skills in Bayesian modeling, culminating in a real-world application: modeling COVID-19 disease dynamics. ### Course Structure and Content The course is divided into four key modules, each crafted to enhance the learner's understanding of Bayesian statistics through hands-on applications: 1. **Introduction to PyMC3 - Part 1** - This module familiarizes participants with the PyMC3 framework for probabilistic programming. It covers essential modeling concepts and the PyMC3 syntax. Students also learn to use ArViz, a visualization library integrated within PyMC3, which helps in visualizing posterior distributions effectively. The practical approach of using Jupyter notebooks facilitates an interactive learning experience. 2. **Introduction to PyMC3 - Part 2** - Building on the foundational concepts, this module delves into applications of PyMC3 for regression and classification problems. The course addresses common challenges, such as dealing with outliers and constructing hierarchical models. A case study further reinforces the learning and application of techniques from the earlier module. 3. **Metrics in PyMC3** - This module shifts focus to evaluating the quality of inferred solutions. Participants explore various metrics and methods to assess their Bayesian models. There is also guidance on debugging algorithms within PyMC3, which adds an essential layer of skills for aspiring data scientists. 4. **Modeling of COVID-19 Cases Using PyMC3** - The final project is an ungraded exploration where students apply all their acquired knowledge to model COVID-19 dynamics using a Susceptible-Infected-Recovered (SIR) model. This self-directed project encourages the application of theoretical concepts to real-life data, stimulating critical thinking and problem-solving skills. ## Learning Outcomes By the end of the course, participants will have a solid understanding of Bayesian modeling techniques using PyMC3, practical skills in handling real data, and the ability to articulate and visualize their results. The progression from basic concepts to hands-on applications ensures that students can grasp both the theoretical and practical aspects of Bayesian inference. ## Course Delivery and Resources The course employs a learner-centric approach with interactive Jupyter notebooks throughout, making complex Bayesian concepts more tangible. With clear instructions provided for downloading and running the notebooks, students will find it easy to engage with the material. Additionally, the dedicated course website offers supplemental materials and resources to facilitate a comprehensive learning experience. ## Recommendations This course is highly recommended for individuals looking to deepen their understanding of Bayesian modeling and inference using PyMC3. It is ideal for: - Data scientists and analysts who want to add Bayesian modeling to their skillset. - Researchers and practitioners in fields such as epidemiology, finance, and social sciences. - Students with a fundamental background in statistics and programming in Python. Overall, "Introduction to PyMC3 for Bayesian Modeling and Inference" serves as an excellent gateway into the world of Bayesian analysis, providing both theoretical foundations and practical applications. Don't miss the opportunity to explore this course on Coursera and elevate your data science capabilities! For more details, visit the course website: [Introduction to PyMC3](https://sjster.github.io/introduction_to_computation).

Syllabus

Introduction to PyMC3 - Part 1

This module serves as an introduction to the PyMC3 framework for probabilistic programming. It introduces some of the concepts related to modeling and the PyMC3 syntax. The visualization library ArViz, that is integrated into PyMC3, will also be introduced. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

This module will teach the basics of using PyMC3 to solve regression and classification problems using PyMC3. It will also show how to deal with outliers in your data and create hierarchical models. Finally, a case study is presented to help apply everything that was learned in Module 1 and 2. The course website ishttps://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#linear-regression-again. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

This module introduces various measures and metrics to assess the quality of the solutions inferred using PyMC3. Hands-on examples are used to illustrate how various methods and visualizations can be used in PyMC3. Finally, a brief overview of how to debug PyMC3 algorithms is provided. The course website ishttps://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#mcmc-metrics. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

This is an ungraded final project. We will utilize everything that has been learned in this course to model the disease dynamics of COVID-19 using a SIR model. Utilizing real-life data, the goal would be to infer the parameters of the SIR model for COVID-19.