Enroll Course: https://www.coursera.org/learn/introduction-to-pymc3
In the ever-evolving world of data science, Bayesian modeling has emerged as a powerful tool for making inferences and predictions based on probabilistic frameworks. If you’re looking to dive into this fascinating field, I highly recommend the course “Introduction to PyMC3 for Bayesian Modeling and Inference” available on Coursera. This course is the final installment in a three-part specialization that equips you with the skills to utilize PyMC3 effectively.
### Course Overview
The course is designed to introduce learners to the PyMC3 framework, which is essential for probabilistic programming. Throughout the course, you will engage with Python and Jupyter notebooks, making the learning process interactive and practical. The course is structured into four main modules:
1. **Introduction to PyMC3 – Part 1**: This module lays the groundwork by introducing the PyMC3 framework and its syntax. You will also learn about the visualization library ArViz, which is integrated into PyMC3, enhancing your ability to visualize complex models.
– [Course Website](https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html)
– [Getting Started](https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html)
2. **Introduction to PyMC3 – Part 2**: Here, you will delve into regression and classification problems, learn to handle outliers, and create hierarchical models. A case study will help you apply the concepts learned in the first two modules.
– [Course Website](https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#linear-regression-again)
– [Getting Started](https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html)
3. **Metrics in PyMC3**: This module focuses on assessing the quality of your solutions. You will explore various metrics and visualizations to evaluate your models, along with debugging techniques for PyMC3 algorithms.
– [Course Website](https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#mcmc-metrics)
– [Getting Started](https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html)
4. **Modeling of COVID-19 cases using PyMC3**: The course culminates in an ungraded final project where you will apply everything you’ve learned to model COVID-19 dynamics using a SIR model. This hands-on project utilizes real-life data, allowing you to infer parameters relevant to the pandemic.
### Why You Should Enroll
This course is perfect for anyone interested in data science, statistics, or machine learning. The hands-on approach, combined with real-world applications, makes it an invaluable resource. Whether you’re a beginner or have some experience in Bayesian modeling, this course will enhance your understanding and skills.
### Conclusion
In conclusion, “Introduction to PyMC3 for Bayesian Modeling and Inference” is a comprehensive course that provides a solid foundation in Bayesian modeling using PyMC3. With its practical applications and engaging content, it is a must-take for aspiring data scientists. Don’t miss out on the opportunity to elevate your skills in this critical area of data science!
For more information, visit the [course website](https://sjster.github.io/introduction_to_computation).
Enroll Course: https://www.coursera.org/learn/introduction-to-pymc3