Enroll Course: https://www.coursera.org/learn/introduction-to-pymc3
In the ever-evolving world of data science and statistics, Bayesian modeling has become a crucial technique for making inferences from data. If you’re looking to delve into the realm of Bayesian inference and probabilistic programming, the course “Introduction to PyMC3 for Bayesian Modeling and Inference” on Coursera is a fantastic choice. This course is not only informative but also practical, giving participants the tools to apply what they learn immediately. Let’s take a closer look at what this course offers.
### Course Overview
The objective of this course is to provide a foundational understanding of PyMC3 and its applications in Bayesian modeling and inference. Attendees will start with the basics, gradually moving on to scalable inference techniques applicable to various problems. This course is the final piece in a three-course specialization, which fosters a comprehensive learning experience.
### Syllabus Breakdown
The course is structured into several modules, each building on the previous one:
1. **Introduction to PyMC3 – Part 1**: This initial module serves as a gentle introduction to the PyMC3 framework. You will learn about the essential concepts of modeling within the PyMC3 environment, including the syntax and visualizations via the ArViz library.
– Helpful Links: [PyMC3 Framework](https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html) & [Download Instructions](https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html)
2. **Introduction to PyMC3 – Part 2**: Here, you’ll explore regression and classification problems, learning how to handle data outliers and build hierarchical models. A case study consolidates the theoretical knowledge gained.
– Helpful Links: [Regression and Classification](https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#linear-regression-again)
3. **Metrics in PyMC3**: In this module, understanding the quality of Bayesian inferences is crucial. You will discover various statistics and metrics to evaluate your results, accompanied by practical examples.
– Helpful Links: [MCMC Metrics](https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#mcmc-metrics)
4. **Modeling of COVID-19 cases**: As the ungraded final project, you’ll apply all your acquired skills to model the dynamics of COVID-19 using the SIR model with real-life data, providing a relevant and timely application of Bayesian modeling.
### Why Take This Course?
– **Hands-On Learning**: The course emphasizes practical, hands-on experience using Python and Jupyter notebooks.
– **Comprehensive Syllabus**: The structured progression through the basics to applying your skills in real-world projects ensures a deep understanding.
– **Community and Support**: Interact with fellow learners and gain insights from instructors, which enhances the learning experience.
### Conclusion
If you are serious about exploring Bayesian methods and wish to employ statistical models in your data analysis toolbox, then the “Introduction to PyMC3 for Bayesian Modeling and Inference” course is a highly recommended resource. Whether you are a beginner or looking to refine your skills, this course provides an effective learning platform to help you achieve your goals.
You can check out the course [here](https://sjster.github.io/introduction_to_computation).
### Tags
1. PyMC3
2. Bayesian Modeling
3. Data Science
4. Coursera
5. Probabilistic Programming
6. Inference Techniques
7. Jupyter Notebooks
8. Statistical Modeling
9. COVID-19 Modeling
10. Machine Learning
### Topic
Bayesian Statistics
Enroll Course: https://www.coursera.org/learn/introduction-to-pymc3