FeaturedUdemy
Bayesian Data Analysis in Python
4.8(260)15k studentsINTERMEDIATEEnglish
Korshub's take
Course Overview
This course introduces probabilistic statistical modeling using Bayesian inference in Python. It teaches how to move beyond classical p-values by utilizing conditional probability for real-world decision-making and predictive modeling.
Who Should Take This Course
Suited for intermediate data scientists and analysts wanting to add Bayesian statistics and PyMC3 modeling to their analytical skillset.
Key Takeaways
- Differentiate Bayesian statistical inference from traditional frequentist approaches.
- Apply Bayesian models to A/B testing, decision analysis, and linear regression.
- Build, fit, and evaluate probabilistic models using Python's PyMC3 library.
About this course
Bayesian data analysis is an increasingly popular method of statistical inference, used to determine conditional probability without having to rely on fixed constants such as confidence levels or p-values. In this course, you’ll learn how Bayesian data analysis works, how it differs from the classical approach, and why it’s an indispensable part of your data science toolbox. You’ll get to grips with A/B testing, decision analysis, and linear regression modeling using a Bayesian approach as you analyze real-world advertising, sales, and bike rental data. Finally, you’ll get hands-on with the PyMC3 library, which will make it easier for you to design, fit, and interpret Bayesian models.
Price
Free
This course is free to enrol.
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