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DataCampBayesian Data Analysis in Python
4.8(260)INTERMEDIATEEnglish
Korshub's take
Mastering Probabilistic Inference
If you are looking to move beyond traditional p-values and fixed confidence levels, this intermediate-level course offers a deep dive into the world of Bayesian statistics. Instead of relying on classical constants, you will learn how to determine conditional probability to gain a more nuanced understanding of your data.
What You Will Gain
- Practical Modeling: Learn to implement linear regression and decision analysis using a Bayesian framework.
- Real-World Application: Apply your skills to diverse datasets, including sales, advertising, and bike rental figures.
- A/B Testing Mastery: Understand how Bayesian methods enhance the way you approach A/B testing.
- Tool Proficiency: Get hands-on experience with the PyMC3 library to design, fit, and interpret complex models efficiently.
This course is ideal for data scientists ready to expand their statistical toolbox with modern, probabilistic techniques.
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.
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