DataCampBayesian Data Analysis in Python
4.8(260)15k studentsINTERMEDIATEEnglish
Korshub's take
Moving Beyond P-Values
If you are looking to evolve your statistical toolkit, this intermediate-level course offers a deep dive into the world of Bayesian inference. Instead of relying on traditional fixed constants like confidence levels, you will learn how to determine conditional probability to gain a more nuanced understanding of your data.
Practical Applications
This course is designed for data scientists who want to move from theory to implementation. You will work with real-world datasets—covering advertising, sales, and bike rentals—to master:
- A/B Testing: Applying Bayesian logic to experiment design.
- Decision Analysis: Making informed choices under uncertainty.
- Linear Regression: Modeling relationships through a Bayesian lens.
By the end of the curriculum, you will be proficient in using the PyMC3 library to design, fit, and interpret complex models with ease.
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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