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This course offers a comprehensive introduction to Bayesian data analysis, a popular technique for statistical inference. Unlike traditional methods, Bayesian data analysis allows you to calculate conditional probabilities without relying on fixed constants such as confidence levels or P values. By taking this course, you will understand how Bayesian data analysis differs from traditional methods and how to incorporate it into your data science toolbox.
The course covers various topics, including decision analysis and A/B testing, which are essential skills for analyzing real-world data such as advertising and sales. You will have the opportunity to work with actual datasets and apply your knowledge to solve practical problems.
One of the highlights of this course is the use of the PyMC3 library, a powerful tool for designing, fitting, and interpreting Bayesian model designs. By learning how to utilize this library effectively, you will be able to apply Bayesian data analysis techniques practically and efficiently.
Whether you are new to Python or already have some experience with it, this course is designed to help you learn Python specifically for data analysis purposes. You will gain a solid foundation in the language used in data science and learn how to leverage Python's capabilities for data analysis.
Overall, this course offers a comprehensive and practical approach to Bayesian data analysis. By taking this course, you will gain valuable skills that can be applied to various real-world data science and analysis scenarios.

Bayesian Data Analysis in Python

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