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Bayesian Data Analysis in Python

Intermediate
4.0+
18 reviews
Updated 12/2024
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
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PythonProbability & Statistics4 hours14 videos49 exercises4,000 XP12,288Statement of Accomplishment

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Course Description

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.

Prerequisites

Introduction to Statistics in Python
1

The Bayesian way

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2

Bayesian estimation

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3

Bayesian inference

Start Chapter
4

Bayesian linear regression with pyMC3

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Bayesian Data Analysis in Python
Course
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Don’t just take our word for it

*4.0
from 18 reviews
44%
33%
11%
6%
6%
  • Li D.
    about 2 months

    Great course. Highly recommended!

  • Vinay B.
    7 months

    Content was great. Helped to build intuition. However, an exercise on implementing pymc3 and coding one from scratch would have helped even more

  • Joel A.
    10 months

    I would have liked a little more information in the section on MCMC. Also, providing more of the underlying theory in credible intervals would be helpful.

  • Vengadesan N.
    over 1 year

    Perfect course for those who need to understand fundamental probability.

  • Nakul S.
    almost 2 years

    Yet another high quality course from the DataCamp stable of instructors. Grid approximation is a very handy tool especially for single parameter posterior estimation. Conjugate priors takes it a step further. MCMC was the icing on the cake allowing one to sample from posterior without knowing the distribution! Moreover,Bayesian A/B testing, decision analysis, and regression models gives one a feel for how bayesian analysis can be practically applied. Overall, a very satisfying course!

"Great course. Highly recommended!"

Li D.

"Content was great. Helped to build intuition. However, an exercise on implementing pymc3 and coding one from scratch would have helped even more"

Vinay B.

"I would have liked a little more information in the section on MCMC. Also, providing more of the underlying theory in credible intervals would be helpful."

Joel A.

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