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Bayesian Data Analysis in Python

4.1+
14 reviews
Intermediate

Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

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4 Hours14 Videos49 Exercises
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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.
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In the following Tracks

Applied Statistics in Python

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  1. 1

    The Bayesian way

    Free

    Take your first steps in the Bayesian world. In this chapter, you’ll be introduced to the basic concepts of probability and statistical distributions, as well as to the famous Bayes' Theorem, the cornerstone of Bayesian methods. Finally, you’ll build your first Bayesian model to draw conclusions from randomized coin tosses.

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    Who is Bayes? What is Bayes?
    50 xp
    Bayesians vs. Frequentists
    100 xp
    Probability distributions
    100 xp
    Probability and Bayes' Theorem
    50 xp
    Let's play cards
    100 xp
    Bayesian spam filter
    100 xp
    What does the test say?
    50 xp
    Tasting the Bayes
    50 xp
    Tossing a coin
    100 xp
    The more you toss, the more you learn
    100 xp
    Hey, is this coin fair?
    100 xp
  2. 2

    Bayesian estimation

    It’s time to look under the Bayesian hood. You’ll learn how to apply Bayes' Theorem to drug-effectiveness data to estimate the parameters of probability distributions using the grid approximation technique, and update these estimates as new data become available. Next, you’ll learn how to incorporate prior knowledge into the model before finally practicing the important skill of reporting results to a non-technical audience.

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  3. 3

    Bayesian inference

    Apply your newly acquired Bayesian data analysis skills to solve real-world business challenges. You’ll work with online sales marketing data to conduct A/B tests, decision analysis, and forecasting with linear regression models.

    Play Chapter Now
  4. 4

    Bayesian linear regression with pyMC3

    In this final chapter, you’ll take advantage of the powerful PyMC3 package to easily fit Bayesian regression models, conduct sanity checks on a model's convergence, select between competing models, and generate predictions for new data. To wrap up, you’ll apply what you’ve learned to find the optimal price for avocados in a Bayesian data analysis case study. Good luck!

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In the following Tracks

Applied Statistics in Python

Go To Track

Datasets

Ads DataBikes Data

Collaborators

Collaborator's avatar
Amy Peterson
Collaborator's avatar
Justin Saddlemyer
Michał Oleszak HeadshotMichał Oleszak

Machine Learning Engineer

Michał is a Machine Learning Engineering Manager based in Zurich, Switzerland. He has a background in statistics and econometrics, holding an MSc degree from Erasmus University Rotterdam, The Netherlands. He has worn many hats, having worked at a consultancy, a start-up, a software house, and a large corporation. He blogs about anything machine learning. Visit his website to find out more.
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Don’t just take our word for it

*4.1
from 14 reviews
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  • Vinay B.
    18 days

    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.
    4 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.
    about 1 year

    Perfect course for those who need to understand fundamental probability.

  • Nakul S.
    over 1 year

    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!

  • Nataliia H.
    over 1 year

    I enjoyed the course on Bayesian Data Analysis: it was easy to follow, the exercises were challenging at the exactly right level.

"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.

"Perfect course for those who need to understand fundamental probability."

Vengadesan N.

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