Bayesian Data Analysis in Python
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Learn Bayesian data analysis in Python: update beliefs with Bayes’ Theorem, run A/B tests and decision analysis, and fit regression models in PyMC3.
Bayesian data analysis treats unknown quantities as probability distributions and revises them as new evidence arrives, so an analyst can reason about conditional probability without leaning on p-values or preset confidence levels. You work with Bayes’ Theorem, see how the Bayesian view differs from the frequentist one, and apply it in Python to coin tosses, drug-effectiveness results, online sales and click data, and avocado prices, finishing with regression models fit in PyMC3.
What You Will Achieve
- Apply Bayes’ Theorem to update a probability, such as whether a coin is fair or an email is spam.
- Estimate distribution parameters with grid approximation, choose a sensible prior, and report point estimates with credible intervals.
- Compare two ad variants with a Bayesian A/B test and weigh the cost and profit of each choice.
- Fit, check and compare regression models in PyMC3, then use their predictions to set a price.
Program Overview
Bayes’ Theorem and Probability Foundations
Introduces the Bayesian outlook on probability and builds a first model from randomized coin tosses.
- Bayesians vs. frequentists
- Probability distributions
- Bayes’ Theorem with cards, spam filtering and test results
- A coin-toss model that learns from more data
Bayesian Estimation and Reporting
Estimates parameters on drug-effectiveness data, adds prior knowledge and presents results for a non-technical audience.
- Grid approximation with and without a prior
- Updating posterior belief and simulating posterior draws
- Choosing a prior
- Point estimates and highest posterior density credible intervals
Business Inference and Regression with PyMC3
Uses online sales marketing data for testing and decisions, then fits and compares regression models in PyMC3 for an avocado pricing case study.
- A/B testing of posterior click rates
- Decision analysis on cost and profit
- Regression, forecasting and predictive distributions
- Markov Chain Monte Carlo, WAIC model comparison and price optimization
Certificate Awarded (Paid)
Complete the course to earn it.
Job Outlook
Bayesian methods are used wherever teams test ad variants, make decisions under uncertainty, forecast from sales data or choose a price, and need to say how sure they are.
Good for: Analysts who want to run A/B tests, decision analysis and regression the Bayesian way in Python, with business data in most exercises.
Keep in mind: The lessons use PyMC3, which has since continued as the PyMC library, so some code may need updating; despite the intermediate label, the opening chapter starts with basic probability.
Bottom line: The closing avocado pricing case study pulls the course’s estimation, decision analysis and PyMC3 modeling work into one practical problem.
Frequently Asked Questions
Which examples and datasets carry the Bayesian methods?
The exercises start with randomized coin tosses and card and spam examples, then move to drug-effectiveness data, online sales marketing and click data, and a closing case study on avocado prices.
What is grid approximation used for here?
Grid approximation applies Bayes' Theorem across a range of candidate parameter values to estimate a probability distribution. The lessons use it on drug-effectiveness data, first without prior knowledge, then with a chosen prior, updating the estimate as new data arrive.
How are the PyMC3 models checked and compared?
The final chapter covers sanity checks on whether the Markov Chain Monte Carlo sampling has converged, inspecting posterior draws, comparing competing models with WAIC, and estimating test error before generating predictions for new data.
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