Bayesian Data Analysis in Python
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
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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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Applied Statistics in Python
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The Bayesian way
FreeTake 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.
Who is Bayes? What is Bayes?50 xpBayesians vs. Frequentists100 xpProbability distributions100 xpProbability and Bayes' Theorem50 xpLet's play cards100 xpBayesian spam filter100 xpWhat does the test say?50 xpTasting the Bayes50 xpTossing a coin100 xpThe more you toss, the more you learn100 xpHey, is this coin fair?100 xp - 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.
Under the Bayesian hood50 xpTowards grid approximation100 xpGrid approximation without prior knowledge100 xpUpdating posterior belief100 xpPrior belief50 xpThe truth of the prior100 xpPicking the right prior50 xpSimulating posterior draws100 xpReporting Bayesian results50 xpPoint estimates100 xpHighest Posterior Density credible intervals100 xpThe meaning of credibility50 xp - 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.
A/B testing50 xpSimulate beta posterior100 xpPosterior click rates100 xpA or B, and how sure are we?100 xpHow bad can it be?100 xpDecision analysis50 xpDecision analysis: cost100 xpDecision analysis: profit100 xpRegression and forecasting50 xpDefining a Bayesian regression model50 xpAnalyzing regression parameters100 xpPredictive distribution100 xp - 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!
Markov Chain Monte Carlo and model fitting50 xpMarkov Chain Monte Carlo100 xpSampling posterior draws100 xpInterpreting results and comparing models50 xpInspecting posterior draws100 xpComparing models with WAIC100 xpMaking predictions50 xpSample from predictive density100 xpEstimating test error100 xpHow much is an avocado?50 xpFitting the model100 xpInspecting the model100 xpOptimizing the price100 xpFinal remarks50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Applied Statistics in Python
Go To Trackcollaborators
prerequisites
Introduction to Statistics in Python
Michał OleszakMachine 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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