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.
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.
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.
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.
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!
Machine Learning Engineer
Michał is a Machine Learning Engineer with a background in statistics and econometrics, holding degrees from Erasmus University Rotterdam, The Netherlands and Warsaw School of Economics, Poland. He is the author of the pmpp R package for forecasting with panel data. Having worked at a data science consultancy, he has gained experience in squeezing value from messy and incomplete data. He's currently shaping the future at an AI startup. Visit his homepage
to find out more.