Bayesian Statistics: Excel to Python A/B Testing

Instructor: EDUCBA

Access provided by Datta Meghe Institute of Higher Education & Research (DU)

3 modules

Gain insight into a topic and learn the fundamentals.

5 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

5 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Apply Bayesian reasoning in Excel to calculate, update, and interpret probabilities.
Build probabilistic models and analyze predictive performance in real datasets.
Use Python with MCMC and PyMC for A/B testing, posterior inference, and scaling.

Skills you'll gain

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Assessments

10 assignments

Taught in English

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There are 3 modules in this course

By the end of this course, learners will be able to apply Bayesian statistics for decision-making in both business and healthcare contexts, implement probabilistic models in Excel, and perform advanced A/B and multi-variant testing using Python.

The course begins with a hands-on introduction to Bayesian reasoning in Excel, where you will learn to structure datasets, calculate joint and conditional probabilities, and update prior probabilities with real-world healthcare examples. You will practice building Bayesian probability tables, interpreting repeated test outcomes, and analyzing predictive performance for evidence-based decision-making. Next, the course transitions into computational Bayesian statistics with Python. You will gain practical experience with Markov Chain Monte Carlo (MCMC) sampling, approximate posterior distributions using PyMC, and explore hierarchical models for A/B and multi-variant testing. What sets this course apart is its dual approach: simple Excel-based foundations for immediate application, followed by advanced Python implementations for scalable experimentation and machine learning integration.

This module introduces the core principles of Bayesian statistics and demonstrates their application in supervised machine learning and A/B testing. Learners will explore the fundamentals of Bayesian inference, examine practical examples of decision-making under uncertainty, and gain hands-on experience implementing Markov Chain Monte Carlo (MCMC) methods using PyMC. By the end of the module, participants will develop the ability to connect Bayesian theory with real-world machine learning experiments.

What's included

8 videos4 assignments1 plugin

8 videosTotal 58 minutes

Introduction to Bayesian Machine Learning9 minutes
Example of Bayesian Machine Learning6 minutes
Example of Bayesian Machine Learning Continues6 minutes
MCMC Module of PYMC Implementation6 minutes
Running the MCMC Module6 minutes
Multiple Variant Testing Using Hierarchial Model9 minutes
Example of Multiple Variant Testing4 minutes
Example of Multiple Variant Testing Continues9 minutes

4 assignmentsTotal 80 minutes

Graded - Foundations of Bayesian Machine Learning30 minutes
Getting Started with Bayesian Learning10 minutes
MCMC in Action with PyMC10 minutes
Hierarchical Models for Multi-Variant Testing30 minutes

1 pluginTotal 15 minutes

From Inference to Experimentation: Applying Bayesian Methods in A/B Testing15 minutes

This module introduces learners to the fundamentals of preparing healthcare datasets for Bayesian statistical modeling using Microsoft Excel. Learners will explore project goals, understand the structure of real-world healthcare testing data, and create efficient summaries for initial analysis. By examining historical, future, demographic, and center-based trends, students will gain the ability to organize, interpret, and structure data effectively, ensuring a strong foundation for Bayesian probability applications in healthcare analytics.

What's included

7 videos3 assignments

This module guides learners through constructing and applying Bayesian probability tables in Microsoft Excel to analyze healthcare testing scenarios. Students will learn how to structure Bayesian frameworks, calculate joint probabilities, update prior probabilities with new evidence, and interpret outcomes across multiple testing cycles. By the end of this module, learners will be able to apply Bayesian reasoning to real-world healthcare data, enhancing accuracy in predictive healthcare analytics.