Bayesian Statistics: Excel to Python A/B Testing

Ends soon! Save on skills that make you shine with 40% off 3 months of Coursera Plus. Save now

Bayesian Statistics: Excel to Python A/B Testing

Instructor: EDUCBA

Included with

Learn more

3 modules

Gain insight into a topic and learn the fundamentals.

27 reviews

6 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

27 reviews

6 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

Tools you'll learn

Microsoft Excel

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

10 assignments

Taught in English

91%

of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

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 assignments

8 videosTotal 58 minutes

Introduction to Bayesian Machine Learning9 minutes
Example of Bayesian Machine Learning7 minutes
Example of Bayesian Machine Learning Continues7 minutes
MCMC Module of PYMC Implementation7 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

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.

What's included

4 videos3 assignments

Instructor

EDUCBA

1,343 Courses297,058 learners

Offered by

EDUCBA

Explore more from Data Analysis

University of California, Santa Cruz
Bayesian Statistics: Techniques and Models
Course
University of California, Santa Cruz
Bayesian Statistics: From Concept to Data Analysis
Course
Databricks
Introduction to Computational Statistics for Data Scientists
Specialization
Tufts University
Hypothesis Testing with Python and Excel
Course

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

5 stars
51.85%
4 stars
44.44%
3 stars
3.70%
2 stars
0%
1 star
0%

Showing 3 of 27

Reviewed on Feb 3, 2026

It transformed my understanding of uncertainty in experiments. Moving from Excel tables to PyMC models felt like a natural, powerful progression for me.

Reviewed on Feb 5, 2026

This course transformed my understanding of A/B testing by introducing Bayesian methods through simple Excel models before advancing into Python analysis.

Reviewed on Feb 9, 2026

A professionally designed course that delivers real value. Bayesian concepts are explained clearly, and the Excel-to-Python A/B testing workflow feels intuitive and industry-relevant.

View more reviews

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.