Bayesian Statistics: Excel to Python A/B Testing

位教师：EDUCBA

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3个模块

深入了解一个主题并学习基础知识。

18 条评论

6 小时完成

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自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

18 条评论

6 小时完成

灵活的计划

自行安排学习进度

您将学到什么

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.

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作业

10 项作业

授课语言：英语（English）

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该课程共有3个模块

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.

涵盖的内容

8个视频4个作业

8个视频总计58分钟

Introduction to Bayesian Machine Learning 9分钟
Example of Bayesian Machine Learning 7分钟
Example of Bayesian Machine Learning Continues 7分钟
MCMC Module of PYMC Implementation 7分钟
Running the MCMC Module 6分钟
Multiple Variant Testing Using Hierarchial Model 9分钟
Example of Multiple Variant Testing 4分钟
Example of Multiple Variant Testing Continues 9分钟

4个作业总计80分钟

Graded - Foundations of Bayesian Machine Learning 30分钟
Getting Started with Bayesian Learning 10分钟
MCMC in Action with PyMC 10分钟
Hierarchical Models for Multi-Variant Testing 30分钟

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.

涵盖的内容

7个视频3个作业

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.

涵盖的内容

4个视频3个作业

位教师

EDUCBA

1,005 门课程 224,281 名学生

提供方

EDUCBA

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学生评论

5 stars
27.77%
4 stars
66.66%
3 stars
5.55%
2 stars
0%
1 star
0%

显示 3/18 个

已于 Feb 3, 2026审阅

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

已于 Feb 15, 2026审阅

The transition from spreadsheets to Python coding is seamless, making Bayesian A/B testing accessible and highly practical.

已于 Feb 14, 2026审阅

An impressive course that balances theory and application, empowering learners to confidently perform Bayesian A/B testing from spreadsheets to Python scripts.

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