返回到 Bayesian Statistics: Excel to Python A/B Testing
学生对 EDUCBA 提供的 Bayesian Statistics: Excel to Python A/B Testing 的评价和反馈
27 个评分
课程概述
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.
热门审阅
KN
Feb 15, 2026
The transition from spreadsheets to Python coding is seamless, making Bayesian A/B testing accessible and highly practical.
SJ
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.
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26 - Bayesian Statistics: Excel to Python A/B Testing 的 27 个评论（共 27 个）
创建者 Kanha N
•Feb 16, 2026
The transition from spreadsheets to Python coding is seamless, making Bayesian A/B testing accessible and highly practical.
创建者 A A
•Nov 13, 2025
Good course to understand theory intuitively, but Pymc package being used is outdated , so commands won't work with new Pymc package