It combines conceptual clarity with hands-on implementation. The Bayesian approach to A/B testing is presented in a practical, decision-focused way. Transitioning from Excel to Python was seamless and empowering for real-world analytics applications.

I particularly enjoyed the structured explanation of prior and posterior probabilities. The Excel exercises are beginner-friendly, and the Python section is well-paced. A highly recommended course for experimentation and analytics professionals.

The instructor explains complex ideas in a straightforward way. This course truly elevates experimentation skills.

The instructor simplifies Bayesian thinking without oversimplifying the math. I loved the structured exercises and real-world A/B testing case studies. Moving from Excel models to Python code felt smooth and rewarding.

I appreciated the clear progression from spreadsheet intuition to Python automation. The course demystifies Bayesian inference and makes A/B testing more strategic and data-driven rather than guesswork-based.

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.

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

Mastering Bayesian methods here gave me the edge in my senior analyst interview. The focus on real-world uncertainty is a game-changer for business strategy.

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

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

It transforms complex Bayesian ideas into actionable insights and smoothly guides learners from spreadsheet analysis to Python-based experimentation.

A transformative course for analysts seeking modern experimentation techniques. Bayesian thinking feels intuitive after this training.

The transition into Python for hierarchical modeling is exactly what is needed for modern, scalable healthcare data science projects.

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

Good course to understand theory intuitively, but Pymc package being used is outdated , so commands won't work with new Pymc package