Experimentation for Data Scientists & Data Analysts

Module Instructor

Banani Mohapatra

Senior Manager, AI/ML & Data Science at Walmart

Course Overview

In this course, you'll master how to design, run, and evaluate experiments that drive real product decisions. Learn from industry expertise how to bridge the gap between statistical rigor and business impact.

What You'll Learn

• Foundations of Experimentation — Core principles and statistical foundations
• Experimentation Across the Product Lifecycle — When and how to test at each stage
• The Experiment Lifecycle Deep Dive — From hypothesis to implementation to analysis
• Measuring Long-Term Impact — Techniques for understanding effects beyond the experiment window
• Interview Case Studies — Real-world scenarios and frameworks for success
• Alternative Methods — Causal inference approaches when A/B testing isn't possible

Your instructor's background

Banani brings 13+ years of experience leading data science initiatives across e-commerce, payments, and real estate. At Walmart, she has driven significant business impact through experimentation and causal inference:

• Led experiments generating $10MM+ in incremental revenue
• Launched fraud reduction features saving $50MM in costs
• Implemented advanced causal inference frameworks (propensity score matching, difference-in-differences, Bayesian modeling)
• Developed dynamic testing algorithms enabling 1000+ simultaneous content variations

Her expertise spans A/B testing, causal inference, growth experimentation, and AI/ML product development. She regularly presents to senior leadership and has spoken at industry conferences including MIT CODE, Grace Hopper Celebration, and IEEE GenAI Summit.