AI Analytics for Builders
Shane Butler
Principal Data Scientist @ Ontra
Sravya Madipalli
Data Science Leader |Ex-Microsoft
Ask Better Questions, Make Faster Decisions - with AI as Your Analyst
The future belongs to full-stack builders, not narrow specialists. As AI reduces the value of deep domain silos, the people who thrive will be the ones who can work across disciplines and ship.
You can already prototype designs, write code, and generate content with AI. But analytical independence - the ability to validate your own ideas and make data-informed decisions - remains the missing skill in most builders' toolkits.
AI can conduct the analysis, but it can't tell you what to ask or whether the answer makes sense. That requires analytical judgment.
This course teaches you to think like a senior Product Data Scientist. You'll learn the frameworks used at FAANG companies to frame problems, choose the right metrics, diagnose root causes, execute the analysis, and turn insights into decisions.
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What makes this different: No SQL. No stats lectures. Five weeks of hands-on real scenarios. You'll build a complete analytical case study - metric specs, deep dives, experiment plans, stakeholder readouts.
The result: You become analytically self-sufficient. The last dependency removed from your toolkit. Make data-informed decisions in hours, not weeks.
What you’ll learn
Transform from consumer of analytics to independent operator - asking sharp questions, validating answers, shipping decisions.
Transform vague ideas into decision-forcing questions.
Distinguish good analytical questions from time-wasting ones using a 3-part framework.
Build a ranked list of analytical questions by expected impact - so you always know what to analyze first.
Define numerators, denominators, time windows, and edge cases so your team never debates "what this number really means" again.
Build metric trees that decompose north star metrics into actionable drivers - separating guardrails from success metrics.
Use AI to draft comprehensive metric specs, generate segment cuts, and run QA checks that catch ambiguity before it breaks trust.
Decompose metric changes into mix shift vs within-segment change - so you know if the problem is who showed up or what they did.
Use funnel debugging and cohort analysis to isolate the real drivers, not just correlations that look interesting but don't matter.
Generate hypothesis trees with AI, then systematically rule them out with evidence - turning "something changed" into "here's why."
Write testable hypotheses with clear success criteria and decision rules.
Leave with an Experiment Brief ready for your product development team.
Use AI to accelerate analysis while validating every output.
Leave with a complete capstone: problem brief to stakeholder readout.
Synthesize analytical findings into strategic recommendations with clear priorities.
Leave with a data-backed roadmap executives trust.
Learn directly from expert instructors
Who this course is for
Product Managers who need analytical independence
Designers and researchers stepping into strategy roles who need to quantify impact, run experiments, and influence with data-driven insights
Engineers and operators who ship features but need the analytical frameworks to measure success, diagnose issues, and prioritize next steps
Course syllabus
Week 1
Foundations – Thinking Like a Product Data Scientist
Week 2
AI-Powered Analysis Fundamentals – Claude Code & Your Analytical Toolkit
AI Analytics Hands-On Practice
Free resource
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Schedule
Live sessions
2-3 hrs / week
Office Hours
Projects
2-3 hrs / week
Capstone Project
Async content
3-4 hrs / week
Recorded Lessons
Frequently asked questions
$1,200
USD