AI Analytics for Everyone
Shane Butler
Principal Data Scientist @ Ontra
Hai Guan
Head of Data @ Ontra AI | Ex-LinkedIn
Ask Better Questions, Make Faster Decisions - with AI as Your Analyst
As AI reduces the value of deep domain silos, the people who thrive will be the ones who can work across disciplines and ship.
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 end-to-end, 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
Shane Butler
Principal Data Scientist @ Ontra | Ex-Stripe, Nextdoor, PwC, Appfolio
Hai Guan
Head of Data @ Ontra | Ex-Nextdoor, LinkedIn, Pinterest, Meta
Sravya Madipalli
Data Science Leader@Superhuman (Prev. Grammarly)| Ex-Microsoft, eBay, Nextdoor
Who this course is for
Product Managers, engineers, and operators who need analytical independence and frameworks to measure success and drive decisions.
Designers and researchers stepping into strategy roles who need to quantify impact, run experiments, and influence with data-driven insights
Data professionals who want to frame better questions, delegate technical execution to AI, and drive impact with agentic analytics tools.
What's included
Only in live cohorts
Live sessions
Learn directly from your instructors in a real-time, interactive format.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
10 live sessions • 84 lessons • 5 projects
Live sessions
2-3 hrs / week
Office Hours
Projects
2-3 hrs / week
Capstone Project
Async content
3-4 hrs / week
Recorded Lessons
Week 1
- 📄
Welcome to Week 1: Thinking Like a Product Data Scientist
- 📄
How Product Teams Work
- 📄
What Product Data Scientists Actually Do
- 📄
The Analytics Workflow Loop
- 📄
Practice: Mapping Real Scenarios to Workflow
- 📄
The Question Ladder: From Goal to Hypothesis
- 📄
Practice: Climbing the Question Ladder
- 📄
Good vs Bad Analytical Questions
- 📄
The Six Questions Exercise
- 📄
Impact vs Curiosity Matrix
- 📄
Practice: Sorting Your Question Backlog
- 📄
The Analysis Continuum
- 📄
Reactive vs Proactive Analytics
- 📄
Questions That Sound Smart But Aren't
- 📄
AI Lesson: Generating Questions with Claude
- 📄
AI Lesson: Validating Questions
- 📄
Capstone: Frame Your Problem Brief
- 📄
Week 1 Wrap Up
- 📄
NovaMart Reference Card
- 📄
Week 1 AI Prompts
- 📄
Templates and Worksheets
- ✍️
Week 1 Submissions
Submit by Feb 1
Jan
26
Kick-Off!!!
Jan
28
Live Office Hour
Foundations – Thinking Like a Product Data Scientist
What makes analysis decision-driving vs just interesting?
Week 1 Resources
Submissions
Week 2
- 📄
Your AI Analytical Toolkit
- 📄
AI Can Help You Set Up
- 📄
Installing Claude Code
- 📄
Choosing Your Editor & Cloning the AI Analyst
- 📄
Talking to Claude Code
- 📄
The AI Analyst First-Run Experience
- 📄
DuckDB & the NovaMart Database
- 📄
Schema Discovery & Data Exploration
- 📄
How the AI Analyst System Works
- 📄
What MCP Is and Why it Matters
- 📄
Setting Up Your First MCP (Notion)
- 📄
The Enterprise Analytics Landscape
- 📄
End-to-End Analysis in Claude Code
- 📄
Analysis Design & Spec Creation
- 📄
Tools Change, Thinking Doesn't
- 📄
Week 2 Wrap-up
Feb
2
Week 2 Launchpad
Feb
4
Live Office Hour
AI-Powered Analysis Fundamentals – Claude Code & Your Analytical Toolkit
How do I actually DO the analysis without becoming a data professional?
Free resources
Frequently asked questions
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