Course

AI Analytics for Everyone

4.9 (15)

Shane Butler

Principal Data Scientist @ Ontra

Sravya Madipalli

Data Science Leader |Ex-Microsoft

+ Hai Guan

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

Shane Butler

Contact

Principal Data Scientist @ Ontra | Ex-Stripe, Nextdoor, PwC, Appfolio

Stripe
Nextdoor
PwC India
Ontra
AppFolio
Sravya Madipalli

Sravya Madipalli

Contact

Data Science Leader@Superhuman (Prev. Grammarly)| Ex-Microsoft, eBay, Nextdoor

Microsoft
eBay
Nextdoor
Grammarly
Superhuman
Hai Guan

Hai Guan

Contact

Head of Data @ Ontra | Ex-Nextdoor, LinkedIn, Pinterest, Meta

Hai has worked at:
Ontra
Nextdoor
LinkedIn
Pinterest
Meta
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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

Maven Guarantee

Your purchase is backed by the Maven Guarantee.

Course syllabus

97 lessons • 5 projects

Unit 0: Orientation

    Start here: Welcome to Maven

    3 items

    Orientation Week Videos

    4 items

Unit 1: Foundations

    Unit 1: Start Here

    1 item

    Foundations – Thinking Like a Product Data Scientist

    18 items

    Week 1 Resources

    3 items

    Submissions

    1 item

Free resources

Frequently asked questions

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