Live course on Maven · 5 weeks · 10 sessions

AI Analytics for Everyone

Transform from consumer of analytics to independent operator, asking sharp questions, validating answers, shipping decisions.

84
lessons
10
live sessions
5
projects
Shane ButlerHai GuanSravya Madipalli
Taught by Shane Butler, Hai Guan and Sravya Madipalli
$1,800
★★★★★4.9/5
15 reviews on Maven
$1,295 self-paced, start any time
Next cohort
Oct 19 to Nov 22, 2026
Format
84 lessons, 10 live sessions, 5 projects
Enroll on Maven
Ten or more seats get 30% off. Team pricing
This course is for
Product Data Science thinking and workflows
Decision-making and influence training
Hands-on with real analytical scenarios
AI as accelerator, judgment as foundation
This course is not
A SQL or Python course
Statistics lectures
Prompt engineering
Passive video watching

What you build each week

WEEK 1

Think like a product data scientist

Your VP Slacks you: "Checkout conversion dropped. Can you look into it?" Most people open a dashboard. You'll learn to ask the right questions first, turning vague requests into sharp, decision-forcing analysis.

You leave with
A Problem Brief with 5 ranked analytical questions that each pass a quality checklist
WEEK 2

Set up your AI analytical toolkit

Support tickets spiked. You investigate the same question three ways, in Claude Code, Hex, and Querio, and learn when each tool fits.

You leave with
A working analytical environment + your first published end-to-end analysis
WEEK 3

Metrics and root cause analysis

Checkout conversion dropped 20%. Three teams define "active user" differently.

You leave with
A metric spec with guardrails + a root cause analysis memo
WEEK 4

Experimentation and causal thinking

Power users have 2x retention, but is that cause or correlation? You'll draw causal DAGs, design an A/B test for a checkout redesign, interpret mixed results, and learn what to do when you can't run an experiment at all.

You leave with
An experiment brief OR a causal analysis design (two equal paths)
WEEK 5

Storytelling and influence

Great analysis gets ignored when you bury the insight on slide 23. You'll size a $900K opportunity, stress-test your assumptions, and build a 3-slide executive readout that gets the decision made.

You leave with
A 3-slide executive readout + opportunity sizing model + final portfolio package

What you get

Plan for 4-6 hours per week: ~3 hours of async lessons and ~1-2 hours of live workshops and exercises. Everything is recorded if you miss a session.

Templates
Analysis Design Template
Metric Spec (7-component)
Root Cause Memo
Experiment Brief
3-Slide Executive Readout
AI Skills for Claude Code
Question Quality Coach
Metric Definer
Root Cause Investigator
Experiment Designer
AI Analyst System
Running Example
12 tables, 50,000+ rows
Real messy-data scenarios
Simpson's Paradox, Power User Fallacy, and more baked in

What students say

From the 15 public reviews on Maven.
★★★★★

“The most valuable thing I took away was how to better structure my thinking as an analyst. The tools only get you so far but the thinking works no matter what tools come and go.”

Storm
Fractional Product Generalist
★★★★★

“Despite being 14 years into the analytics field, I am glad I took this one. Opened up so many perspectives.”

Avinash
Founder and consultant, DecisionNudge
★★★★★

“It's a good class for analytics professionals. It's like a masterclass for BI/DA/DS.”

Shirley
Data Scientist, Stanford Children's Health
★★★★★

“The course gave me a strong foundation in leveraging Claude Code for analytical workflows. It was engaging, challenging and just what I needed to set me on the right track.”

Ahmad
Independent Consultant, D Cubed Analytics
★★★★★

“Great class. Learned a lot and putting to actual use. Highly recommended.”

William
Co-Founder and CEO, Timuro

Your instructors

Shane Butler
Shane Butler
Co-founder, AI Analyst Lab
10+ years in product data science, causal inference and AI evaluation at Stripe, Nextdoor and Ontra.
Hai Guan
Hai Guan
Co-founder, AI Analyst Lab
Ran data at Nextdoor, LinkedIn, Pinterest and Meta. 16+ years teaching product people to decide with data.
Sravya Madipalli
Sravya Madipalli
Sr Manager, Data Science at Superhuman
14+ years building data science teams at Microsoft, eBay and Nextdoor.
Taking the course as a team, or expensing it?

Ten or more seats get 30% off. Private cohorts run on your schedule. Maven issues an invoice you can expense, and we can send a short note for your manager on request.

Questions people ask before they enroll

Do I need to know SQL or Python?

No. This course teaches analytical thinking and decision-making, not coding. AI handles execution, you learn the judgment that AI cannot replace.

How much time per week?

Plan for 4-6 hours per week: ~3 hours of async lessons and ~1-2 hours of live workshops and exercises. Everything is recorded if you miss a session.

What if I fall behind?

All content stays available after the cohort ends. You also get lifetime access to recordings and materials. Many students finish at their own pace.

Is this relevant if I already have a data team?

Especially then. The goal is analytical independence, asking better questions, interpreting results confidently, and unblocking yourself instead of waiting in a queue.

What do I get at the end?

Five portfolio-ready artifacts (problem brief, published analysis, metric spec, experiment brief, executive readout), a full AI analyst toolkit, and a community of peers.

Next cohort starts Oct 19.

$1,800 · ★ 4.9/5 from 15 reviews · Or come to a free workshop this Wednesday. Register free
Enroll on Maven