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Leave with a working AI analyst system on your laptop (skills, agents, data connections) and a finished analysis ready for Monday.

Principal Data Scientist @ Ontra 

Shane Butler

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

Data Science Leader |Ex-Microsoft

Sravya Madipalli

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

Head of Data @ Ontra AI | Ex-LinkedIn

Hai Guan

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

Bootcamp 1

This is a hands-on weekend bootcamp (4 hours Saturday, 4 hours Sunday) where you build AI analysts powered by Claude Code. Not a lecture series. Not a demo you watch. You set up your own environment, build custom skills and agents, connect to real data, and leave with a working system that turns business questions into charts, narratives, and presentations in minutes.No Python. No SQL. No coding of any kind. Claude Code writes all the code. You direct the analysis.By Sunday evening, you will have a complete AI analyst repo on your computer: 45 skills, 27 agents, data connections, and a finished analysis you can share with your manager Monday morning. Everything runs locally. No vendor lock-in. No proprietary platform. Just a repo you own.AI is changing how teams make decisions with data. The bottleneck is no longer the analysis — it is knowing how to set up the tools. This bootcamp gives you that setup in a weekend. You will not learn about agentic systems. You will build one.

Build agentic data analysts for your company in one weekend. No code.

Set up Claude Code as your personal AI analyst environment

Build custom skills that encode analytics best practices as markdown

 Build agents that automate multi-step analysis workflows

Produce a complete analysis with charts, narrative, and slide deck

Connect your own company data and analyze it before you leave

Product managers who are tired of waiting in the data team's queue. Stop waiting. Analyze it yourself.

Data scientists who want to multiply their output. You know how to do the analysis manually. Now automate it.

Engineers who want to make data-driven decisions without hiring a data team.

Master the Data Ops systems behind successful AI products, lead execution, reduce risk, and earn trust across Eng, Research, and leadership

Cohort 1

ex-Head of tPgM/Ops @ Google & ex-Apple

Apple

Google

Jeanne Williams

ex-Head of tPgM/Ops @ Google & ex-Chief of Staff @ Apple 

This is the first certification designed specifically for AI Product Teams focused on Data operations, AI operations, and cross-functional execution — built from 20+ years of real experience running AI/ML programs, data pipelines, infra, privacy, and readiness operations at global scale (Google, Apple).You’ll leave with a repeatable system, templates, and a DataOps operating model.ʏᴏᴜ ᴡɪʟʟ ʟᴇᴀʀɴ ʜᴏᴡ ᴛᴏ:<ul><li>Build DataOps workflows (intake → validation → delivery) that scale</li><li>Audit data quality across the 6 core dimensions (freshness, coverage, etc.)</li><li>Translate governance (GDPR/AI Act/CCPA) into real operational processes</li><li>Decide when to automate vs use human-in-the-loop for quality &amp; compliance</li><li>Build observability + measurement loops (golden sets, logs, dashboards)</li><li>Define OKRs and operating models for DataOps as a function</li></ul> ʏᴏᴜ’ʟʟ ʙᴜɪʟᴅ ᴀ ʀᴇᴘᴇᴀᴛᴀʙʟᴇ ᴏᴘᴇʀᴀᴛɪɴɢ ᴍᴏᴅᴇʟ That you can apply at work immediately with our capstone project: <ul><li>Data intake + requirements</li><li>Governance constraints + access model</li><li>Pipeline strategy (ETL/ELT)</li><li>Readiness + quality checks</li><li>Human-in-the-loop plan</li><li>Metrics + monitoring plan</li></ul>

Become the Data Operations leader AI product teams need to ship with confidence

Master DataOps fundamentals for AI-driven organizations

Lead cross-functional alignment across Eng, Research, Product & Privacy

Build DataOps metrics, dashboards & measurement systems

Conduct rigorous data audits and quality assessments

Build governance-aligned data strategies that scale

Tech Team Leaders who must guide AI-driven teams and need DataOps expertise to improve delivery, quality, and cross-functional execution.

Tech PgMs who want to deepen their AI, data, and governance expertise to advance into senior, high-impact roles.

Cross-functional tech professionals who partner with AI teams and need DataOps skills to execute and scale AI initiatives.

Cut analysis time in half and uncover more powerful insights—without sacrificing quality or trust in your work.

Ex-Head of UXR, Spotify Business/SYB

Caitlin Sullivan

Ex-Head of User Research, Spotify Business. 2000+ Hours Testing AI for Research.

Cohort 6

Cohort 7

‼️ Revamped 2026 Curriculum + format. Price will go up after March 1st 2026. Includes content updates from 2 future cohorts! Is this you? You’re sitting in front of a mountain of raw feedback—interview transcripts, survey results, and customer messages from multiple sources. Your brain is fried from collecting it all, but the real challenge hasn’t even started yet: turning that pile into clear, actionable insights. Stakeholders are emailing:👋 “Where’s the analysis?”👋 “When can we share this with the team?”👋 "We need to decide about [product change] NOW." -You've been trying to use AI to speed things up, but it gave you results that didn’t feel reliable.- Vague themes, out-of-context quotes, or flat-out fake "user insights."- You have a constant fear that it missed something critical that *you* would have found.- And after all the time spent, you decide to re-do the analysis manually anyway.-I’ve been there, too. 🙋‍♀️If you’ve felt stuck in endless ChatGPT loops, redoing AI’s work, or wondering if there’s a better way—this is for you.With the right knowledge and systems, AI stops being such a gamble and becomes an ally you can trust for faster - and more complete analysis.

2026: AI isn't optional. It's a core tool - and most people haven't mastered it.

Learn AI Analysis Systems That Deliver 2-10x Faster

Gain Speed Without Sacrificing Quality

Develop a Partner for Reducing Risk

Uncover Hidden Opportunities + POVs

Run Efficient Tool Experiments

Get Confident About AI Decisions 

You’re expected to deliver actionable insights for product decisions fast, even when research isn't even your "main" job.

You're a Designer, Researcher or Product person spending hours digging through customer input, searching for insights that matter.

You’ve tried AI tools, but the results feel “meh.” Instead of saving time, they create more work -- and you're back to doing it yourself.

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

Stripe

Nextdoor

PwC India

Ontra

AppFolio

Microsoft

eBay

Grammarly

Superhuman

LinkedIn

Pinterest

Ask Better Questions, Make Faster Decisions - with AI as Your Analyst

Frame Problems Like a Senior Data Scientist

Design Metrics That Actually Drive Decisions

Diagnose Root Causes Without Guessing

Design Experiments That Ship

Turn Analysis Into Action With AI as Your Copilot

Synthesize Insights and Prioritize for Impact

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.

Learn practical, hands-on methods to evaluate, measure, and improve AI products using data science to make better product decisions.

Principal Data Scientist, AI Evaluations

Principal Data Scientist, AI Evaluations at Ontra

With the recent leaps in model capabilities, it is becoming clear that much of the AI evaluation work previously requiring humans to build and run can be automated through agentic systems. This course teaches you how to build those systems, and where human judgment and input is still required.The foundational work of designing eval frameworks, defining quality signals, and structuring failure analysis remains critical. But the execution of that work, running evaluations, scoring results, iterating on improvements, and surfacing what matters, can now be handled by agentic workflows that operate continuously and at scale.This course equips you with the capability to build, test, and deploy automated evaluation systems using Claude Code. You will leave with working systems, not just conceptual understanding.The focus is on automation that is trustworthy. Systems that score their own output, flag uncertainty, and know when to surface decisions to a human rather than proceeding autonomously.The foundational content on AI evals for product development is available free at <a target="_blank" rel="noopener noreferrer nofollow" href="https://aianalystlab.ai">aianalystlab.ai</a>. If you are new to AI evals, start there.

Build the agentic systems that automate your AI evaluation workflow from end to 

Build agentic evaluation workflows in Claude Code

Automate scoring and quality assessment

Implement iterative improvement loops

Scale experiment execution

Integrate with production data and decision-making

You will get the most out of it if you are actively working on, or about to ship, AI features in a real product.

Real-World AI Product Experience

Product Managers: Want automated eval systems for your team? Learn what is possible, reliable, and where human oversight stays essential.<pre><code class="language-markdown"></code></pre>

Data Scientists &amp; Analysts: Spending more time running evals than learning? Build systems that handle execution so you focus on decisions.

Engineers and ML Engineers: val bottlenecks slowing your release cycle? Automate repetitive work and ship with speed and confidence.

AI Fundamentals

Apply AI in your role

Career Development

AI Fundamentals

Career development

Craft

AI Fundamentals

Career development

Craft

AI Fundamentals

Career development

Craft

AI Fundamentals

Craft

AI Fundamentals

Leadership fundamentals

AI Fundamentals

Leadership fundamentals

By Role

Data Analysis

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