Data stakeholders are talking AI and data leads are lost. This 6 week program gives you practical knowledge of AI for your work. You leave with a working agent and confident about what to do about AI. Stop Feeling Lost.
Analysts use AI to write queries faster. An agent goes further. It works round the clock, answers instantly, and multiplies how much your data actually gets used. Stakeholders expect agents now.
Multiply this across hundreds of users. See the difference?
This program is designed around you — senior, time-poor, already fluent in the data. Not a beginner's course on how LLMs work. Here's how that translates into what you actually learn.
Years of knowing what a bad number looks like — the definitions, the caveats, the sanity checks. The one new move is handing that to an agent as context.
Four data leaders, not a 25-person webinar. You get unstuck in the session, the attention is on your build, and you're with people who get your world.
Not a tour of features. You build one real use case end to end — none of the side quests you'd hand a junior anyway.
"Where do I even start with AI?"
Context is the engine powering AI agents, see it in action below. Each time you hand the agent one more thing an analyst knows, and the answer goes from confident-and-wrong to correct.
Nothing about the model changed. Every jump in correctness came from context: sample size, segmentation, the dew rule you already knew. You're starting from years of knowing what a bad number looks like.
Not a 25-person webinar where you watch a speaker. Four data leaders in the room, building with the instructor at their shoulder — and with each other.
Stuck on your setup, your access, your data? It gets fixed there and then — not in a ticket queue between sessions.
Every choice is made against your data and the questions you actually get asked, not a generic demo everyone shares.
Four people who get your world. Trade AI ideas and work through real challenges together — that's half the value.
4 seats per cohort. When they're gone, they're gone.
Not a tour of features. One real use case, built end to end across twelve sessions — the parts of data work that matter, none of the side quests you'd hand a junior anyway. Every week ends in something committed to your repo.
The repo is the receipt
The whole thing runs end to end on my infrastructure, on a dataset you bring or one I give you. You leave able to repeat it on your own stack. Wiring up your company's warehouse, permissions, and stakeholders is a team engagement. That's a separate conversation.
Week titles and file names are illustrative. The shape is real: weeks 1–2 stand the repo up, 3–4 build the agent properly, 5–6 prove it's correct and ship it.
Six weeks in, you own an agent pointed at your data. A stakeholder can trust it because every number opens up.
select
count(distinct case when
days_since_signup between 24 and 33
and is_active_day then user_id end)
* 100.0 / count(distinct user_id) as retention_pct
from fct_user_activity
where signup_cohort = '2026-03';
Building it for work, on your real data? You can host it on your own infrastructure instead. Ask on the call.
Analytics engineering, BI, data modeling, product analytics. The unglamorous parts of data, done for a decade, for teams I led.
I don't teach "AI transformation." I teach data people to build one real thing end to end, then a harder one.
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