Vibe-Coding for Data Analytics Professionals
$149.00
Who this is for
Senior analysts, BI analysts, and analytics managers. You don't need to learn to code from zero — you need the bridge from "I can compute the answer" to "I built a thing my stakeholders can use to make data-driven decisions."
When is it
Week 1 - July 8th, 2026; Week 2 - July 15th, 2026, Week 3 - July 22nd, 2026.
Bonus week: Demo your app! July 29th, 2026.
Time: 7-8pm (Wednesday evenings)
What makes it different from the free content you've already tried
You leave with a deployed app at a public URL on your own data — a portfolio piece for LinkedIn, interviews, and reviews.
You build on messy, multi-table data — not a clean demo file. The hard part real analysts hit (joining inconsistent sources into something analysis-ready) is the part we actually do together.
You learn to trust the output and prove it — grounding the AI so it stays inside the data and says when it can't support a conclusion.
Your app recommends decisions, not just numbers — it names the risk drivers, suggests actions, and flags what to investigate next.
You leave knowing how to run it at work / for your clients — cost, privacy, and governance covered — and how to pitch it: an initiative one-pager and two or three resume bullets the work actually earns.
And the things no recording gives you: feedback on your build through a peer cohort of data pros, and the accountability that gets a busy analyst to actually finish.
Quantity
What we'll cover
What you walk away with:
A deployed app at a public URL, built on your own data
A reusable prompt library and the repeatable clean-prompt-build workflow
An initiative one-pager and two to three credible AI bullets for your resume
A peer cohort and expanded network
Format: Three weeks, one live ~1-hour session per week, plus light work between sessions. Bonus: Demo day on week 4. Small cohort. Designed for about 2 hours a week — around a full-time job, not in spite of one.
Week 1
Get your data AI-ready, and learn to trust the output.
Bring messy, real-world data — free-text fields, inconsistent categories, more than one table. You'll use AI to clean, enrich, and combine it into one analysis-ready layer, and you'll learn the prompt patterns that make the output reliable: how to ground the AI in your data and make it admit when it doesn't know. You leave with an analysis-ready dataset and a prompt library you'll keep using at work.
Week 2
Build and ship your app.
Using Lovable — and the meta-skill of having Claude write your first build prompt — you'll go from your enriched data to a working dashboard with filters and charts, then deploy it to a public URL. You'll see real, deployable code being written; this is an AI pair-programmer, not a black box. You leave with a live app on your own data that you can open on your phone and send to your manager.
Week 3
Make it decide, and make it count.
Your app stops describing and starts advising: a grounded, in-page AI insight that names the risk drivers, suggests actions, and flags what to look at next — plus an honest first look at prediction and connecting to live data, with their limits named rather than oversold. Then we make it real: what it costs, how to handle privacy and governance, and how to pitch it — an initiative one-pager and the resume bullets the work earns.
Bonus: Peer demo day.
Showcase your app to the crowd - stand proud in your achievment!
If you are still on the fence
"I can learn this free." The pieces are on YouTube; the structured path on your real data, ending in a shipped artifact with someone to unblock you, is not.
"I don't have time." Three weeks, 1-hour session each, built around a full-time job.
"Will it move my career?" No promises on your raise — but you leave with a deployable artifact and the language to pitch it, which beats a certificate of completion.