Agentic Analytics: Build an AI Analyst
Leave with a working AI analyst on your machine, a test suite that scores it, and a package you can show your boss.
What changes in five weeks
What you build each week
Foundations of agentic analytics
Build one skill on Tuesday and a whole small system on Friday, and check it still works after its memory is cleared.
Design the system, connect MCPs and a warehouse
Move your work into the full system, point it at a warehouse, and publish to a workspace your team already reads.
AI evals for AI analytics
Tuesday works without an answer key: does it agree with itself, is there a source behind every number, do four methods agree on one question. Friday adds the answer key: questions with known answers, and an AI judge you calibrate.
Context engineering and management
Run the same metric three ways, then write down what a contested metric means so the analyst reads it every time. Change one thing, re-run the tests, read what moved.
Open source models and leading an AI-native data team
Decide where a model should run for your company, run the course tests on a second model, and reconcile a disagreement. The last session is a workshop on the system you built.
What you get
Two live sessions of two hours each week, one homework exercise of 30 to 60 minutes, and an optional office hour. Homework is never a prerequisite for the next session.
What students say
“Best course I've attended so far. Fully hands-on, with three instructors who were friendly, real, and not afraid to say 'I don't know.'”
“Loved it, gained actionable skills, would recommend to any analytics team.”
“Especially helpful for beginners looking to explore how AI-enabled workflows can accelerate insights without compromising rigor or accuracy.”
“This course completely changed my idea of data science work of the future. I have a new direction for self-directed learning and a whole world of new ideas for my career.”
“Practical, engaging, and a strong introduction to AI analytics. It was especially useful to see how Claude can support real-world business thinking and workflow design.”
“You'll come out of the course with tons of ideas for agents and skills you can create to make your company's data org operate more efficiently.”
Your instructors
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 coding experience?
No. Everything is built with markdown and natural language. You need a terminal, a Claude subscription, and about five hours a week.
How much time per week?
Two live sessions of two hours, one homework exercise of 30 to 60 minutes on a guided path, and an optional office hour. Homework is never a prerequisite for the next session.
Can I use my own company data?
Every exercise runs on NovaMart, the course dataset, so you are never blocked by access. You leave with a guided path for your own data, applied after the course under your company's rules.
What if I miss a session?
Every session is recorded and every session has a written guide. You also get a free retake of a later cohort.
How is this different from AI Analytics for Everyone?
AI Analytics for Everyone teaches the analytical thinking: the questions, the metrics, the decisions. This course teaches you to build the system that does the work and to prove its numbers are reliable. Many people take both.
Can my company pay for this?
Yes. Maven issues an invoice you can expense. Ten or more seats get 30% off, and we can send a short note for your manager on request.
Who is this course not for?
People who want a prompt-engineering course, a coding course, or a certificate for watching videos. Every session is hands-on building.