Advanced Data Analysis with AI
Working analysts: take your data work past spreadsheets. Multi-table SQL with AI as your pair, statistical thinking that survives scrutiny, A/B tests you can defend, time-series limits, decision frameworks. 8 intermediate lessons + harder quizzes + certificate.
If you completed the beginner Data Analysis with AI course at 100% — like most of our completers do — you’re ready for the level the beginner course can’t teach. This is the course that goes there.
The beginner course taught mindset, exploration, visualization, and reporting. Solid foundations. But there’s a gap between knowing those concepts and operating as an intermediate analyst. The gap is five specific capability jumps — and most analysts don’t realize they’re stuck on one until a senior colleague names it.
This course closes the gap. Eight lessons, harder quizzes (Analyze and Evaluate Bloom’s level), real cases drawn from documented practitioner failure modes — the empirical study of 4,602 incorrect AI-generated SQL queries, the Greenland 2016 paper on 25 named p-value misinterpretations, the Johari 2017 KDD paper on the peeking problem, the MIT research on cognitive debt. By the end, your analyses will survive scrutiny in ways the beginner course can’t prepare you for.
Built for working analysts (marketing, product, operations, finance), spreadsheet power users transitioning into rigorous analysis, and consultants who want to ship work that holds up. The course assumes basic SQL and arithmetic-level statistics but does not require Python.
What You'll Learn
- Identify the five capability jumps that separate beginner from intermediate analysts and diagnose your own weakest jump
- Apply AI-assisted multi-table SQL workflows and catch the seven named errors AI commonly makes
- Analyze p-values and confidence intervals correctly using the five named misinterpretations from Greenland 2016
- Evaluate A/B test results against the four named pitfalls (peeking, underpowering, novelty, SRM)
- Apply time-series decomposition and refuse to forecast when any of the five stop-signs are present
- Apply OODA, AARRR, and the data-driven vs data-informed posture to real decisions
- Apply the validation checklist to AI-generated work and avoid cognitive debt
After This Course, You Can
What You'll Build
Course Syllabus
Prerequisites
- You've completed an introductory data analysis course OR have ~6 months of working analyst experience
- You can write basic SELECT/WHERE/GROUP BY SQL on a single table
- You're comfortable with mean, median, percentage change — but you don't need to have used p-values or confidence intervals before
- No Python required — the course teaches AI-assisted workflows for non-coding analysts
Who Is This For?
- Analysts who completed a beginner data analysis course and want the next level
- Working analysts with 6-18 months of experience who feel they've plateaued
- Spreadsheet power users transitioning into more rigorous analytical work
- Marketing, product, and operations analysts who run A/B tests and want to make calls they can defend
- Solopreneurs and consultants who want to ship analyses that hold up to scrutiny from sophisticated clients
Frequently Asked Questions
Is this a follow-up to the beginner Data Analysis with AI course?
Yes. The beginner course teaches mindset, exploration, and reporting. This course teaches the technical and cognitive depth that separates intermediate from beginner analysts — multi-table SQL, statistical inference, A/B test discipline, time-series limits, and decision frameworks. If you scored 100% on the beginner course quizzes (most completers do), this is your next course.
Do I need to know Python?
No. The course is designed for working analysts who use spreadsheets and SQL but don't write Python. AI tools (Code Interpreter, Claude Code) are taught as analyst-facing tools, not as coding environments. If you do write Python, the course is still useful — Python-specific extensions are noted but not required.
How is this course harder than the beginner one?
The quizzes are at Analyze and Evaluate Bloom's level instead of Understand. You'll be given real-world cases (an A/B test that's gone wrong, a forecast a stakeholder is asking for, a SQL query AI generated) and asked to diagnose what's broken using the named patterns from the lessons. The cognitive load is higher but matches what intermediate analyst work actually feels like.
Will I get a certificate?
Yes. Complete all 8 lessons (including the capstone integration) and you get a verifiable Advanced Data Analysis with AI credential. The credential signals more than course completion — the capstone requires you to apply the framework on a real analysis from your work.
