Guided labs and worked examples
Small Excel, SQL, Tableau, and AI activities include a starting file, numbered directions, expected result, and self-check.
Courses and learning paths
The course materials begin with plain-language orientation, show complete examples, and move from small practice tasks to a recommendation a student can explain and defend.
Course map
Open a phase to see the purpose, tools, and evidence students create.
Each week has two one-hour sessions. Week 0 is a separate setup orientation. Weekly assignments total 950 points; the Practice Ledger adds 50, for 1,000 total.
Create an evidence inventory, label AI inferences, and choose a capability target.
Submit: Profile, AI exchange, disclosure, and proof target.
Explore where your capabilities create value and how AI changes the work.
Submit: Career map, AI impact map, and proof plan.
Meet the business and frame the $8 million expansion decision.
Submit: Problem brief and evidence request.
Plan the investigation, build an Excel analysis, and reconcile results.
Submit: ORBIT plan, workbook, findings, and checks.
Inspect data quality and query related tables to produce checked evidence.
Submit: Quality log, queries, evidence log, and checks.
Compare explanations and build a defensible evidence chain.
Submit: TRACE analysis and provisional recommendation.
Challenge AI claims, assumptions, and the provisional recommendation.
Submit: AI record, claim audit, red-team notes, and revision.
Explain verified findings through a focused dashboard.
Submit: Storyboard, dashboard, checks, and walkthrough.
Recommend an investment with allocation, gates, risks, and sources.
Submit: One-page memo, allocation, gates, and disclosure.
Defend the recommendation and show evidence of capability growth.
Submit: Board deck, presentation, portfolio, and reflection.
Assessment path
Small Excel, SQL, Tableau, and AI activities include a starting file, numbered directions, expected result, and self-check.
Students record prompts, formulas, queries, decisions, data checks, corrections, citations, and limits as they work.
A concise recommendation supported by evidence, risk, alternatives, implementation steps, and a method for checking results.
Students present their recommendation, answer realistic stakeholder questions, and explain uncertainty without overstating the evidence.
A curated collection of work showing how the student framed, analyzed, checked, communicated, and improved a decision.
Instructor materials
Each major topic is connected to an instructional explanation and an example students can inspect before starting.
Topic-by-topic PowerPoint instruction with key concepts, demonstrations, and discussion prompts.
Plain-language visual references for SPARK, TRACE, tools, workflows, and AI verification.
Model reports, workbooks, queries, dashboards, memos, and presentation structures.
Learning objectives, lesson flow, misconceptions, checks for understanding, and debrief questions.
Student-friendly criteria for evidence, correctness, explanation, judgment, and professional delivery.
Navigate by week, session, student materials, or instructor materials. Authorized sign-in required.
Tool progression
| Tool | What students use it for | First concrete task | How AI supports the work |
|---|---|---|---|
| Excel | Inspecting, cleaning, calculating, and summarizing a manageable file. | Prepare a weekly sales report from a supplied workbook. | Explain a formula, suggest a check, and help diagnose an error. |
| SQL | Asking precise, repeatable questions of data stored in related tables. | Return sales records that meet clearly stated conditions. | Draft a query, then explain and test every clause. |
| Tableau | Turning patterns into visual explanations for a defined audience. | Build a dashboard that supports the expansion decision. | Compare chart choices and critique clarity. |
| AI assistant | Brainstorming, explaining, drafting, critiquing, and checking work. | Turn a vague request into five useful clarification questions. | AI is the support tool; the student remains responsible for verification. |
AI Orchestration and Intelligent Business Systems develops the ability to design and govern an AI-enabled business workflow. It retains a separate 15-week, 30-session university plan and a 15-module self-paced blueprint.
The five stages are Define, Decompose, Delegate, Design, and Deliver. Learners frame outcomes, map tasks and data, assign work to people or tools, design controls, and evaluate the system. The blueprint describes six portfolio projects and a capstone; a complete lesson-by-lesson online course is not represented as released.
Try the public practice lab before committing to the larger case. Faculty can use the teaching resources to plan delivery and assess reasoning.