AI Data Analysis with ChatGPT and Excel

Turn messy business data into structured analysis, clear visualizations, defensible insights, and better decisions

Created byDr. Law Payne

Last updated 10/2026

English

What you'll learn

- Build a practical AI Data Analysis strategy connected to meaningful business outcomes.
- Diagnose workflows, evidence gaps, assumptions, risks, and decision bottlenecks.
- Use AI for research, analysis, generation, and coordination without delegating accountability.
- Apply human review, verification, privacy, and quality controls at the right moments.
- Measure efficiency, quality, user response, business value, and organizational learning.
- Develop a ninety-day roadmap for implementation, governance, and responsible scale.

Course content

1 section • 5 lectures • 48m total length

The Verification-First AI Analysis Workflow9:41
Learning objective: Analyze and apply business question, data dictionary, analysis plan, AI role, evidence standard in a realistic professional or business context.
Lecture description: This lecture explains business question, data dictionary, analysis plan, AI role, evidence standard. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Clean Structure and Understand Business Data9:42
Learning objective: Explain and apply data profiling, missing values, category consistency, outliers, reproducible cleaning in a realistic professional or business context.

Lecture description: This lecture explains data profiling, missing values, category consistency, outliers, reproducible cleaning. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Analyze Drivers Segments and Patterns9:50
Learning objective: Analyze and apply descriptive analysis, segmentation, driver hypotheses, comparison design, statistical caution in a realistic professional or business context.

Lecture description:
This lecture explains descriptive analysis, segmentation, driver hypotheses, comparison design, statistical caution. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Visualize Findings and Communicate Insight9:41
Learning objective: Explain and apply chart selection, visual hierarchy, executive narrative, uncertainty, decision framing in a realistic professional or business context.

Lecture description:

This lecture explains chart selection, visual hierarchy, executive narrative, uncertainty, decision framing. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Build Repeatable Analysis Systems9:43
Learning objective: Design and apply prompt libraries, quality checks, documentation, privacy, continuous improvement in a realistic professional or business context.

Lecture description:

This lecture explains prompt libraries, quality checks, documentation, privacy, continuous improvement. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.

Requirements

- No coding experience is required unless a lecture explicitly demonstrates a technical workflow.
- Access to relevant AI tools is helpful but not required to understand the frameworks.
- Bring a real business, client, team, project, or professional objective for the exercises.
- Be willing to test assumptions and verify AI-generated claims.

Description

This course contains the use of artificial intelligence.

Artificial intelligence creates value when it improves a real decision, workflow, or customer outcome. AI Data Analysis with ChatGPT and Excel provides a structured system for professionals who want to move from raw spreadsheets to decision-ready insight using a repeatable, verification-first analysis workflow.

This course is designed for business analysts, managers, marketers, consultants, entrepreneurs, and professionals who work with Excel data. It moves beyond disconnected prompts and tool demonstrations. Students learn how to define outcomes, organize evidence, design practical workflows, evaluate quality, preserve human accountability, and connect AI activity to measurable results.

Across five substantial lectures, Dr. Law Payne explains the strategic logic and practical execution of AI Data Analysis. Every lecture includes a decision framework, a continuing business case, quality controls, implementation guidance, and an assignment that learners can apply to an organization, client, career, or business.

The course emphasizes responsible practice. Students learn where AI can accelerate research, analysis, generation, and coordination, as well as where privacy, bias, accuracy, security, or reputation require stronger safeguards. The aim is not maximum automation. The aim is better performance with visible ownership.

Dr. Law Payne is a business educator, growth strategist, nine-figure entrepreneur, published author, and Doctor of Business Administration specializing in Organizational Leadership and Development. His experience includes more than one billion dollars in managed sales and more than fifteen years of teaching, coaching, and mentoring across business, psychology, leadership, fitness, and education.

By the end of the course, students will have a working AI Data Analysis operating system and a ninety-day plan for disciplined implementation.

Who this course is for:

- Business analysts, managers, marketers, consultants, entrepreneurs, and professionals who work with Excel data.
- Professionals who want structured workflows instead of prompt collections.
- Teams seeking practical AI adoption with human accountability.
- Learners who want to connect AI activity to customer, operational, or financial outcomes.