Analysing Data in Excel using ChatGPT, Co-pilot and more — Lesson 1: AI-Assisted Excel Data Preparation and Question Framing

Learning Objectives

• Frame analytical questions, identify required data, prepare structured tables, clean records, and define expected outputs
• Write effective prompts that include context, constraints, examples, privacy boundaries, and validation requirements
• Validate ai-assisted excel data preparation and question framing decisions with documented requirements, evidence, risk controls, and measurable acceptance criteria

Detailed Topics

• Frame analytical questions: Frame analytical questions, identify required data, prepare structured tables, clean records, and define expected outputs
• Write effective prompts that include context: Write effective prompts that include context, constraints, examples, privacy boundaries, and validation requirements
• Validate ai-assisted excel data preparation and question framing decisions with documented requirements: Validate ai-assisted excel data preparation and question framing decisions with documented requirements, evidence, risk controls, and measurable acceptance criteria

Guided Demonstration

1. Review the requirement, users, data, dependencies, permissions, risks, and expected result.
2. Demonstrate the relevant configuration, development, analysis, administration, testing, or operational workflow step by step.
3. Capture settings, outputs, logs, screenshots, calculations, test results, and other useful evidence.
4. Compare the observed result with acceptance criteria and explain any corrective action.

Hands-On Activity

Create a requirements and readiness checklist, then design or configure the first working foundation.

• Document assumptions, prerequisites, and the planned sequence before starting.
• Complete the activity and preserve evidence of normal, boundary, and failure behavior.
• Record issues, corrective actions, reusable notes, and the next improvement priority.

Scenario Analysis

• Identify the business or technical decision that must be made.
• Compare valid implementation approaches and their trade-offs.
• Consider security, privacy, accessibility, performance, support, and operational impact.

Troubleshooting Focus

• Separate symptoms from causes and collect evidence before applying a fix.
• Check configuration, data, permissions, dependencies, capacity, connectivity, and recent changes.
• Validate rollback, recovery, ownership, user impact, and business continuity.

Validation Checklist

• The requirement, scope, inputs, and expected outcome are clear.
• The selected design or configuration is documented and follows approved standards.
• Normal, boundary, failure, security, accessibility, and operational conditions are reviewed.
• The result is supported by evidence and the next improvement is prioritized.

Lesson Outcome

Learners can explain and apply AI-Assisted Excel Data Preparation and Question Framing in a realistic scenario and validate the result with documented evidence.