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AI Career Skills Course
Module 6: AI For Data And Analysis
AI, Machine Learning and Generative AI Basics
LLMs, Tokens and Context Windows
Hallucinations, Confidence and AI Limitations
Clear Task Prompts and Output Formats
Context, Examples and Few-Shot Prompting
Prompt Debugging and Iteration
AI for Writing, Summaries and Emails
AI for Research and Meeting Notes
AI Automation for Routine Office Work
AI Study Planning and Concept Learning
AI Practice Questions and Feedback Loops
Using AI Responsibly in Assignments
AI for Code Explanation and Debugging
AI for Tests, Refactors and Code Review
AI Pair Programming Workflow
AI for Spreadsheet Cleanup and Analysis
Asking Better Data Questions
Chart, Insight and Decision Summaries
Embeddings and Semantic Search
RAG Workflows and Knowledge Bases
AI Agents, Tools and Automation
Privacy, Sensitive Data and Access Control
Bias, Fairness and Harmful Output Checks
Evaluating AI Answers Before Use
CONTENTS

Asking Better Data Questions

Convert vague analysis requests into measurable, answerable questions.

AI Career Skills Course
Module 6: AI For Data And Analysis
AI career skills
generative AI
+6
May 28, 2026
27
A

Learning Outcome

Convert vague analysis requests into measurable, answerable questions.

Core Ideas

  • Metric: A measurable quantity.
  • Segment: A subset such as region or user group.
  • Baseline: Comparison point for change.
  • Confounder: A factor that can distort interpretation.

Career Use Case

A manager can turn 'why are sales down' into measurable questions about period, segment, channel, and comparison baseline.

Practical Workflow

  1. Start by naming the outcome: what should improve after using Asking Better Data Questions?
  2. Add the input material, constraints, and success criteria before asking for output.
  3. Ask for assumptions and uncertainty when the answer affects a real decision.
  4. Verify important claims, numbers, and policy statements before publishing or acting.

Hands-On Mini Task

  • Rewrite one vague data request into a measurable question with metric, timeframe, segment, and decision use.
  • A good question can be answered from available data and leads to a decision.
  • Before moving on, explain how Metric and Segment change the decision.

Common Mistakes

  • Using a generic prompt when the task needs clear context.
  • Accepting polished wording as proof of accuracy.
  • Sharing private data without redaction or approval.
  • Skipping a final human review for important decisions.

Quick Revision

Module 6: AI For Data And Analysis lesson 17 is about practical judgement: use AI to increase speed, but keep the goal, context, evidence, and accountability clear.

FAQs

Is Asking Better Data Questions only for technical users?

No. The course treats AI as a practical workplace and learning skill, with technical depth only where it improves judgement.

Should I trust AI output immediately?

No. Use AI to accelerate work, then verify facts, privacy, source fit, and reasoning before relying on the result.

What should I practice after this lesson?

Rewrite one vague data request into a measurable question with metric, timeframe, segment, and decision use.

How does the linked practice quiz help?

The practice quiz checks the lesson concepts immediately with feedback, while the paid mock bundle uses separate assessment questions.

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mediumAI Career Skills
Practice Quiz 17: Asking Better Data Questions
12 questions12 min

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Lesson 2 of 3 in Module 6: AI For Data And Analysis
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AI for Spreadsheet Cleanup and Analysis
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Chart, Insight and Decision Summaries
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