Applied Data Analytics with Excel, SQL, Power BI and Python — Lesson 1: Analytics Requirements, Data Quality and Excel Preparation

Learning Objectives

• Explain and apply analytics requirements, data quality and excel preparation within a practical Applied Data Analytics with Excel, SQL, Power BI and Python scenario
• Validate analytics requirements, data quality and excel preparation decisions using requirements, configuration evidence, quality checks, risks, and measurable outcomes

Detailed Topics

• Explain and apply analytics requirements, data quality and excel preparation: Explain and apply analytics requirements, data quality and excel preparation within a practical Applied Data Analytics with Excel, SQL, Power BI and Python scenario
• Validate analytics requirements, data quality and excel preparation decisions using requirements, configuration evidence, quality checks, risks, and measurable outcomes: Validate analytics requirements, data quality and excel preparation decisions using requirements, configuration evidence, quality checks, risks, and measurable outcomes

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 Analytics Requirements, Data Quality and Excel Preparation in a realistic scenario and validate the result with documented evidence.