Applied Data Analytics with Excel, SQL, Power BI and Python — Lesson 3: Python Analysis, Statistics and Reproducible Workflows

Learning Objectives

• Explain and apply python analysis, statistics and reproducible workflows within a practical Applied Data Analytics with Excel, SQL, Power BI and Python scenario
• Validate python analysis, statistics and reproducible workflows decisions using requirements, configuration evidence, quality checks, risks, and measurable outcomes

Detailed Topics

• Explain and apply python analysis, statistics and reproducible workflows: Explain and apply python analysis, statistics and reproducible workflows within a practical Applied Data Analytics with Excel, SQL, Power BI and Python scenario
• Validate python analysis, statistics and reproducible workflows decisions using requirements, configuration evidence, quality checks, risks, and measurable outcomes: Validate python analysis, statistics and reproducible workflows 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

Analyze a realistic business case, compare implementation options, and validate the selected approach.

• 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 Python Analysis, Statistics and Reproducible Workflows in a realistic scenario and validate the result with documented evidence.