4.12.3: Hands-On Lab Exercise

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Page ID: 58301

Felix Amoruwa
De Anza College

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Hands-On Lab Exercise

Lab: Cleaning a Messy Dataset

Scenario:

You receive a CSV file of 10,000 customer orders with known issues. Write SQL queries or pseudocode to clean each issue.

Issues to address:

Missing values: 500 orders have NULL shipping_address (required field); 800 have NULL discount_code (optional field)
Type mismatch: order_total stored as VARCHAR containing "$" signs
Duplicates: 120 orders appear twice (same order_id)
Invalid values: 45 orders have negative order_total; 30 have order_date in the future
Inconsistency: customer_state field contains "CA", "California", "ca", "Calif."

Instructions:

Profile (15 min): Write queries to quantify each issue (count NULLs, duplicates, invalids).
Clean (30 min): Write SQL for each fix: handle NULLs (delete vs. flag), CAST types, deduplicate, filter invalids, standardize states.
Validate (15 min): Write post-cleaning validation queries proving each issue is resolved.
Document (15 min): Create a cleaning log recording every transformation, rationale, and records affected.
Impact Analysis (15 min): Calculate what percentage of records were modified or removed. Discuss how this could affect downstream analysis.

Deliverables:

Profiling queries with results, cleaning SQL scripts, validation queries, cleaning log, impact analysis.

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