Lesson Plan

• Describe common data quality issues such as missing values, duplicates, and inconsistent formats.
• Apply a systematic cleaning process to a raw dataset using spreadsheet or programming tools.
• Perform basic data transformations (normalisation, encoding, aggregation) and justify their use.
• Validate the cleaned data by re‑running summary checks and comparing key totals.

• Projector and screen
• Laptop for teacher demonstration (Python with pandas installed)
• Student laptops or computer lab PCs
• Sample CSV file (e.g., sales.csv) printed and digital copies
• Spreadsheet software (Excel or Google Sheets)
• Handout summarising the step‑by‑step cleaning process

Begin with a quick visual of a messy data table and ask students what problems they see. Recall that reliable analysis depends on clean data, linking to previous lessons on data collection. State that by the end of the lesson they will be able to clean and transform a dataset confidently.

1. Do‑now (5'): Students examine a printed CSV excerpt and list observed issues.
2. Mini‑lecture (10'): Explain why data cleaning matters and review common quality problems.
3. Demonstration (15'): Live cleaning of the sample file in Python/pandas – inspect, handle missing values, remove duplicates, standardise dates, correct types, treat outliers, apply transformations.
4. Guided practice (15'): In pairs, students replicate the process using Excel or Google Sheets on a second dataset.
5. Check for understanding (5'): Quick Kahoot quiz on identifying issues and appropriate handling methods.
6. Consolidation (5'): Recap the eight‑step cleaning pipeline and discuss common pitfalls.

Summarise the key steps of the cleaning pipeline and emphasise the importance of validation. Students complete an exit ticket describing one issue they fixed and the method used. For homework, they clean a provided raw dataset and submit the cleaned CSV with a brief reflection.