L01 Data to Intelligence

What this lecture is about

Data wrangling turns raw files into usable evidence. The workflow is: collect, clean, validate, transform, analyze, and explain.

Key ideas

• Raw data is rarely ready for analysis.
• Cleaning must be reproducible, not manual guesswork.
• Validation checks protect the final insight from broken inputs.
• Automation matters when the same workflow repeats across many files.

Student review notes

1. Identify the source and format of each dataset.
2. Check missing values, duplicates, inconsistent labels, and outliers.
3. Document every transformation so another student can reproduce the result.
4. Explain what the processed data can and cannot prove.

Assignment link

Use this structure when writing the methodology section: source, preparation steps, validation checks, limitations, and evidence produced.