### 🎯 Challenge 02 — Objectives

**From Data Chaos to Data Confidence**

In this stage, you’ll learn how to **detect, repair, and validate** data-quality issues so every downstream analysis and model rests on a trustworthy foundation.
You’ll practice the essential skills of a modern data scientist: turning “structured but messy” data into something reproducible, auditable, and ready for AI.

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#### 🧭 Core Objectives

You’ll expand on the schema from **Challenge 01 (Spaghetti → Schema)** and build the habits of professional data stewardship:

1. **Audit for errors** — Profile missing values, duplicates, drifted categories, time-zone inconsistencies, and outlier spikes.
2. **Repair with integrity** — Apply multiple imputation methods (mean, median, regression, kNN, time-aware) while preserving data meaning.
3. **Validate results** — Confirm that cleaning hasn’t introduced bias or distorted key distributions.
4. **Document transparently** — Produce clear audit logs, change summaries, and reproducibility notes others can verify.

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#### 🧰 Broader Learning Outcomes

By completing this challenge, you’ll learn to:

* Treat **data quality as part of modeling**, not a separate cleanup task.
* Create **reproducible audit notebooks** and data-quality reports that show your reasoning.
* Use **Python tooling** (pandas, missingno, scikit-learn imputers) to automate checks and fixes.
* Communicate results clearly — in both technical notebooks and concise summaries.

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#### 🪜 Looking Ahead

Challenge 02 is where data science becomes accountable.
The practices you build here — documentation, reproducibility, and statistical validation — prepare you for **Challenge 03 (Data → Signal)**, where you’ll use this trusted dataset to model patterns and forecast the future.