## ⚖️ Challenge 01 — Evaluation

**Clarity. Reproducibility. Insight.**

This first challenge focuses on process quality rather than leaderboard competition.
Your goal isn’t to “win” — it’s to **build a reproducible analytical foundation** that others can trust, reuse, and extend across the series.

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### 🧩 Evaluation Principles

Submissions are assessed on five core dimensions that define professional data science craftsmanship:

| Area                          | What Reviewers Look For                                                                      |
| ----------------------------- | -------------------------------------------------------------------------------------------- |
| **Structure & Normalization** | Logical, well-documented ERD; correct keys, constraints, and naming consistency.             |
| **ETL Integrity**             | Clean, repeatable pipeline with type coercion, referential checks, and idempotent execution. |
| **Analytics Readiness**       | Demonstrated ability to query or model against the schema (proof it “works”).                |
| **Baseline Modeling**         | A simple, documented predictive example with sound metric and clear reasoning.               |
| **Documentation & Clarity**   | Readable notebooks, data dictionary, and concise explanation of design choices.              |

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### 🧮 Scoring Framework

This challenge is in **open beta**, so evaluation emphasizes learning and collaboration rather than points.
Each submission receives a qualitative review:

| Rating            | Description                                                                   |
| ----------------- | ----------------------------------------------------------------------------- |
| ✅ **Pass**        | Complete, reproducible workflow that meets all major requirements.            |
| ⚙️ **Partial**    | Solid progress with small issues in documentation, reproducibility, or logic. |
| 🧭 **Needs Work** | Missing core elements or unclear reasoning — resubmission encouraged.         |

There is no numeric leaderboard — think of this as a **craft-based studio model**.
Feedback will evolve through discussion threads, peer reviews, and shared improvement examples.

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### 🏅 Recognition Badges

Exceptional submissions may receive informal recognition within the discussion forum:

🥇 **Gold Schema** – Exceptionally clean ERD & ETL; reproducible and elegantly documented
🥈 **Silver Schema** – Complete, functional workflow with minor improvements possible
🥉 **Bronze Schema** – Meets core requirements, demonstrates clear learning and iteration

Future challenges will introduce automated validation tools to check schema integrity and reproducibility.
For now, **the community acts as the reviewer**, focusing on quality, clarity, and openness.

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### 🔮 Looking Ahead

As the series matures, the evaluation framework will expand to include:

* **Automated reproducibility tests** (schema builds, ETL checks, data validation)
* **AI-assisted review summaries** highlighting best practices
* **Peer learning credits** for meaningful reviews and contributions

Challenge 01 establishes the **standard of transparency** that every later challenge builds upon —
because in data science, **trust begins with structure**.