# Data Academy Honors - Ranks

Four ranks representing progressive mastery in data science for chemical engineering. Each rank builds on the previous, culminating in Senior Data Scientist status.

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## Rank Progression

```
Data Apprentice → Data Analyst → Data Scientist → Senior Data Scientist
    (Week 4)        (Week 8)       (Week 12)          (Week 16)
```

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## Rank 1: Data Apprentice

*"Beginning the journey into data-driven discovery."*

**Symbol:** 🔍 (Magnifying Glass)

**Timeline:** Achievable by Week 4

### Requirements

| Requirement | Details |
|-------------|---------|
| **Badges** | 1 badge (typically Data Wrangler) |
| **Quizzes** | Passing average on lectures 01-03 |
| **Capstone** | Reflection essay |

### Capstone Activity

**Write a 1-page reflection:**
*"How Data Science Changed My View of Chemical Engineering Problems"*

Prompts to address:
- What surprised you about working with real data?
- How might data science apply to problems in your research or interests?
- What skills do you most want to develop further?

### Recognition
- Digital badge for portfolio
- Listed in course recognition page

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## Rank 2: Data Analyst

*"Demonstrating foundational competence in data analysis."*

**Symbol:** 📊 (Bar Chart)

**Timeline:** Achievable by Week 8

### Requirements

| Requirement | Details |
|-------------|---------|
| **Badges** | 2 badges |
| **Midterm** | Score 70% or higher |
| **Capstone** | Analysis presentation OR peer teaching |

### Capstone Activity (choose one)

#### Option A: Analysis Presentation
Present a 5-minute analysis of a chemical engineering dataset:
- Explain your data exploration process
- Show key visualizations
- Discuss insights and limitations

#### Option B: Peer Teaching Session
Create and lead a 15-minute tutorial on a course concept:
- Topics: Missing data, PCA interpretation, cross-validation, etc.
- Include hands-on exercise for peers
- Demonstrate deep understanding through teaching

### Recognition
- Digital badge for portfolio
- Peer teaching opportunities
- Study group leadership role

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## Rank 3: Data Scientist

*"Working professional level competency in ML/data science."*

**Symbol:** 🎯 (Target)

**Timeline:** Achievable by Week 12

### Requirements

| Requirement | Details |
|-------------|---------|
| **Badges** | 4 badges |
| **Performance** | Maintained 75%+ quiz average |
| **Field Experience** | Complete one real-world experience |
| **Capstone** | Full modeling project |

### Field Experience Options (choose one)

| Experience | Description | Documentation |
|------------|-------------|---------------|
| **Research Collaboration** | Analyze data from a campus research group | Summary of contribution |
| **Industry Connection** | Interview a data scientist at a company | 1-page interview summary |
| **Competition Participation** | Participate in a Kaggle competition | Submission evidence and reflection |
| **Professional Development** | Attend a data science meetup or webinar | Summary and key takeaways |

### Capstone Activity

**Complete a Full Modeling Project:**
- Choose a chemical engineering prediction problem
- Apply the complete data science workflow
- Include proper validation and uncertainty
- Document all decisions and trade-offs

**Deliverables:**
1. Jupyter notebook with full analysis
2. 2-page summary report
3. Brief presentation (5-10 minutes)

### Recognition
- Digital badge for portfolio
- Potential TA or peer mentor role
- Project may be featured in course materials

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## Rank 4: Senior Data Scientist

*"Full mastery of data science for chemical engineering."*

**Symbol:** 🏆 (Trophy)

**Timeline:** Achievable by Week 16

### Requirements

| Requirement | Details |
|-------------|---------|
| **Badges** | All 5 badges |
| **Final Exam** | Score 70% or higher |
| **Project** | Complete semester project with distinction |
| **Capstone** | Presentation with uncertainty quantification |

### Capstone Activity

**Presentation with Full Uncertainty Quantification:**

Present your semester project (10-15 minutes) demonstrating:

1. **Clear Problem Statement**
   - Why this problem matters
   - What decisions depend on the answer

2. **Complete Methodology**
   - Data sources and quality assessment
   - Model selection justification
   - Validation strategy

3. **Results with Uncertainty**
   - Predictions with confidence intervals
   - Sensitivity to assumptions
   - Known limitations

4. **Interpretability**
   - What drives the predictions?
   - SHAP or similar analysis
   - Physical/chemical interpretation

5. **Recommendations**
   - What should stakeholders do with these results?
   - When should the model be trusted vs. questioned?

### Recognition
- Digital badge for portfolio
- Certificate of Distinction
- Project archived in course materials
- Strong recommendation letter support

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## Rank Summary

| Rank | Badges | Exam | Capstone | Week |
|------|--------|------|----------|------|
| Data Apprentice 🔍 | 1 | - | Reflection | 4 |
| Data Analyst 📊 | 2 | Midterm 70%+ | Presentation OR Teaching | 8 |
| Data Scientist 🎯 | 4 | 75%+ avg | Modeling Project + Field Exp | 12 |
| Senior Data Scientist 🏆 | 5 | Final 70%+ | UQ Presentation | 16 |

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## Timeline at a Glance

```
Week 1-4:   Lectures 01-03  →  Data Wrangler badge  →  Data Apprentice
Week 5-8:   Lectures 04-07  →  Pattern Seeker, Model Builder  →  Data Analyst
Week 9-12:  Lectures 08-09  →  Ensemble Master  →  Data Scientist
Week 13-16: Lectures 10-11  →  Uncertainty Expert  →  Senior Data Scientist
```

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## Notes for Success

### Start Early
Don't wait until the last week to attempt badge activities. The quizzes and activities build on each other.

### Document Everything
Keep a running notebook of your analyses. This becomes your portfolio and makes capstones easier.

### Ask for Help
Characters like Query Quinn remind us: asking good questions is a sign of strength, not weakness.

### Connect to Your Research
The most meaningful projects connect data science to problems you care about in chemical engineering.

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*"The rank is not the goal—the skills are. The rank is just recognition of what you've learned to do."*