# Data Academy Honors - Badges

Five badges representing core competencies in data science and machine learning for chemical engineering applications.

---

## Badge Requirements

Each badge requires:
1. **Quiz Average**: 75%+ on relevant section quizzes
2. **Assignments**: Complete all coding assignments in the section
3. **Badge Activity**: Complete one activity from the options below

---

## Badge 1: Data Wrangler 🔍

*"Before you can analyze data, you must understand it."*

**Champions:** Nadia Null, Otto Outlier, Practice Panda

**Lectures:** 01-NumPy, 02-Pandas Intro, 03-Intermediate Pandas

### Core Competencies
- Load and inspect datasets (CSV, Excel, databases)
- Handle missing data appropriately
- Detect and address outliers
- Perform data transformations and aggregations
- Create exploratory visualizations

### Key Skills Demonstrated
- `pandas` DataFrame manipulation
- Missing value strategies (drop, fill, impute)
- GroupBy operations and pivoting
- Basic matplotlib/seaborn plots
- Data quality assessment

### Badge Activities (choose one)

| Activity | Description | Deliverable |
|----------|-------------|-------------|
| **EDA Report** | Perform exploratory analysis on a chemical engineering dataset | Jupyter notebook with visualizations and insights |
| **Data Cleaning Challenge** | Take a messy dataset and clean it systematically | Before/after comparison with documentation |
| **Missing Data Investigation** | Analyze patterns of missingness in real data | Report on missingness mechanisms and chosen strategy |

---

## Badge 2: Pattern Seeker 🗜️

*"Less is more—find what matters."*

**Champions:** Dee Dimension, Viz Vizzy, Cora Correlation

**Lectures:** 04-Dimensionality Reduction

### Core Competencies
- Understand the curse of dimensionality
- Apply PCA for linear dimensionality reduction
- Use t-SNE/UMAP for visualization
- Interpret reduced representations
- Choose appropriate number of components

### Key Skills Demonstrated
- Variance explained analysis
- Loading interpretation
- Visualization of high-dimensional data
- Feature correlation analysis
- Dimension selection strategies

### Badge Activities (choose one)

| Activity | Description | Deliverable |
|----------|-------------|-------------|
| **Spectra Analysis** | Apply PCA to spectroscopic data (IR, Raman, NMR) | Notebook explaining chemical meaning of components |
| **Process Visualization** | Reduce high-dimensional process data for monitoring | Dashboard showing 2D representation with labels |
| **Feature Study** | Compare PCA vs t-SNE on a chemical dataset | Written comparison with visualizations |

---

## Badge 3: Model Builder 🎯

*"Let me draw you a line through that."*

**Champions:** Reggie Regression, Ridge & Lasso, Val Validation, Barry Bias-Variance

**Lectures:** 05-Linear Regression, 06-Regularization, 07-Nonlinear Methods

### Core Competencies
- Build and evaluate regression models
- Understand bias-variance tradeoff
- Apply regularization (Ridge, Lasso, ElasticNet)
- Use cross-validation properly
- Avoid data leakage

### Key Skills Demonstrated
- Feature scaling and preprocessing
- Hyperparameter tuning
- Model evaluation metrics (MSE, MAE, R²)
- Train/validation/test splits
- Pipeline construction

### Badge Activities (choose one)

| Activity | Description | Deliverable |
|----------|-------------|-------------|
| **Property Prediction** | Build a model to predict a chemical/physical property | Notebook with model selection justification |
| **Regularization Study** | Compare Ridge vs Lasso on multicollinear data | Report with coefficient analysis |
| **Cross-Validation Demo** | Demonstrate why CV prevents overfitting | Tutorial notebook with visualizations |

---

## Badge 4: Ensemble Master 🌲

*"Many trees make a forest of wisdom."*

**Champions:** Forrest Random, Greta Gradient-Boost, Clara Cluster

**Lectures:** 08-Ensemble Methods, 09-Clustering

### Core Competencies
- Build and tune Random Forest models
- Apply gradient boosting (XGBoost, LightGBM)
- Understand bagging vs boosting
- Apply clustering algorithms (k-means, hierarchical, DBSCAN)
- Evaluate unsupervised learning results

### Key Skills Demonstrated
- Feature importance analysis
- Hyperparameter optimization for ensembles
- Cluster validation metrics
- Choosing k for k-means
- Interpreting dendrograms

### Badge Activities (choose one)

| Activity | Description | Deliverable |
|----------|-------------|-------------|
| **Ensemble Comparison** | Compare Random Forest vs XGBoost on ChemE data | Notebook with performance and interpretability analysis |
| **Molecule Clustering** | Cluster chemical compounds by properties | Report with cluster interpretations |
| **Process Segmentation** | Use clustering for batch process analysis | Visualization of operating regimes |

---

## Badge 5: Uncertainty Expert 📏

*"A prediction without uncertainty is just a guess."*

**Champions:** Quinn Quantify, SHAP Shapley, Professor Pipeline

**Lectures:** 10-Uncertainty Quantification, 11-Model Interpretability

### Core Competencies
- Quantify prediction uncertainty
- Build confidence/prediction intervals
- Apply SHAP for model interpretation
- Explain model predictions locally and globally
- Communicate uncertainty to stakeholders

### Key Skills Demonstrated
- Gaussian Process regression
- Ensemble uncertainty methods
- SHAP value computation and plots
- Feature importance comparison methods
- Clear uncertainty communication

### Badge Activities (choose one)

| Activity | Description | Deliverable |
|----------|-------------|-------------|
| **Uncertainty Report** | Build a model with full UQ for a design decision | Report suitable for engineering decision-making |
| **SHAP Analysis** | Interpret a black-box model using SHAP | Notebook with global and local explanations |
| **Risk Communication** | Present model predictions with uncertainties | Presentation or document for non-technical audience |

---

## Badge Summary Table

| Badge | Lectures | Key Concepts | Validation |
|-------|----------|--------------|------------|
| Data Wrangler 🔍 | 01-03 | pandas, missing data, EDA | Can clean and explore any dataset |
| Pattern Seeker 🗜️ | 04 | PCA, t-SNE, dimensionality | Can reduce and visualize high-D data |
| Model Builder 🎯 | 05-07 | Regression, regularization, CV | Can build validated predictive models |
| Ensemble Master 🌲 | 08-09 | RF, XGBoost, clustering | Can apply advanced ML methods |
| Uncertainty Expert 📏 | 10-11 | UQ, SHAP, interpretability | Can quantify and explain predictions |

---

## Badge Activity Submission

For each badge activity:
1. Create a Jupyter notebook or report
2. Include clear documentation of your process
3. Explain your reasoning and choices
4. Submit through the course assignment system

**Evaluation Criteria:**
- Technical correctness (40%)
- Clear documentation (30%)
- Insight and interpretation (20%)
- Code quality (10%)

---

*"Every badge represents not just what you know, but what you can do with data."*