data-science

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
name: data-science
description: Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy. Use when building ML models, analyzing data, creating dashboards, or designing data architectures.
---

# Data Science Expert

Comprehensive data science frameworks for analytics, machine learning, and data-driven decision making.

## Data Strategy

### Data Maturity Model

| Level | Name                | Characteristics                           |
| ----- | ------------------- | ----------------------------------------- |
| 1     | **Ad Hoc**          | Manual, inconsistent, siloed              |
| 2     | **Opportunistic**   | Some automation, point solutions          |
| 3     | **Systematic**      | Defined processes, governance emerging    |
| 4     | **Differentiating** | Data-driven decisions, advanced analytics |
| 5     | **Transformative**  | AI-first, competitive advantage           |

### Analytics Value Chain

```
DATA → INFORMATION → INSIGHT → ACTION → VALUE

PROGRESSION:
Descriptive: What happened?
Diagnostic: Why did it happen?
Predictive: What will happen?
Prescriptive: What should we do?
Autonomous: Self-optimizing systems
```

## Statistical Analysis

### Descriptive Statistics

```
CENTRAL TENDENCY:
- Mean: Sum / Count (sensitive to outliers)
- Median: Middle value (robust to outliers)
- Mode: Most frequent value

DISPERSION:
- Range: Max - Min
- Variance: Average squared deviation
- Standard Deviation: √Variance
- IQR: Q3 - Q1 (robust)

DISTRIBUTION SHAPE:
- Skewness: Asymmetry (0 = symmetric)
- Kurtosis: Tail heaviness (3 = normal)
```

For detailed inferential statistics and hypothesis testing, see [Statistical Methods Reference](references/statistical-methods.md).

## Machine Learning

### Algorithm Selection

| Task                         | Algorithms                                                   | When to Use                      |
| ---------------------------- | ------------------------------------------------------------ | -------------------------------- |
| **Classification**           | Logistic Regression, Random Forest, XGBoost, Neural Networks | Categorical outcomes             |
| **Regression**               | Linear Regression, Ridge/Lasso, Random Forest, XGBoost       | Continuous outcomes              |
| **Clustering**               | K-Means, Hierarchical, DBSCAN                                | Group discovery                  |
| **Dimensionality Reduction** | PCA, t-SNE, UMAP                                             | Feature reduction, visualization |
| **Anomaly Detection**        | Isolation Forest, One-Class SVM, Autoencoders                | Outlier detection                |
| **Time Series**              | ARIMA, Prophet, LSTM                                         | Sequential data                  |
| **Recommendation**           | Collaborative Filtering, Content-Based, Matrix Factorization | Personalization                  |
| **NLP**                      | Transformers, BERT, GPT                                      | Text understanding/generation    |

For detailed ML pipelines, feature engineering, and model monitoring, see [ML Pipelines Reference](references/ml-pipelines.md).

## Data Governance

### Data Governance Framework

```
GOVERNANCE PILLARS:

POLICIES:
- Data ownership
- Data classification
- Data retention
- Data access
- Data quality standards

ROLES:
- Data Owner: Accountable for data domain
- Data Steward: Day-to-day quality management
- Data Custodian: Technical implementation
- Data Consumer: End user

PROCESSES:
- Data cataloging
- Metadata management
- Data lineage
- Issue resolution
- Change management

METRICS:
- Data quality scores
- Policy compliance
- Data access requests
- Issue resolution time
```

### Data Quality Dimensions

| Dimension        | Definition                        | Measurement               |
| --------------