- 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.
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.
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 |
|---|
| Accuracy | Correct representation of reality | % records matching source |
| Completeness | All required data present | % non-null values |
| Consistency | Same across systems | % matching across sources |
| Timeliness | Available when needed | Latency, freshness |
| Validity | Conforms to format/rules | % passing validation |
| Uniqueness | No unwanted duplicates | Duplicate rate |
Business Intelligence
BI Architecture
ARCHITECTURE LAYERS:
DATA SOURCES:
- Operational systems
- External data
- IoT/streaming
DATA INTEGRATION:
- ETL/ELT pipelines
- Data lakes
- Data warehouses
SEMANTIC LAYER:
- Business definitions
- Calculated metrics
- Hierarchies
- Relationships
PRESENTATION:
- Dashboards
- Reports
- Ad-hoc analysis
- Embedded analytics
Dashboard Design Principles
DESIGN PRINCIPLES:
PURPOSE:
- One clear objective per dashboard
- Know your audience
- Enable decisions
LAYOUT:
- Most important top-left
- Related items grouped
- Progressive disclosure
- Whitespace for clarity
VISUALS:
- Right chart for data type
- Consistent formatting
- Minimal decoration
- Color with purpose
INTERACTIVITY:
- Filters for exploration
- Drill-down capability
- Cross-filtering
- Tooltip details
Metric Design
METRIC DEFINITION TEMPLATE:
NAME: [Metric name]
DEFINITION: [Clear business definition]
FORMULA: [Precise calculation]
OWNER: [Responsible person]
DATA SOURCE: [Where it comes from]
GRAIN: [Level of detail]
FREQUENCY: [Update cadence]
DIMENSIONS: [Slicing attributes]
TARGETS: [Goals/benchmarks]
RELATED: [Related metrics]
Predictive Modeling
Use Case Framework
| Use Case | Business Application | Approach |
|---|
| Churn Prediction | Retention programs | Classification |
| Demand Forecasting | Inventory planning | Time series |
| Lead Scoring | Sales prioritization | Classification |
| Price Optimization | Revenue management | Regression/RL |
| Fraud Detection | Risk mitigation | Anomaly detection |
| Recommendation | Personalization | Collaborative filtering |
| Customer Segmentation | Marketing targeting | Clustering |
| Lifetime Value | Customer investment | Regression |
Data Ethics & Privacy
Ethical AI Framework
PRINCIPLES:
FAIRNESS:
- No discriminatory outcomes
- Bias testing across groups
- Regular auditing
ACCOUNTABILITY:
- Clear ownership
- Decision audit trails
- Escalation process
TRANSPARENCY:
- Explainable decisions
- Clear documentation
- User communication
PRIVACY:
- Data minimization
- Consent management
- Security controls
Bias Detection
BIAS TYPES:
HISTORICAL: Reflects past discrimination
REPRESENTATION: Training data not representative
MEASUREMENT: Proxy variables correlate with protected attributes
AGGREGATION: Single model for diverse populations
EVALUATION: Inappropriate benchmarks
FAIRNESS METRICS:
- Demographic Parity: Equal positive rates
- Equalized Odds: Equal TPR and FPR
- Individual Fairness: Similar inputs, similar outputs
- Calibration: Equal accuracy across groups
Analytics Team Structure
Team Roles
| Role | Focus | Skills |
|---|
| Data Engineer | Pipelines, infrastructure | SQL, Python, Spark, Cloud |
| Data Analyst | Reporting, ad-hoc analysis | SQL, BI tools, Statistics |
| Data Scientist | Modeling, ML | Python/R, ML, Statistics |
| ML Engineer | Model deployment | MLOps, Software Engineering |
| Analytics Engineer | Data modeling | dbt, SQL, Data Modeling |
Operating Models
| Model | Description | Best For |
|---|
| Centralized | Single analytics team | Consistency, efficiency |
| Decentralized | Embedded in business units | Business alignment |
| Hub & Spoke | Central CoE + embedded | Balance of both |
| Federated | Shared platform, domain teams | Scale with autonomy |
References
See Also
