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name: advanced-analytics description: Advanced analytics including machine learning, predictive modeling, and big data techniques version: "2.0.0" sasmp_version: "2.0.0" bonded_agent: 06-advanced-analytics-specialist bond_type: PRIMARY_BOND

Skill Configuration

config: atomic: true retry_enabled: true max_retries: 3 backoff_strategy: exponential model_training_timeout: 3600

Parameter Validation

parameters: skill_level: type: string required: true enum: [intermediate, advanced, expert] default: intermediate focus_area: type: string required: false enum: [regression, classification, clustering, timeseries, feature_engineering, all] default: all deployment_target: type: string required: false enum: [notebook, api, batch, realtime] default: notebook

Observability

observability: logging_level: info metrics: [model_accuracy, training_time, prediction_latency, feature_importance] model_versioning: true

Advanced Analytics Skill

Overview

Master advanced analytics techniques including machine learning, predictive modeling, and big data processing for sophisticated data analysis.

Core Topics

Machine Learning Fundamentals

• Supervised vs unsupervised learning
• Classification algorithms (logistic regression, decision trees, random forest)
• Regression algorithms (linear, polynomial, ensemble methods)
• Clustering (K-means, hierarchical, DBSCAN)

Predictive Analytics

• Time series forecasting (ARIMA, exponential smoothing)
• Customer segmentation and RFM analysis
• Churn prediction models
• A/B testing and experimentation

Big Data Technologies

• Introduction to Spark and PySpark
• Data lakes and data mesh concepts
• Cloud analytics platforms (AWS, GCP, Azure)
• Real-time analytics with streaming data

Advanced Techniques

• Feature engineering best practices
• Model validation and cross-validation
• Hyperparameter tuning
• Model deployment considerations

Learning Objectives

• Build and validate machine learning models
• Implement predictive analytics solutions
• Work with big data technologies
• Apply advanced statistical techniques

Error Handling

Related Skills

• statistics (for foundational statistical knowledge)
• programming (for ML implementation)
• databases-sql (for big data querying)