advanced-analytics

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
config	{"atomic":true,"retry_enabled":true,"max_retries":3,"backoff_strategy":"exponential","model_training_timeout":3600}
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	{"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

Error Type	Cause	Recovery
Overfitting	Model too complex	Add regularization, reduce features
Underfitting	Model too simple	Add features, increase complexity
Data leakage	Target info in features	Review feature engineering pipeline
Class imbalance	Skewed target	Use SMOTE, class weights, or resampling
Convergence failure	Poor hyperparameters	Grid search, adjust learning rate

Related Skills

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