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)