diegosouzapw 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)