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  1. Skills Directory
  2. R for Applied Data Science

R for Applied Data Science

Advanced

The advanced level focuses on building full ML pipelines, training ensemble models, tuning hyperparameters, and interpreting model predictions. Learners handle complex workflows, imbalanced data, and apply techniques for model explainability.

Key Competencies:

  • Machine Learning Pipelines: Develop complete pipelines for preprocessing, modeling, and evaluation.

  • Ensemble Methods: Train models such as Random Forest and Gradient Boosting for improved accuracy.

  • Hyperparameter Tuning: Optimize model performance using techniques like GridSearchCV and RandomizedSearchCV.

  • Model Validation: Implement cross-validation strategies for robust model evaluation.

  • Model Explainability: Use SHAP and LIME to interpret predictions from black-box models.

  • Advanced Feature Engineering: Perform techniques such as Recursive Feature Elimination for feature selection.

  • Imbalanced Data Handling: Address class imbalance using SMOTE and class weighting strategies.