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  1. Skills Directory
  2. R for Applied Data Science

R for Applied Data Science

Intermediate

The intermediate level focuses on advanced data operations, classification modeling, and insightful visualizations. Learners group and integrate data, detect anomalies, engineer features, and evaluate model performance using tidymodels.

Key Competencies:

  • Data Grouping: Use aggregation techniques to group and summarize data.

  • Data Integration: Combine multiple datasets using common keys.

  • Anomaly Detection: Identify and address outliers in numerical data.

  • Feature Engineering: Create and transform features to improve model performance.

  • Advanced Visualization: Use tools like heatmaps and box plots to examine variable relationships and distributions.

  • Modeling: Build classification models using tidymodels and other libraries for training, testing, and validation.

  • Model Evaluation: Assess model performance using metrics such as precision, recall, and RMSE.