Statistical Inference and ML in R

This unit develops core statistical inference skills and introduces practical machine learning workflows using R. Students will study distributions, sampling, hypothesis testing, and implement/visualize models (decision trees, random forests, k-means, neural networks) and build basic Shiny dashboards to communicate results.

Learning Objectives

• Analyze distributions and variability by computing and interpreting standard scores, means, standard deviations, sampling distributions, and applying the Central Limit Theorem to real datasets in R
• Apply hypothesis testing and confidence interval procedures in R to draw valid inferences from sample data and evaluate limits of inference
• Demonstrate implementation and evaluation of supervised and unsupervised learning models (decision trees, random forests, k-means, and basic neural networks) using R packages, and assess model separability and performance
• Create reproducible visualizations and interactive dashboards with ggplot2 and Shiny to communicate analytical findings and model results