Foundations of Statistical Programming in R

This unit establishes core statistical concepts and introduces the R environment, tools, and workflows needed for data analysis. Students learn exploratory data analysis, basic visualization, sampling and hypothesis testing, and introductory supervised learning (decision trees and random forests), preparing them for deeper modeling and application in subsequent units.

Learning Objectives

• Demonstrate foundational statistical concepts by computing and interpreting measures of central tendency, variability, z-scores, and properties of the normal curve using R on real datasets
• Apply R tooling and workflows by installing and managing packages, importing and preparing data (including from the UCI ML Repository), and creating reproducible R scripts and simple Shiny components
• Analyze datasets through exploratory data analysis and base R graphics to identify patterns, counts, and relationships and to produce summary statistics that inform further analysis
• Evaluate basic inferential and introductory machine learning procedures by simulating sampling distributions, performing one- and two-sample hypothesis tests, and building and assessing simple decision tree and random forest models in R