Foundations of Statistical Computing

This unit develops foundational statistical thinking and practical R skills needed for data analysis and introductory machine learning. Students learn to compute and visualize descriptive statistics, simulate sampling distributions (including the Central Limit Theorem), perform basic inference, and apply/compare simple machine learning methods and dashboards in R.

Learning Objectives

• Apply R and RStudio tools (packages such as ggplot2, rattle, shiny) to compute descriptive statistics (mean, standard deviation, z-scores) and produce reproducible visualizations and dashboard elements
• Demonstrate understanding of probability and sampling distributions by simulating normal distributions and the Central Limit Theorem in R and constructing confidence intervals
• Analyze results from hypothesis tests and sampling-distribution-based inference in R, including calculating p-values, interpreting test outcomes, and explaining limitations of confidence intervals
• Evaluate and implement basic machine learning techniques in R (decision trees, random forests, k-means clustering, and simple neural networks), compare model separability and performance, and prepare models for integration into Shiny dashboards