Foundations of Data Analysis in R

This unit introduces core statistical concepts (central tendency, variation, sampling distributions, hypothesis testing) alongside practical R skills for data import, package management, visualization, and simple model-building. It reinforces exploratory data analysis using base R and shinydashboard, and provides hands-on exposure to decision trees, random forests, and public datasets (e.g., UCI) to prepare learners for deeper predictive modeling and deployment in subsequent units.

Learning Objectives

• Analyze descriptive and inferential statistical measures (means, standard deviation, z-scores, sampling distributions) to summarize datasets and interpret variation, sampling error, and the implications for inference
• Apply R and relevant packages to install libraries, import and inspect datasets, perform basic data cleaning, and produce reproducible base R visualizations
• Demonstrate the construction, interpretation, and basic evaluation of simple predictive models (decision trees and random forests) using representative datasets from repositories such as UCI
• Evaluate and present analytical findings by creating appropriate visualizations and interactive elements (using shinydashboard) to communicate insights to technical and non-technical audiences