Foundations of Statistics in R

Understanding data requires a solid grasp of statistical reasoning and the computational tools to analyze real-world datasets. This text-based course guides you through foundational statistical principles and shows you how to implement them clearly and effectively using R. You will develop the analytical skills required to summarize metrics, identify patterns, and evaluate distributions with confidence. What you'll learn: Understand core statistical terminology, measurement scales, and variable classifications; Apply fundamental sampling methods including simple random, stratified, and cluster sampling in R; Compute and interpret frequency tables, relative frequencies, and cumulative distributions; Measure central tendency and dispersion using modern R and tidyverse workflows; Practice exploratory data analysis to detect skewness, outliers, and anomalies; Evaluate statistical findings critically to avoid common analytical pitfalls and bias. The curriculum begins with core definitions and theoretical concepts before moving into guided R code examples and written practice exercises. This course is designed for beginners, aspiring data analysts, and researchers with no prior background in statistics or programming. Start reading today to build your practical statistics and R data analysis foundation.