Foundations of Probability and Data Analysis in R

Data is only as valuable as your ability to understand and interpret it correctly. To make sound, data-driven decisions, you need a solid grasp of both probability theory and exploratory data analysis. This text-based course guides you through the essential concepts of probability and data analysis using R and RStudio. You will transition from understanding basic statistical terms to writing clean R code that uncovers patterns, tests hypotheses, and visualizes data distributions effectively. What you'll learn: - Understand fundamental probability concepts, including conditional probability and Bayes' rule. - Explore different sampling methods and evaluate how they impact the scope of scientific inference. - Apply modern tidyverse packages in R for efficient data manipulation and exploratory data analysis. - Calculate key numeric summary statistics to describe data distributions and variability. - Create clean, informative data visualizations using modern R plotting libraries. - Practice setting up structured data projects in RStudio for reproducible analysis. You will begin by learning foundational statistical terminology and probability rules before moving into hands-on data exploration. The lessons progress from theoretical concepts to practical, written R code examples that show you how to clean, summarize, and interpret real-world datasets. This course is designed for absolute beginners to statistics and R programming, requiring no prior coding or advanced mathematical background. Start building your data analysis foundation and gain the skills to interpret data with confidence.