Modern Statistical Computing and Regression in R

In today's data-driven world, the ability to analyze relationships between variables and build reliable predictive models is a critical skill. R remains the premier language for statistical computing, but modern workflows require more than just writing legacy code. This text-based course guides you from the absolute basics of R programming to building, evaluating, and interpreting sophisticated regression models. You will learn to clean data, visualize relationships, and apply modern statistical techniques to solve real-world analytical problems. What you'll learn: - Understand the foundational principles of statistical computing, data types, and basic R syntax. - Clean and manipulate datasets using modern tidyverse packages for efficient data preparation. - Build and interpret linear and logistic regression models to uncover patterns and make predictions. - Evaluate model fit, diagnose assumptions, and handle common regression pitfalls like multicollinearity. - Apply modern modeling workflows to streamline your statistical analysis and ensure reproducible results. The course begins with core statistical terminology and fundamental R operations before advancing to data visualization, exploratory analysis, and practical regression modeling techniques. This course is designed specifically for beginners with no prior programming or advanced statistical background, making it ideal for aspiring data analysts, researchers, and students. Start reading today to build a strong, practical foundation in modern statistical computing.