Data Science Foundations with R

Data-driven decision making is one of the most in-demand skills today, yet getting started with programming can feel overwhelming. This course provides a clear, step-by-step introduction to R, the language built specifically for statistical computing and data analysis. You will transform from a complete beginner into a confident analyst capable of working with real-world datasets. By reading through clear explanations and structured code examples, you will learn how to import data, clean messy tables, and generate meaningful insights. You will also discover modern R practices, including the tidyverse ecosystem and clean code formatting, ensuring your workflow aligns with industry standards. What you'll learn: - Understand foundational programming concepts in R, including variables, data types, and vectors - Import and wrangle messy datasets using dplyr and tidyverse tools - Create clear, informative data visualizations to communicate key insights - Clean and format raw data to prepare it for statistical analysis - Write efficient, readable R code using modern styling conventions - Apply basic statistical reasoning to draw conclusions from structured data This course begins with core definitions and essential syntax before guiding you through practical data manipulation and visualization workflows. You will read structured explanations, analyze working code blocks, and complete written exercises to reinforce your understanding. This course is designed entirely for beginners with no prior programming or statistical background. Start your journey into data science today and build a strong foundation in R.