Exploratory Data Analysis with R and ggplot2

Data is only as valuable as the insights you can extract from it. Exploratory Data Analysis (EDA) is the essential first step in any data science workflow, allowing you to understand your dataset's structure, detect anomalies, and identify key patterns. This text-based course guides you through the foundational concepts of EDA using the powerful R programming language and the ggplot2 visualization library. You will transition from looking at raw spreadsheets to writing clean, reproducible R code that reveals the stories hidden within your data. What you'll learn: Understand the core principles of Exploratory Data Analysis and data preparation; Import and tidy raw datasets using modern tidyverse tools like dplyr and readr; Create clear, professional visualizations using ggplot2 layers, aesthetics, and geometries; Identify relationships and correlations in data using linear analysis and statistical summaries; Handle missing values, outliers, and anomalous data points systematically; Structure reproducible EDA workflows using modern R scripting best practices. You will start with the basic terminology of data structures and R syntax before moving step-by-step through data manipulation, advanced plotting techniques, and basic statistical modeling. This course is designed for beginners, aspiring data analysts, and researchers with no prior programming experience. Begin your journey into data exploration and start turning raw numbers into meaningful discoveries today.