Data Analysis in R: Seattle Housing Case Study

Real-world data is often messy, complex, and full of hidden stories. Understanding how to clean, visualize, and model this data is the core skill of any successful data analyst. In this text-based course, you will work through a comprehensive case study of Seattle housing prices to learn the principles of Exploratory Data Analysis (EDA) and introductory regression modeling using R and the tidyverse. Throughout this course, you will learn to: 1. Understand foundational data science concepts and the tidy data workflow for statistical analysis. 2. Clean and wrangle messy housing datasets using modern tidyverse packages like dplyr and tidyr. 3. Create clear data visualizations with ggplot2 to uncover trends and patterns in housing prices. 4. Apply linear regression models to analyze the relationship between house features and market values. 5. Practice modern R workflows using Quarto for reproducible data reports. 6. Interpret statistical outputs and model coefficients with confidence. We begin with the core terminology of data science, tidy data principles, and R programming basics. From there, you will read through step-by-step code explanations, progressing from initial data exploration to building and evaluating your first predictive models. This course is designed for beginners who want to learn R and data analysis through a practical, hands-on case study, with no prior programming or advanced statistical knowledge required. Start reading today to build your data analysis skills with R.